Institutionen för medicin och hälsa

Institutionen för medicin och hälsa Department of Medical and Health Sciences Master Thesis Synthetic MRI for visualization of quantitative MRI Examensarbete utfört i medicinsk teknik vid Tekniska högskolan i Linköping av Erika Peterson LITH-IMH/RV-A--10/001--SE Linköping 2008 Department of Medical and Health Sciences Linköpings universitet SE-581 83 Linköping, Sweden Linköpings tekniska högskola Linköpings universitet 581 83 Linköping

Synthetic MRI for visualization of quantitative MRI Examensarbete utfört i medicinsk teknik vid Tekniska högskolan i Linköping av Erika Peterson LITH-IMH/RV-A--10/001--SE Supervisor: Examiner: Marcel Warntjes CMIV, Linköpings universitet Peter Lundberg IMH, Linköpings universitet Linköping, 4 September, 2008

Avdelning, Institution Division, Department Division of Medicine and Health Department of Medical and Health Sciences Linköpings universitet SE-581 83 Linköping, Sweden Datum Date 2008-09-04 Språk Language Svenska/Swedish Engelska/English Rapporttyp Report category Licentiatavhandling Examensarbete C-uppsats D-uppsats Övrig rapport ISBN ISRN LITH-IMH/RV-A--10/001--SE Serietitel och serienummer Title of series, numbering ISSN URL för elektronisk version http://www.imh.liu.se http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-zzzz Titel Title Syntetisk MRT som visualisering av kvantitativ MRT Synthetic MRI for visualization of quantitative MRI Författare Author Erika Peterson Sammanfattning Abstract Magnetic resonance imaging (MRI) is an imaging technique that is used in hospitals worldwide. The images are acquired through the use of an MRI scanner and the clinical information is provided through the image contrast, which is based on the magnetic properties in biological tissue. By altering the scanner settings, images with different contrast properties can be obtained. Conventional MRI is a qualitative imaging technique and no absolute measurements are performed. At Center for Medical Imaging and Visualization (CMIV) researchers are developing a new MRI technique named synthetic MRI (SyMRI). SyMRI is based on quantitative measurements of data and absolute values of the magnetic properties of the biological tissue can be obtained. The purpose of this master thesis has been to take the development of SyMRI a step further by developing and implementing a visualization studio for SyMRI imaging of the human brain. The software, SyMRI Brain Studio, is intended to be used in clinical routine. Input from radiologists was used to evaluate the imaging technique and the software. Additionally, the requirements of the radiologists were converted into technical specifications for the imaging technique and SyMRI Brain Studio. Additionally, validation of the potential in terms of replacing conventional MRI with SyMRI Brain Studio was performed. The work resulted in visualization software that provides a solid formation for the future development of SyMRI Brain Studio into a clinical tool that can be used for validation and research purposes. A list of suggestions for the future developments is also presented. Future clinical evaluation, technical improvements and research are required in order to estimate the potential of SyMRI and to introduce the technique as a generally used clinical tool. Nyckelord Keywords magnetic resonance imaging, absolute quantification, synthetic magnetic resonance imaging, visualization

Abstract Magnetic resonance imaging (MRI) is an imaging technique that is used in hospitals worldwide. The images are acquired through the use of an MRI scanner and the clinical information is provided through the image contrast, which is based on the magnetic properties in biological tissue. By altering the scanner settings, images with different contrast properties can be obtained. Conventional MRI is a qualitative imaging technique and no absolute measurements are performed. At Center for Medical Imaging and Visualization (CMIV) researchers are developing a new MRI technique named synthetic MRI (SyMRI). SyMRI is based on quantitative measurements of data and absolute values of the magnetic properties of the biological tissue can be obtained. The purpose of this master thesis has been to take the development of SyMRI a step further by developing and implementing a visualization studio for SyMRI imaging of the human brain. The software, SyMRI Brain Studio, is intended to be used in clinical routine. Input from radiologists was used to evaluate the imaging technique and the software. Additionally, the requirements of the radiologists were converted into technical specifications for the imaging technique and SyMRI Brain Studio. Additionally, validation of the potential in terms of replacing conventional MRI with SyMRI Brain Studio was performed. The work resulted in visualization software that provides a solid formation for the future development of SyMRI Brain Studio into a clinical tool that can be used for validation and research purposes. A list of suggestions for the future developments is also presented. Future clinical evaluation, technical improvements and research are required in order to estimate the potential of SyMRI and to introduce the technique as a generally used clinical tool. v

Acknowledgments I wish to acknowledge and thank all those people who helped and inspired me throughout the work! A special thanks to: Marcel Warntjes, my supervisor, for his never-ending optimism, knowledge, teaching and support. Janne West, for being an invaluable source of information when it comes to just about anything concerning computers. Peter Lundberg, for serving as my examiner and giving me useful tips concerning the written report. All the people at CMIV, for an encouraging and inspiring research environment with heaps of passion and nice coffee breaks. The Radiologists who devoted some of their time on giving valuable input to this master thesis. Mum, Dad and Simon, for support and encouraging discussions about what to do with my life. And thanks to you, Mum, for wise suggestions regarding the scientific work. Marcus, for not caring too much about what I do, just liking me as I am. vii

Contents 1 Introduction 3 1.1 Problem Description.......................... 3 1.1.1 Previous Research....................... 4 1.2 Thesis Objectives............................ 4 1.2.1 SyMRI Brain Studio...................... 4 1.2.2 Radiologist Interaction..................... 5 1.2.3 Validation of the Technique.................. 5 1.2.4 Research Questions....................... 5 1.2.5 Scope.............................. 5 1.3 Previous Knowledge.......................... 6 1.4 Method................................. 6 1.5 Target Audience............................ 6 1.6 Outline of the Report......................... 6 2 The MRI Scanner 9 2.1 Magnetic Resonance Imaging..................... 9 2.1.1 The net magnetisation, ˆM0.................. 10 2.1.2 The RF-pulse & its B 1 -field.................. 11 2.1.3 Spatial dependence - The Gradient Coils........... 12 2.1.4 Spin Relaxation......................... 13 2.1.5 From Signal Detection to Image Processing......... 16 3 Conventional Contrast-Weighted Imaging 19 3.1 Contrast Weighted Imaging...................... 19 3.1.1 PD-weight............................ 20 3.1.2 T 1 -weight............................ 20 3.1.3 T 2 -weight............................ 21 3.1.4 FLAIR............................. 21 4 Quantitative MRI 23 4.1 The Concept of qmri......................... 23 4.2 MR Parameters to Quantify...................... 23 4.2.1 Tissue Characterisation.................... 24 4.3 Data Acquisition............................ 25 4.3.1 QRAPMASTER........................ 25 ix

x Contents 4.4 Fitting the Data to the Mathematical Models............ 25 4.4.1 Quantification Maps...................... 26 4.4.2 SyMRI BrainStudio Fitting Tool............... 27 4.5 Synthetic MRI............................. 27 4.5.1 Synthetic Contrast Weighted MRI.............. 27 4.5.2 Tissue Segmentation...................... 28 4.5.3 Normalization.......................... 28 5 Methods 29 5.1 SyMRI Brain Studio.......................... 29 5.1.1 System Environment...................... 29 5.2 Radiologist Interaction......................... 29 5.2.1 Radiologist Interaction I.................... 30 5.2.2 Radiologist Interaction II................... 30 5.2.3 Radiologist Interaction III................... 31 5.2.4 Radiologist Interaction IV................... 31 5.2.5 Radiologist Interaction V................... 31 5.3 Validation of the Technique...................... 31 6 SyMRI Brain Studio 33 6.1 Architecture............................... 33 6.2 Main Features.............................. 34 6.2.1 Quantification Maps...................... 34 6.2.2 Display of SyMRI Contrast-Weighted Images........ 36 6.2.3 Graphical User Interface.................... 39 6.2.4 Autoscale............................ 40 6.2.5 Colormap............................ 40 6.2.6 Colorbar............................. 40 6.2.7 The Font............................ 41 6.3 SyMRI Brain Studio Releases..................... 41 6.3.1 v.1.0.0.............................. 41 6.3.2 v.3.1.0.............................. 41 6.3.3 v.3.2.0.............................. 42 6.3.4 v.4.1.0.............................. 42 7 Radiologist 43 7.1 Radiologist Interaction I........................ 43 7.1.1 Drawbacks........................... 43 7.1.2 Advantages SyMRI....................... 46 7.1.3 Future Potential of SyMRI.................. 47 7.2 Radiologist Interaction II....................... 48 7.2.1 The Clinical Routine - an MRI Examination of the Brain.. 49 7.2.2 Interaction with SECTRA IDS 5............... 49 7.2.3 Autoscale............................ 49 7.2.4 The SyMRI Software...................... 50 7.3 Radiologist Interaction III....................... 50

Contents xi 7.4 Radiologist Interaction IV....................... 50 7.4.1 Follow up - Drawbacks..................... 50 7.5 Radiologist Interaction V....................... 51 8 Validation of the Technique 53 8.1 Required Characteristics........................ 53 8.2 Potential of SyMRI Brain Studio................... 54 8.2.1 Scan Time........................... 54 8.2.2 The All-in-One Approach................... 54 8.2.3 Quantitative Measurements.................. 54 8.3 SyMRI Today.............................. 55 9 Discussion 57 9.1 SyMRI Brain Studio.......................... 57 9.1.1 Design Decisions........................ 58 9.2 Radiologist Interaction......................... 61 9.2.1 Selection of Radiologists.................... 61 9.2.2 Data Sets............................ 61 9.2.3 Lack of Anatomic Detail in SyMRI images.......... 62 9.2.4 MRI Knowledge - MRI Experience.............. 62 9.3 Validation................................ 62 10 Conclusions and Future Research 65 10.1 Research Questions........................... 65 10.2 Future Research............................ 66 10.3 Development Suggestions....................... 67 10.3.1 SyMRI SECTRA PACS IDS5 Plug in & SyMRI Brain Studio 67 Bibliography 69 A Magnetic Resonance 71 A.0.2 Nuclear Spin.......................... 71 A.0.3 Magnetic Properties & Energy Levels of Nuclear Spin... 72 A.0.4 A Macroscopic Net Magnetization.............. 74 B Abbreviations 75

List of Figures 2.1 MRI Scanner.............................. 9 2.2 Spin-Echo Pulse Sequence....................... 10 2.3 The Net Magnetization, ˆM0...................... 11 2.4 90 pulse................................ 11 2.5 Slice Selecting Gradient........................ 12 2.6 Phase Encoding Gradient....................... 12 2.7 Frequency Encoding Gradient..................... 13 2.8 Spin Relaxation............................. 13 2.9 FID................................... 14 2.10 T 1 -relaxation curves.......................... 15 2.11 T 2 -relaxation curves.......................... 15 2.12 K-space................................. 17 3.1 PDw image............................... 20 3.2 T 1 w image................................ 20 3.3 T 2 w image................................ 21 3.4 FLAIR image.............................. 22 4.1 qmri vs MRI.............................. 24 4.2 T 1, T 2 and PD............................. 24 4.3 QRAPMASTER Scanner Sequence.................. 26 4.4 Fit of Data............................... 26 4.5 T1-map................................. 27 6.1 The Visualization Pipeline....................... 34 6.2 Quantification Maps: T 1, T 2, PD................... 34 6.3 Quantification Maps: B 1, Mean Erros................ 35 6.4 Region of Interest............................ 35 6.5 R 1 R 2 -plot................................ 36 6.6 SyMRI Brain Studio: Default Four Viewport Display........ 37 6.7 Navigation Window.......................... 37 6.8 Fat Suppression............................. 38 6.9 T 1 Enhanced Image........................... 38 6.10 Popup Menu.............................. 39 6.11 Colorbar................................. 41 7.1 Pixel Resolution............................ 46 7.2 Artifacts SyMRI............................ 47 A.1 Nuclear Spin.............................. 72 A.2 Nuclear Spin Orientations for H +................... 72 A.3 Hydrogen Nuclei in a Magnetic Field................. 73 A.4 The Net Magnetization, ˆM0...................... 74

2 Contents List of Tables 6.1 MR parameter values: Brain Tissue................. 36 6.2 Default Scanner Parameters...................... 37

Chapter 1 Introduction Magnetic Resonance Imaging (MRI) is an important and widely used medical imaging technique used in the routine clinical workflow worldwide, particularly for soft tissue visualizations in neurological applications. The technique was first put into clinical use in the 1980s [1] and it has developed rapidly ever since. In 2007, approximately 40 million MRI examinations were performed [2]. An advantage of MRI compared to imaging modalities such as x-ray and computed tomography is the absence of ionizing radiation. Instead, MRI uses magnetic fields and radio frequency pulses to yield diagnostic images. At Center for Medical Imaging Science and Visualization (CMIV) researchers are developing a new MRI technique named synthetic MRI (SyMRI). Based on the quantification of four magnetic resonance (MR) parameters SyMRI provides a new approach to MRI, where quantitative rather than qualitative data is measured. The technique is believed to have a promising future and the long-term goal is to develop and establish SyMRI as a new MRI technique providing faster examinations with improved clinical information [3]. 1.1 Problem Description SyMRI is based on quantitative measurements of data opposed to conventional contrast-weighted MRI where no absolute values are measured. The quantitative approach provides additional information through the absolute values given. Moreover, in theory a single quantitative measurement makes it possible to postsynthesize an infinite number of contrast-weighted images that with the conventional technique each need a separate scan to be acquired. The approach of quantitative MRI (qmri) has been discussed since the end of the 1990s [4], but has until now been constrained from being generally used in clinical applications. The main reasons for this have been the absence of measurement methods and data processing techniques that provide adequate information within a clinical acceptable time frame [3]. 3

4 Introduction 1.1.1 Previous Research At CMIV, a scanning technique allowing fast quantification of the four MR parameters longitudinal relaxation (T 1 ), transverse relaxation (T 2 ), proton density (PD) and the amplitude of the local B 1 -field has been developed. The scanner sequence, Quantification of Relaxation times And Proton density by Multi-echo Acquisition of a Saturation recovery using TSE Read-out (QRAPMASTER), allows the volume of a head to be examined in about five minutes [3], which is a clinically acceptable time. Hence, QRAPMASTER provides the raw data needed for SyMRI. An additional development in the direction to integrate SyMRI into the clinical workflow is the development of the SyMRI SECTRA IDS 5 Plugin. The plug-in provides a framework for the development of fitting tools and visualization studios for SyMRI as a plug-in to the SECTRA Picture Archiving and Communication System (PACS) workstation IDS 5, used by hospitals worldwide. The plug-in has an implemented cardiac visualization studio, Cardiac Studio, which is under clinical evaluation [5]. 1.2 Thesis Objectives In spite of the promising theories there are persisting challenges that need to be overcome in order to introduce SyMRI as a widespread clinical tool. First of all radiologists have no experience in interpretation based on quantified data and they are familiar with and completely rely on the conventional set of contrast-weighted images. This master thesis aims to take the development of SyMRI further by implementing a visualization studio for SyMRI of the brain and work on the interaction between the imaging technique and the radiologists. The project is divided into three somewhat integrated parts, the development and implementation of a visualization studio for SyMRI of the brain (SyMRI Brain Studio), the analysis and optimization of the interaction between the radiologists and the technique (Radiologist Interaction), and a validation on how many of today s conventional MRI examinations that can be replaced with the new technique in the future (Validation of the Technique). 1.2.1 SyMRI Brain Studio The work with SyMRI Brain Studio includes the continuous development and implementation of the visualization tool SyMRI Brain Studio in SyMRI SEC- TRA IDS 5 Plugin (Visual C++ 6, Microsoft 1995). SyMRI Brain Studio should allow radiologists to compare SyMRI with conventional contrast-weighted MRI but also have features taking advantage of the extended potential of SyMRI. The work and the code should be implemented and documented in a way that enables smooth continuous development of SyMRI Brain Studio into software for clinical use. Brain Studio should include: Post-exam synthesis of conventional contrast-weighted images (T 1 w, T 2 w, P Dw and Fluid-Attenuated Inversion Recovery (FLAIR)).

1.2 Thesis Objectives 5 Display of quantification maps. A Graphical User Interface (GUI) allowing the user to experience the enhanced features and possibilities of qmri. Aim to become a visualization studio for routine clinical examinations using SyMRI. 1.2.2 Radiologist Interaction By working on the interaction between the radiologist and SyMRI, the aim is to increase the understanding on how SyMRI visualizations can be developed in order to optimize their clinical use. This part of the work should: Serve as a background and source of information for the continuous development of SyMRI Brain Studio during the time frame of this master thesis. Convert demands and requirements from radiologists into technical requirements on SyMRI Brain Studio. Present suggestions on further developments of SyMRI SECTRA IDS 5 Plugin and SyMRI Brain Studio. 1.2.3 Validation of the Technique The validation phase should result in an estimate on how many of today s conventional MRI examinations that can be replaced by the new technique in the future. 1.2.4 Research Questions How can the quantitative data set be introduced and presented in a visualization tool in an efficient way? What technical improvements are required to satisfy the demands of the radiologist? How should the GUI of SyMRI Brain Studio be developed in order to optimize the interaction between SyMRI and the radiologist? How many of today s conventional MRI examinations can be replaced with the new technique in the future? 1.2.5 Scope Factors such as the time frame (30 ECTS credits, 20 full-time weeks) and previous knowledge within the area of research set the limitations of this master thesis. The project does not aim to present a fully developed clinical visualization tool for SyMRI of the brain, but to provide a basis for such a future development.

6 Introduction 1.3 Previous Knowledge The author possesses four and a half year of undergraduate studies within the area of engineering biology, including basic knowledge in image processing, computer programming, scientific visualizations and medical imaging. The author has no previous knowledge in the clinical use of MRI, the quantification of MR data or GUI development. 1.4 Method The thesis work has been performed in an iterative manner by combining literature studies, software development and input from radiologists and researchers. A weekly MRI course as well as a practical introduction to the MRI scanner was followed in order to get an enhanced understanding within the area of MRI. A more detailed method specification can be found in chapter 5. 1.5 Target Audience The report is written in an attempt to reach an as wide audience as possible although the focus is individuals with technical background and an interest in MRI. No previous knowledge in MRI is required but basic knowledge in imaging, anatomy and physics will help the understanding. The abbreviation list in Appendix B of the report is present in order to straighten out possible question marks. 1.6 Outline of the Report Introduction Chapter 1 provides the reader with an introduction to this master thesis. The chapter is divided into a number of sections including problem description, thesis objectives, previous knowledge of the author, method used, target audience and outline of the report. Background Chapters 2, 3 and 4 will together accommodate the reader with the technical background needed to understand the work of this master thesis. Chapter 2 describes how the properties of the phenomenon forming the basis of MRI, the magnetic resonance, is used to create medical images using an MRI scanner. Chapter 3 explains how MRI is used to yield conventional contrastweighted MRI images with today s conventional MRI techniques. Chapter 4 explains the concept of qmri, which forms the basis of SyMRI through the quantitative data acquisition and the fitting of the data to mathematical models. Furthermore, the chapter describes the approach of SyMRI visualizations.

1.6 Outline of the Report 7 Methods Chapter 1 gives a short overview of the general method used, while chapter 4 provides a more detailed description of the methods used in the different parts of the work. Result Chapter 6, 7 and 8 explain the different steps and outcomes of the work. Chapter 6 describes the features and development of SyMRI Brain Studio. Chapter 7 give details about the radiologist interactions and their outcome, while chapter 8 contains the validation of the technique and its potential to replace conventional contrast-weighted MRI in the clinical routine. Discussion Chapter 9 contains a discussion regarding the work and outcome of the different parts of this master thesis. Conclusions and Future Development In chapter 10 the research questions are answered and future work within the area is proposed together with future development suggestions for the SyMRI Sectra IDS 5 plugin and SyMRI Brain Studio.

Chapter 2 The MRI Scanner The chapter introduces magnetic resonance imaging (MRI). In the end of the chapter, the reader should have a basic understanding on how MR enables the generation of medical contrast weighted images through the application of magnetic fields and radio frequency pulses (RF-pulses) to biological tissue. 2.1 Magnetic Resonance Imaging Figure 2.1. MRI scanner. A schematic picture of a MRI scanner is shown in Fig 2.1. During the clinical examination, the patient is lying on the patient table inside the bore of the magnet. The magnet supplies a large homogeneous and static magnetic B 0 -field, which causes a magnetization ˆM 0 of the patient volume. Through a scanner pulse sequence MRI images are given. The pulse sequence contains hardware instructions causing the components of the MRI scanner to stimulate magnetic interaction in a pre-defined manner in the patient volume (Fig 2.2). The first line in the pulse sequence displayed in Fig 2.2 describes the RF-pulse (Section 2.1.2). The RFpulse tips ˆM0 into a plane where it can be processed and measured. The three following rows describe the behaviour of the three magnetic field gradients used to 9

10 The MRI Scanner distinguish different spatial locations within the patient volume (Section 2.1.3). The last section of the pulse sequence contains the data acquisition, where the analogue digital converter (ADC) converts the continuous analogue signal to digital sample points. The pulse sequence is then repeated for each required data line by altering the phase encoding gradient at each repetition. The period between two repetitions is called repetition time (TR). The design of pulse sequences is an entire research field alone aiming to get the optimal image coverage in shortest time possible. Figure 2.2. Spin-echo pulse sequence illustrating the hardware instructions of the MRI scanner. 2.1.1 The net magnetisation, ˆM0 The net magnetization of the patient volume, ˆM0 (Fig 2.3), is predominantly formed through the magnetic properties of hydrogen nuclei in biological tissue. In presence of the external magnetic field ˆB 0, the hydrogen nuclei, which consist of a single proton, will precess around ˆB 0 with a certain precessional frequency, the Larmor frequency (f L ). Nuclei precessing with the same f L are said to be in magnetic resonance and are referred to as an isochromat. Inside ˆB 0, a thermal equilibrium of the isochromat is reached, giving an excess number of nuclei that precess in the positive direction of ˆB0. The excess of spins cause the net magnetisation ˆM 0 to appear. ˆM0 precesses around ˆB 0, but is often illustrated as a net vector in a rotating frame (Fig 2.3). A deeper introduction to the quantum physics underlying MRI is available in Appendix A.

2.1 Magnetic Resonance Imaging 11 Figure 2.3. In presence of a magnetic field B 0 a resulting net magnetization will appear due to an excess number of spins in the direction of ˆB0. 2.1.2 The RF-pulse & its B 1 -field The RF-pulse induced by the RF-coil will apply an oscillating magnetic field, ˆB1, in the xy-plane orthogonal to ˆB 0. Net magnetization caused by isochromats in resonance with the rotational frequency of ˆB1 will then experience a static magnetic field causing the magnetization to additionally precess around ˆB 1. In the rotating frame a tipping of ˆM0 can be seen proceeding between two states, ˆM0 and ˆM 0. By choosing the bandwidth of the RF-pulse, it can be determined which isochromats in an inhomogeneous magnetic field that will experience a static B 1 -field. Figure 2.4. 90 pulse in the rotating frame of reference. The duration of the RF-pulse determines the tip-angle α according to α = γb1t 2π. An RF-pulse that will tip the net magnetization into the xy-plane is referred to as a 90 pulse (Fig 2.4). The RF-pulse will cause the spins to be in phase coherence pointing in the same direction in the xy-plane [1]. However, immediately after the RF-pulse different relaxation mechanisms will cause the magnetization to relax back to its thermal equilibrium M 0. The relaxation mechanisms are crucial for the image contrast achieved in MRI and are explained in section 2.1.4.

12 The MRI Scanner 2.1.3 Spatial dependence - The Gradient Coils The three orthogonal gradient coils inside the MRI scanner are applying magnetic field gradients across the patient volume in order to make the magnetization spatial dependent according to: ˆB i = ˆB 0 + G ˆ T ˆr i (2.1) Gˆ T is the summation of gradients at a location ˆr i [6]. The application of gradients will create isochromats throughout the patient volume with distinguished resonance frequencies: Slice Selecting Gradient f i = γ(b 0 + ˆ G T ˆr i ) (2.2) Figure 2.5. Slice Selecting Gradient. The slice selecting gradient (G ss ) is introduced at the time of the RF-pulse in order to make the RF-pulse slice selective (Fig 2.5). The thickness of the slice is proportional to the RF-pulse bandwidth and inversely related G ss, SLICE width = RF bandwidth γg ss [1]. The direction of the slice is determined by the direction of the gradient. After G ss a rephasing gradient is needed in order to realign the spins due to the dephasing caused by the excitation. In multi-slice imaging where a number of slices are used to visualize the patient volume G ss is normally kept constant while alternating the frequency of the RF-pulse. Phase Encoding Gradient Figure 2.6. Phase Encoding Gradient. The phase encoding gradient (G pe ) gives spatial phase variation and is applied after the RF-pulse but before the data acquisition (Fig 2.6)[7]. The spins in the

2.1 Magnetic Resonance Imaging 13 sample dephase until G pe is turned off after which they return to their original frequency keeping their phase angle. The phase differences cause phase encoding in one of the in-plane directions of the slice. The phase differences remain until another gradient is applied or the MR-signal decays due to T 2 -relaxation (Section 2.1.4)[1]. Frequency Encoding Gradient Figure 2.7. Frequency Encoding Gradient. The frequency encoding gradient or read out gradient, G ro, will add or subtract from the magnetization along the second dimension within the image slice (Fig 2.7). The gradient is applied during the data acquisition making the signal consisting of a number of different frequencies corresponding to different locations within the second in-plane axis of the slice. 2.1.4 Spin Relaxation Figure 2.8. Spin relaxation back to thermal equilibrium. Spin relaxation is the process following an RF-pulse when the isochromats release and redistribute absorbed energy as they go back to their thermal equilibrium state ˆM 0 (Fig. 2.8). Two different relaxations can be measured, the longitudinal relaxation (T 1 -relaxation) and the transverse relaxation (T 2 -relaxation). The relaxations are dependent on two distinguished relaxation mechanisms, those who transfer energy away from the spins to the lattice and those who redistribute energy within the spin system itself [4]. The relaxation is dependent on a number of

14 The MRI Scanner tissue specific properties such as intra- and intermolecular interactions. Together with the proton density, the spin relaxation will form the basis of image contrast in MRI. Bloch Equations The behaviour of the magnetization during the excitation through RF-pulses and the following relaxation has been modelled in a set of differential equations by Bloch [6]. Bloch s equations are solely based on classical mechanics and are considering the net magnetization. The Bloch equations and their solutions are shown in the equations below. d M dt = γ M B = γ (M y B z M z B y )i (M z B x M x B z )j (M x B y M y B x )k (2.3) M z (t) = [M z (o) M o ]e t/t1 + M 0 M x (t) = [M x (0)cos(ω o t) + M y (0)sin(ω o t)]e t/t2 M y (t) = [M x (0)sin(ω o t) M y (0)cos(ω o t)]e t/t2 M xy = M 2 x + M 2 y After a 90 RF-pulse, the solutions to the Bloch equations models M xy as a decaying signal oscillating with the Larmor frequency, while M z exponentially grows back to M 0. The precessing magnetic field M xy will induce a voltage in the RF-coil and the signal, a free induction decay (FID) can be modelled according to S(t) = S 0 e t/t2 e iωlt (Fig. 2.9). In MRI, either the FID or one or several echoes created from the FID are measured. Figure 2.9. Free induction decay.

2.1 Magnetic Resonance Imaging 15 Figure 2.10. The T 1-relaxation curve of two tissues with different T 1. T 1 -relaxation The exponential recovery of M z back to its thermal equilibrium state M 0 after a RF-pulse is called longitudinal relaxation, spin-lattice relaxation or T 1 -relaxation. The T 1 -value (T 1 ) serves as an absolute measurement of the T 1 -relaxation and is the time when M z has recovered to 63% of M 0 M z0 (Fig 2.10). Apart from molecular dependencies, T 1 is dependent on the scanner field strength and temperature [1]. The inverted T 1 is often referred to as T 1 -relaxation rate (R 1 ). T 1 -relaxation is induced as the isochromats release their energy to the surrounding lattice and is strongly correlated with molecular motion. Molecular motion in surrounding molecules will cause local magnetic fields tumbling with different frequencies. Tumbling with f L perpendicular to B 0 will due to the resonance condition induce energy transfer from the spin system to its surroundings. T 2 -relaxation Figure 2.11. The T 2-relaxation curve of two tissues with different T 2. The transverse relaxation, spin-spin relaxation or T 2 -relaxation is present in the xy-plane after the RF-pulse. The T 2 -value (T 2 ) serves as an absolute measurement of the T 2 -relaxation and is the time when M xy has decreased to 37% of the value immediately after a RF-pulse (Fig 2.11). T 2 is dependent on the scanner field strength as well as temperature. The inverted T 2 is referred to as the T 2 -relaxation rate (R 2 ) [1]. T 2 -relaxation is affected by relaxation mechanisms redistributing energy within the spin system as well as the energy transfer from the spins to the lattice. Hence, T 2 is always shorter than or equal to T 1. The T 2 -relaxation caused by redistribution of energy within the spin system is caused when the isochromats

16 The MRI Scanner lose the phase coherence gained through the RF-pulse. The relaxation is induced by fluctations in the magnetic field experienced by the isochromats, making the precess with slightly different frequencies. 2.1.5 From Signal Detection to Image Processing The pulse sequence makes it possible to distinguish the signal from different spatial locations within the patient volume. The signal measured and sampled are often one or several echoes from the appearing FID. The time between the exciting RF-pulse and the created echo are called echo time (TE). Echoes are created in two major ways using spin echo (SE) or gradient echo (GE) techniques. Both techniques create an echo of the signal in the transverse xy-plane that the receiving coil detects by measuring the induced voltage (Eq. 2.4). emf = d dt [ M tot B rec ] (2.4) Sample emf = electro motive force, B rec = receiving coil sensitivity A schematic image of a SE sequence is shown in Fig. 2.2. The SE is formed using a refocusing 180 pulse that flips the magnetization around the y-axis at time T E 2 after the initial RF-pulse. The pulse will refocus the transverse spins that have dephased due to T 2 -relaxation. The spins will now come back into phase coherence creating an echo at time T E according to S SE = S 0 e T E/T2. In GE sequences, a negative gradient lobe is used to form the echo. Turbo spin echo (TSE) is an approach where several refocusing pulses are applied during each repetition in order to form several echoes from a single excitation pulse. Analog to Digital Converter The analogue-to-digital converter (ADC) digitalizes the emf and stores it in numeric form in a computer. The raw data space where the data storage is done is called k-space. K-space K-space is a two or three dimensional data space used in MRI, with one frequency encoding and one phase encoding direction. K-space is often looked upon as a trajectory path for the phase encoding and frequency encoding gradients. The rows in k-space are collected throughout the repetitions of the pulse sequence as the gradients are changed. The middle of k-space collects low frequencies containing SNR and contrast and the outer rows will collect high frequencies containing edges, boundaries and image resolution [1]. The number of sample points in the frequency encoding direction of k-space will determine the number of sample points collected during each echo. The number of sample points in the phase encoding direction will determine how many times the sequence has to be repeated to fill all the lines in k-space. The number of data points sampled and the image resolution

2.1 Magnetic Resonance Imaging 17 determines the field of view (FoV). The reconstruction from k-space to a spatial image is made through a 2D Fourier transform (Fig 2.12). Figure 2.12. An MRI image and corresponding k-space. Sensitivity encoding (SENSE) is a technique giving shorter acquisition times by not acquiring data to all lines in k-space, instead several receiving coils are used. This will cause aliasing that appears when signals are superimposed on each other due to a too large FoV. Through knowledge of the sensitivity of the receiving coil elements and a coil sensitivity that varies in space, a defined equation system can be generated (Eq. 2.5). The system can be used to unwrap the pixels and a complete image can be restored. m i = s 1i p1 + s 2i p2 (2.5) m i is the measured signal of a coil, s is the sensitivity of the receiving coil and p is the signal at each pixel.another technique to achieve faster image acquisition is echo planar imaging (EPI). With EPI a number of lines are collected in k-space during each repetition using gradients.

Chapter 3 Conventional Contrast-Weighted Imaging The chapter will give the reader an insight on how the conventional set of contrastweighted MRI images is achieved and used is in today s clinic. 3.1 Contrast Weighted Imaging In conventional contrast-weighted MRI, tissue can be distinguished through differences in image pixel intensity. The image contrast is caused by differences in signal amplitude at different locations of the patient volume. The signal amplitude is dependent on proton density (PD), T 1 -relaxation and T 2 -relaxation and a number of scanner parameters. The scanner parameters TE and TR are used to create images with fixed contrast behaviour. TE and TR can be varied to produce images whose contrast is mainly dependent on T 1 -relaxation, T 2 -relaxation, PD or a combination of these. The images are said to have a certain T 1 -weight (T 1 w), T 2 -weight (T 2 w) and PD-weight (PDw). TE determines the amount of T 2 w. With a relatively long TE the signal amplitude will be dependent on the T 2 -relaxation. Tissue compartments with short T 2 will dephase faster than tissue compartments with long T 2 and hence have a more attenuated signal at the formed echo. With short TE, very limited T 2 w is present. TR is the time between the applications of the excitation RF-pulses and determines the amount of T 1 w in the image. With a long TR, all tissue compartments will have time to relax back to their thermal equilibrium M 0 before the application of an additional RF-pulse. Contrarily, with a short TR only tissue compartments with short T 1 will have time to recover before a new RF-pulse is applied. Hence, the signal from tissue compartments with a long T 1 will be attenuated. An additional not yet mentioned scanner parameter is the inversion time (TI). TI is present in the case of an inversion recovery sequence, with an inversion prepulse at time TI before the exciting RF-pulse. The inversion pre-pulse can be used to cancel out signals from a certain tissue. The inversion pre-pulse is an 180 -pulse 19

20 Conventional Contrast-Weighted Imaging that initially inverts the magnetization M 0. The application of the RF-pulse at time TI will cancel out signals from tissue passing the zero-signal line at that time. 3.1.1 PD-weight (a) (b) Figure 3.1. A PDw image is given using long TR and short TE. P Dw is achieved using long TR and short TE, which keep the T 1 w and T 2 w to a minimum (Fig 3.1.1). P Dw-images are not normally a part of general MR protocols of the brain. PD is strongly correlated with water content, which is rather similar in brain tissue, therefore P Dw-images do not yield very good image contrast for brain applications. 3.1.2 T 1 -weight (a) T1w Image (b) NavigationT1w Figure 3.2. A T 1w image is given using short TR and short TE.

3.1 Contrast Weighted Imaging 21 T 1 w-images are often referred to as anatomical scans since the T 1 -relaxation in different brain tissues are varying yielding good contrast between different tissues in the brain (Fig 3.1.2). In T 1 w-images the boundaries between tissues can be seen clearly. Fluids have almost no signal intensity due to the long T 1 -values while water based tissues appear mid-grey and fat based tissue very bright due to the short T 1. The signal from PD and T 1 -relaxation counteracts each other in T 1 w-images, since grey matter has higher PD but longer T 1 than white matter. 3.1.3 T 2 -weight (a) T 2 w Image (b) Navigation T 2 Figure 3.3. A T 2-weighted image is given using long TR and long TE. Fluids get the highest pixel intensity in T 2 w-images due to long T 2 -relaxation while water- and fat-based tissues appear mid-grey. In brain imaging T 2 w images are often referred to as pathology scans since many pathological processes in the brain result in an increased water content, making them easy to spot in T 2 w images as the image intensity is increased. 3.1.4 FLAIR FLAIR images (Fig 3.4) are achieved by applying an inversion pre-pulse to a T 2 w- image in order to cancel out the signal from cerebrospinal fluid (CSF). FLAIR images show brain interfaces very good and make it possible to distinguish cerebrospinal fluid (CSF) from other tissues or diseases that appear bright in T 2 w- images [8]. The Image - Pixel by Pixel An image stack used in MRI consists of a number of images representing the slices collected throughout the patient volume. The FoV and the number of sample points measured in the frequency encoding direction will together with the number of repetitions determine the image resolution. Each pixel in the image represents the signal from a voxel of the patient volume. Partial volume is a phenomenon

22 Conventional Contrast-Weighted Imaging Figure 3.4. In the FLAIR image the signal from CSF is cancelled out. that arises when a voxel contains a number of tissue compartments with different relaxation rates.

Chapter 4 Quantitative MRI The chapter explains the concept of qmri throughout the process of data acquisition, fitting of the data and post processing through the rendering of SyMRI images and the display of quantification maps. 4.1 The Concept of qmri While conventional contrast-weighted MRI is a qualitative technique where the intensity of a pixel in the final image have no absolute value, qmri is a quantitative approach taking MRI into the area of measurement science [4]. In qmri the quantitative and absolute values of the different MR parameters are measured for each voxel within the slices in the patient volume. The measured values can later be used for different kind of data analyses and image rendering. To illustrate the difference between the two techniques, conventional MRI can be described as a snap-shot imaging technique where a scanner sequence with predefined settings sample signals at time-points were good contrast between clinical important tissues are given. In the quantitative approach, the scanner sequence is used to collect data points in order to estimate the behaviour of the complete relaxation within the tissue and measure T 1, T 2 and PD (Fig 4.1). QMRI opens the possibility for reproducible and comparable measurements allowing reliable multi-centre studies and studies of disease progress and response to treatment in patients. There are researchers claiming that qmri opens the possibilities for a paradigm shift in the medical MR science. Until now clinical qmri has been constrained from being generally used, due to excessive scan times and extensive post-data processing [3]. 4.2 MR Parameters to Quantify In qmri the three MR parameters of clinical importance are T 1, T 2 and PD (Fig. 4.2). T 1 and T 2 provide information about the spin relaxation of the biological tissue while PD corresponds to proton density of the tissue. From a technical point of view, other MR parameters might be necessary to measure in order to 23

24 Quantitative MRI Figure 4.1. QMRI enables tissue to be accurately characterized through the absolute values of the MR parameters. In the figure the quantitative values of the four pixels indicates white matter or white matter looking tissue. The information is given regardless of the greyscale mapping of the image and neighbouring pixel intensities. Using conventional contrast MRI, the intensities of the pixels will not supply any quantitative information on the characteristics of the tissue. The clinical interpretation needs to be based on the image contrast and given knowledge about the specific scanner sequence used. get accurate and reproducible measurements. One such parameter is the B 1 -field strength to correct for B 1 -field inhomogeneity resulting in inaccurate flip-angles. Figure 4.2. The graph shows T 1, T 2 and PD and how they are related to the relaxation behaviour of tissue. 4.2.1 Tissue Characterisation By creating a 3D Cartesian grid with PD, R1 and R2 on the axes, tissue specific clusters can be identified in order to characterize tissue. At the moment, several research projects are aiming to investigate and map the MR parameter values in healthy and diseased tissues in order to use the absolute values as diagnostic tools, both for characterisation of disease and normal tissue. For example, T 1 has been shown to be affected in a number of neurological diseases such as multiple sclerosis (MS), intracranial tumours, stroke and dementia [4].

4.3 Data Acquisition 25 4.3 Data Acquisition In qmri, the scanner sequence performs the signal acquisition at a number of timepoints during the magnetic relaxation following the RF-pulses. The collected data points are used to fit the data to the relaxation behaviour predicted by the Bloch equations. The scanner sequence has to provide enough data to achieve acquired dynamic range of the parameters to be measured as well as sufficient SNR and resolution of the image. Due to the long scan times needed to achieve good SNR and reliable measurements, many scanners sequences will quantify only one of the MR-parameters. As an example, the present gold standard for T 1 quantification is the inversion recovery sequence. By repeating the scan and measure the signal with a number of different TIs, the complete T 1 -relaxation can be estimated. Multi echo acquisition methods are needed in order to measure T 2 and PD [4]. The sequence used for data acquisition within the synthetic imaging project at CMIV is QRAPMASTER [3]. 4.3.1 QRAPMASTER QRAPMASTER performs simultaneous quantification of T 1, T 2, PD and B 1 -field strength throughout the patient volume. The sequence was developed in order to scan a head within a clinical acceptable period of five minutes. The T 1 measurements are given through a spoiled saturation pulse, which also allows measurements of the B 1 -field strength. By dividing the scanner sequence into a saturation block and an acquisition block performing saturation and acquisition on different slices QRAPMASTER provides fast enough measurements. The number of scans and the delay times (TD) in between them determine the dynamic range of T 1. T 2 is measured using a fast multi echo gradient spin echo sequence (GRaSE) which uses EPI. The number of echoes and the spacing in between them determine the dynamic range of T 2. Additionally, the sequence includes a REST slab that is placed a distance away from the image volume. The REST slab saturates the signal from blood to prevent motion artefacts. This will cause all flowing blood to appear black in the images. A schematic picture of the sequence can be seen in Fig 4.3. PD is given in relation to M 0, which is calculated from T 1, T 2 and B 1 -field strength. 4.4 Fitting the Data to the Mathematical Models After the data acquisition, fitting of the data to the relaxation models are needed to calculate T 1, T 2 and PD. The most basic way to fit T 1 and T 2 is to assume a monoexponentional relaxation curve in each measured voxel. By doing so the relaxation within each voxel can be fitted by a least square fit to a monoexponential curve (Fig. 4.4), minimum two data points are needed. When using the golden standard for T 1 measurement as described in Section 4.3 the data can be fitted to the following equation S(T I) = S 0 (1 2e T I/T 1 ) where S 0 is the signal achieved from M 0 [4].

26 Quantitative MRI Figure 4.3. Schematic schedule of the QRAPMASTER scanner sequence (Ref [3], Page 321) Figure 4.4. relaxation. The data points are fitted to the mathematical models describing the In reality multiexponentional relaxation is common due to partial volume effects within the voxels. Simplification to a monoexponential fit is reasonable when the relaxation exchange between the compartments within the voxel is larger than the relaxation rate within each compartment [4]. In brain tissue, T 1 is often monoexponential while T 2 is bi- or multiexponential. If multiexponential relaxations are fitted to monoexponential curves the data will be scanner parameter dependent. 4.4.1 Quantification Maps Quantification maps are used to visualize the measured MR parameter values in each voxel. In Fig 4.4.1, a T 1 -map is displayed were the color of the pixels correspond to T 1 in milliseconds. The image analysis of the quantified values can be made in a number of ways including for example a region of interest (ROI), histogram analysis or voxel based group mapping [3].

4.5 Synthetic MRI 27 Figure 4.5. T1-map (Image retrieved from Contrast Predictor) 4.4.2 SyMRI BrainStudio Fitting Tool The SyMRI BrainStudio Fitting Tool is used to create T 1, T 2 and PD maps from the data acquired with QRAPMASTER. The SyMRI fitting tool uses a least square fit to a monoexponential curve for T 1 and T 2. B 1 -field strength is given from the saturation pulse and is together with T 1 and T 2 used to calculate an appropriate M 0. PD is then given from T 1, T 2, M 0 and a number of scaling factors in order to correct for a number of scanner parameters. 4.5 Synthetic MRI SyMRI uses T 1, T 2 and PD maps to post-synthesize contrast-weighted MRI images. 4.5.1 Synthetic Contrast Weighted MRI The image contrast that would have been given through a conventional scanner sequence can be post-synthetisized using Eq. 4.1 [3]. The equation predicts the signal intensity from a voxel with a certain T 1, T 2 and PD given the scanner settings TE and TR. This can be done since the quantified parameters give enough information to predict the complete relaxational behaviour of the tissue. To postsynthesize a contrast-weighted image that would have been given from an inversion recovery sequence, Eq. 4.2 is used in order to take account for the influence of the inversion prepulse [3]. S P D 1 e T R/T 1 1 e T R/T 1cosα e T E/T 2 (4.1) S P D 1 2e T I /T 1 + e T R/T 1 1 e T R/T 1cosα e T E/T 2 (4.2)

28 Quantitative MRI 4.5.2 Tissue Segmentation Synthetic images can also be calculated on a purely quantitative basis using tissue characterisation. In this way images can be displayed in a way that is not possible in conventional contrast-weighted MRI. The 3D Cartesian grid with PD, R1 and R2 on the axis will provide a foundation for tissue segmentation and the creation of contrast-weighted images with certain tissues suppressed. 4.5.3 Normalization In all conventional contrast-weighted images there is a certain amount of PDw since the proton density supply the origin of the signal detected by the MRI scanner. Since the PD contribution from each voxel is known using qmri the images can be normalized excluding the contrast dependence on PD giving images with pure T 1 w or T 2 w.

Chapter 5 Methods The chapter describes the methods used in this master thesis throughout the implementation and development of SyMRI Brain Studio, the interaction with radiologists and the validation of the technique. 5.1 SyMRI Brain Studio SyMRI Brain Studio was developed and implemented in an iterative manner in parallel with the continuous development of the SyMRI Plugin framework (Synthetic MR Technologies AB, 2007) and the improvement of the SyMRI Brain fitting tool and the scanner sequence settings. A number of versions of SyMRI Brain Studio were developed throughout the work. SyMRI Brain Studio v.1.0.0 was the first version developed, which was used for a first validation of an onsite radiologist (Sec 5.2.1). Together with user input from other radiologists and researchers SyMRI Brain Studio v.3.1.0 was developed and sent to a number of off-site hospitals for validation. SyMRI Brain Studio v.3.1.0 was additionally installed at CMIV for research and validation purposes and later upgraded with the successive versions v.3.2.0., v.3.3.0 and v.4.1.0. 5.1.1 System Environment SyMRI Brain Studio is developed as a visualization studio for SyMRI SECTRA IDS 5 Plugin in Microsoft Visual C++ 6.0. The SyMRI SECTRA Plugin provides a framework for SyMRI fitting tools and visualization modes using raw data stored as DICOM [9] files in SECTRA IDS 5. 5.2 Radiologist Interaction Radiologists with an interest in SyMRI were asked to participate in the work. Since the technique introduces a new concept of MRI, the most important issue was to find radiologists prepared to put their time on evaluating a not yet clinical 29

30 Methods introduced imaging technique. The meetings with the radiologists were arranged for two reasons. First, there was an interest in evaluating the technique from a radiologist s point of view, and to convert the demands from the radiologist into technical demands on SyMRI. Secondly, there was an interest in getting familiar with and understand the radiologist s working environment. The interaction with the radiologists was done under the principle that it is easier to learn about the users need and problems by learning the way they are working, rather than to ask questions about what they would expect from a new quantitative MRI technique. 5.2.1 Radiologist Interaction I Title: Comparison: Conventional T1w, T2w and FLAIR vs SyMRI Software: SyMRI Brain Studio v.3.0.0, Contrast Predictor v.1.1 beta, SECTRA IDS 5 Resolution: Conventional MRI 0.8mm, SyMRI: 1 mm Slice thickness: Conventional MRI 3mm, SyMRI: 5mm The aim with the meeting was to get an idea on how an experienced radiologist would sense the new technique and find it in comparison to conventional contrastweighted MRI. A week prior to the meeting, the radiologist had been given a short introduction to SyMRI Brain Studio and Contrast Predictor in order to be able to use and interact with the software independently. During the meeting the radiologist looked at SyMRI images alone as well as compared SyMRI to conventional contrast-weighted MRI. Information was also gathered regarding preferences on the user interface as well as windowing options and the possibilities of auto contrast. Additionally, an observation was made on how the radiologist interacts with IDS 5. During the day, a number of the SyMRI unique concepts were introduced in order to observe the radiologist s reaction on features such as contrast optimization, post-examination tissue nulling and PD normalisation that are only possible using SyMRI. The observer (i.e. the author) was taking basic notes in a continuous manner in order to collect as many thoughts and observations as possible without interfering with the workflow. The notes were put together into a document [10] which were read through and commented by the radiologist before finished. The document was written in Swedish to prevent language difficulties to interfere with the findings. The documentation was written in such a way that it could be used to evaluate future versions of SyMRI Brain Studio. Problem with the data transfer resulted in only one subject with available data for conventional contrast weighted MRI and SyMRI through SyMRI Brain Studio. Additionally six data set for SyMRI visualizations in Contrast Predictor were used from which four also were available in PACS. In total seven subjects were investigated, out of them three were diagnosed with multiple sclerosis (MS) and three with brain tumours. 5.2.2 Radiologist Interaction II Title: How are IDS 5 used by the radiologist today? Software: SECTRA IDS 5

5.3 Validation of the Technique 31 The meeting was made as an observation and discussion in order to observe how the radiologist uses conventional contrast-weighted MRI in the clinical routine. The radiologist was asked to explain the routine clinical work using a standard protocol for a brain examination. Additionally an observation was made on how the radiologist interacted with the SECTRA IDS 5. Notes were taken during the session and the findings where put together into a document [11]. 5.2.3 Radiologist Interaction III Title: Introduction to SyMRI Software: SyMRI Brain Studio v.4.1.0, SECTRA IDS 5 The radiologist was briefly introduced to the concept of SyMRI and a discussion about the SyMRI images and features was hold. Three different SyMRI data sets were looked at. 5.2.4 Radiologist Interaction IV Title: Comparison: Conventional T1w, T2w and FLAIR vs SyMRI - follow up Software: SyMRI Brain Studio v.3.1.0, Contrast Predictor v.1.1 beta, SECTRA IDS 5 Resolution: Conventional MRI 0.8mm, SyMRI: 1 mm Slice thickness: Conventional MRI 3mm, SyMRI: 5mm A follow up was made on Radiologist Interaction I in order to see what progress that had been achieved concerning the drawbacks found during the first meeting (Section 7.1). The data set that were used in Radiologist Interaction I was re-looked upon and additionally two data sets available for comparison between conventional contrast-weighted MRI and SyMRI Brain Studio. 5.2.5 Radiologist Interaction V Title: Response Leiden University Medical Centre (LUMC) Resolution: Conventional MRI 0.8mm, SyMRI: 1 mm Slice thickness: Conventional MRI 3mm, SyMRI: 5mm Software: SyMRI Brain Studio v.3.1.0 SyMRI Brain Studio was sent to LUMC in Leiden, Holland for validation on a 3T MRI scanner and to analyse the potential for SyMRI imaging of infants and small children. Their response was used as an additional input for this master thesis. 5.3 Validation of the Technique The validation was made as a discussion based on the information and knowledge gained from the work with the master thesis.

Chapter 6 SyMRI Brain Studio SyMRI Brain Studio is a visualization studio developed to visualize quantitative MR data of the brain using quantification maps and SyMRI. This chapter explains the features of Brain Studio, its design and the successive development of the different versions of the software. 6.1 Architecture Brain Studio is implemented in C++ as a class inheriting the SyMRI Plugin s virtual CSyMRIImage class. Necessary modifications in the source code of the SyMRI Plugin framework were made as described in the extensibility chapter in the SyMRI SECTRA IDS 5 Plugin Design document [5]. The architecture of SyMRI Brain Studio is divided into three sections, each with local functions implemented to customize the visualization mode and create the final user interface. Rendering Overridden functions: DrawImage() The rendering section calculates and visualizes the SyMRI images and quantification maps based on the user defined settings. A synthetic T 2 w image is displayed as default. Heads Up Display (HUD) Overridden functions: DrawHUD(), SetUpHudData() The HUD gives the end-user information about patient data, the quantitative data inside the region of interest (ROI) as well as the SyMRI settings of displayed contrast images. User Event Handler Overridden functions: HandleMenuSelection(), ModifyMenu(), OnKeyDown(), OnLMouseDown(), OnLMouseUp(), OnMouseMove(). User events are handled through menu selections, short commands and mouse bottom clicks. 33

34 SyMRI Brain Studio Figure 6.1. The visualization pipeline contains several steps ranging from the initial data acquisition to the rendering of images. SyMRI Brain Studio renders and visualizes the data through the quantification maps provided by the SyMRI fitting tool. An overview of the SyMRI visualization pipeline can be seen in Fig 6.1. The raw data is retrieved and handled as DICOM images by the SyMRI plugin framework. The quantified data is retrieved by the fitting tool and used by the visualization studio were the rendering of quantification maps and post-synthesized images are made. The SyMRI framework enables a debug mode that has been used for error search. 6.2 6.2.1 Main Features Quantification Maps The quantification maps display the quantified MR data pixel by pixel throughout each slice of the brain. In the four viewport default mode a T1 -map, a T2 -map and a PD-map is rendered and displayed together with the latest active contrast weighted SyMRI image (Fig 6.2). The quantification maps are visualized using a rainbow colormap and are autoscaled based on the absolute T1, T2 and PD values present in human brain (Table 6.1). Two additional quantification maps are accessible through the popup menu, the B1 -map displaying variations in the B1 -field and the mean-error-map supplying information about estimated errors in the fitting algorithm (Fig 6.3). Figure 6.2. Default display of quantification maps using four viewports. Upper left: T2 w Upper right: T1 -map Lower left: T2 -map Lower right: PD-map

6.2 Main Features 35 Figure 6.3. Additional quantification maps. Left: B 1 map Right: Mean Error Map Region of Interest The region of interest (ROI) is used to display information about the quantitative MR parameters of the pixels (Fig 6.4). The size of the ROI can be determined by the user, but are limited to a rectangle with a minimum of six viewport pixels. The measurement values of the pixels inside the ROI are displayed in the upper right corner of the HUD, displaying the mean and standard deviation of T 1, T 2, PD, the pixel intensity value inside the ROI and the ROI position in viewport coordinates. The relaxation rates within the ROI are plotted in the R 1 R 2 -plot. Figure 6.4. ROI and the supplied info displayed in the HUD R 1 R 2 -plot In the R 1 R 2 -plot, the relaxation rates are forming the two axes in a 2D Cartesian grid (Fig 6.5). Clusters characterising white matter, central white matter, internal capsule, putamen, grey matter and CSF are defined based on the mean values in Table 6.1 and are indicated in the plot as ellipses. The area of each cluster is based on empirical studies and on-going projects at CMIV working on the classification of the clusters given by the QRAPMASTER sequence. The bounding lines in between the clusters illustrate relaxation values possible caused by partial volume effects. The relaxation rates inside the ROI are plotted in the graph and each data point is stretched over an area corresponding to 0.05 ± 0.01s 1 in the R 1 - direction and 0.4 ± 0.08s 1 in the R 2 -direction. The intensity value given each pixel is linearly related the maximum distance in the x- or y-direction from the actual data point. Finally the pixel intensities are scaled in the interval [-mean/2

36 SyMRI Brain Studio mean/2] and are displayed with a threshold discriminating intensity values initially below zero. This is done in order to get a weighted plot. Figure 6.5. The relaxation rates are plotted and specific clusters indicate different types of brain tissue Table 6.1. MR parameter values for brain tissue. Tissue T 1 T 2 White Matter 570 75 Central White Matter 630 85 Internal Capsule 665 68 Putamen 800 73 Grey Matter 1100 96 CSF 3800 1800 Fat 320 90 6.2.2 Display of SyMRI Contrast-Weighted Images The contrast images are rendered based on the measured MR parameters and the current settings for TE, TR (Eq. 4.1), and TI (Eq. 4.2) in the case of a simulated inversion pre-pulse. The default settings with values corresponding to generally used scanner parameters (Table. 6.2) are accessed through the popup menu and keyboard accelerators. The parameters can be modified by the end user through menu options and the left mouse bottom to render any contrast-weighted image possible. The additional image settings: colormap, interpolation, modulus/real and autoscale are modified through the popup menu and keyboard accelerators. Fig 6.6 show the four viewport default display with a T 1 w image, a T 2 w image, a P Dw-image and a FLAIR image.

6.2 Main Features 37 Figure 6.6. Upper left: default T 1w Upper right: default T2w Lower left: default PDw Lower right: default FLAIR Table 6.2. Default Scanner Parameters Image TR TE TI Mode T 1 w 350 10 - - T 2 w 4500 100 - - PDw 6000 10 - - FLAIR 6000 120 200 Modulus Navigation Window The navigation window shows the current contrast-weight of the image in the viewport (Fig 6.7) and is displayed in the HUD in order to navigate the user to the current image-weight while freely selecting the scanner parameters TR and TE. Figure 6.7. The navigation window allows for user guidance when adjusting the current image-weight.

38 SyMRI Brain Studio Fat Suppression Menu options and keyboard accelerators for fat suppression are available (Fig. 6.8). When calculating the image a mask is applied that excludes the intensity contributions from fat. The fat suppression is unique to SyMRI and can not be achieved on the same basis in conventional imaging as the selection is based on the measured MR parameters. (a) T2 w Image (b) T2 w Image with Fat Suppression Figure 6.8. Fat Suppression is used to exclude the signal contribution from fat. T1 Enhanced Image (a) T1w Image (b) Enhanced T1 w Image Figure 6.9. The contrast between white and grey matter is enhanced through the PD-normalization. By excluding the PD-contribution in Eq 4.1 & Eq. 4.2 a PD-normalized image can be calculated neglecting the signal contribution from PD. This will create enhanced contrast between grey matter and white matter in T1 w images since the T1 and PD effects counteract each other in a conventional T1 w- image. (Fig. 6.9).

6.2 Main Features 39 6.2.3 Graphical User Interface The GUI is divided into a number of features, the popup menu, the HUD and the keyboard accelerators. PopUp Menu The pop-up menu is divided into four sections (Fig 6.10). The image settings section is partly pre-defined by the SyMRI framework and handles general image settings: pan, zoom, interpolation of the image, linking between slice selection in IDS5 and the plugin, autoscale, colormap, modulus or real image, linked viewports, settings of the R 1 R 2 -plot and the number of viewports displayed. The second section allows easy to use pre-defined default values for SyMRI contrast-weighted images, either one by one or as a four viewport default option supplying the user with the conventional set of contrast images for typical brain applications in addition with a P Dw image (Fig. 6.6). The third menu section visualizes the quantification maps one by one or as a four-viewport default option (Fig. 6.2). The fourth section provides options for modification of the simulated scanner parameter settings TE, TR and TI as well as the simulation of an inversion pre-pulse and qmri features in terms of fat suppression, PD-normalization and T 1 enhancement. Additionally there is a stack tile sub-menu and a close bottom provided by the SyMRI framework. Figure 6.10. Popup Menu Heads Up Display The HUD supplies the user with information regarding the examination, the patient, the image settings and the quantified data inside the ROI. Information on the

40 SyMRI Brain Studio name of the patient, date of birth, date and time of examination, sequence used, slice thickness, FoV, current slice number and position within slices are retrieved from the SyMRI framework and displayed in SyMRI Brain Studio. Information about the SyMRI contrast image settings are displayed in terms of the current TE, TR, TI and through the navigation window. In the case of default contrast images, active T 1 enhancement or active fat suppression a label indicating their presence is shown in the central upper part of the HUD. The software version and a caution label indicating that the software is an investigational device are also displayed. The ROI information is displayed as explained in section 6.2.1. In the upper right corner the scale window parameters are shown, C indicating window centre and W indicating window width. Keyboard Accelerators The keyboard accelerators are available to give the end user a smooth working environment. The accelerators are based on the name of the features - autoscale - A, fat suppression F, inversion pre-pulse I etc. The default contrast SyMRI images are indexed by numbers ranging from one to five and the numbers seven to zero are dedicated to the different quantification maps. 6.2.4 Autoscale The autoscale algorithm for contrast-weighted images was given through multiple regression analysis resulting in the following equations: W = 100+2 abs(mean), C : 80 + 1.7 C + 0.6 mean. The equations were derived based on the mean value and standard deviation of the pixels in the images as well as the width and centre of satisfying scaling windows. Synthetic T 1 w, T 2 w, P Dw and FLAIR images with satisfying image contrast from three complete data sets were analysed. The quantification maps all have individual scaling settings based on their T 1, T 2 and P D value. The autoscale is automatically turned off in between slices in order to get a constant colormap throughout the brain. 6.2.5 Colormap The colormap used for all colored images is the hue based rainbow colormap ranging from blue to cyan, green, yellow and red. Blue indicates the lowest pixel value. 6.2.6 Colorbar The colorbar is displayed in the HUD for all coloured images and is integrated in the SyMRI logo. The colorbar ticks indicate max value, min value and window centre (Fig 6.11).

6.3 SyMRI Brain Studio Releases 41 Figure 6.11. The colorbar. 6.2.7 The Font The font used is Arial and the font size is optimized to be as large as possible without interfering with the display of the calculated image. The font size is implemented in the function GetPerfectFont(). In the case of a too small viewport no HUD is displayed and an error message appears on the screen. 6.3 SyMRI Brain Studio Releases The previous parts of this chapter have explained SyMRI Brain Studio as it appeared in the end of the work with this master thesis. The following section will guide the reader through the successive development throughout the different versions of SyMRI Brain Studio. 6.3.1 v.1.0.0 SyMRI Brain Studio v.1.0.0 was the version used in the first radiologist interaction (Section 5.2.1). All possible SyMRI images could be rendered using TR, TE and TI modifications and a simple R 1 R 2 -plot and a navigation window were displayed in the HUD. 6.3.2 v.3.1.0 Based on the findings in Radiologist Interaction I and II (Section 5.2.1 & 5.2.2) SyMRI Brain Studio v.3.1.0 was developed. Major changes were the re-arrangement of the information to be found in the HUD and the extended and developed R 1 R 2 - plot. The HUD was divided into a more logical way, the navigation window was made smaller and the navigation curse was made circular instead of squared. Additionally keyboard accelerators were introduced in order to make the user interface easier. Also the algorithm for choosing font size were improved in order to enlarge the font when possible, to be compatible with the split of screens in IDS5 and to fit the R 1 R 2 -plot even within small viewports. A major improvement for v.3.1.0 was the improved implementation of the R 1 R 2 -plot. The time needed to plot the graph

42 SyMRI Brain Studio was enormously reduced by introducing a bitmap back-buffer instead of plotting the values one by one in the present device context. This improvement eliminated the previous problem with too slow calculations making it almost impossible to plot data from a whole slice as well as scroll between slices and to use the R 1 R 2 - plot within more than one viewport. Additionally the R 1 R 2 plot was improved by weightening the plot which allowed for an assessment of a ROI containing the complete image slice without getting a completely white graph since all pixels were hitted at least once. In addition, a median filter was applied to get rid of some noise assuming that all important features of the brain are at least nine voxel in size. Axis and ticks were added to the plot to give more information to the user. Additionally, all options for image settings in the popup menu were moved to the submenu called image settings in order to reduce the size of the menu. 6.3.3 v.3.2.0 Additional SyMRI image options were added through T 1 enhancement and an IR Real image. An additional general R 1 R 2 -plot in order to allow examination of other parts of the body than the brain using SyMRI Brain Studio was implemented. 6.3.4 v.4.1.0 This version was mainly not developed by the author, except from minor changes in the structure of the code. The version is equipped with a segmentation mode, which is neither explained nor evaluated in this master thesis.

Chapter 7 Radiologist The chapter explains the outcome of the interactions with the radiologists and describes how they effected the development of SyMRI Brain Studio and the underlying imaging technique. Future development suggestions and conclusions made can be found in Chapter 9 and 10. 7.1 Radiologist Interaction I Radiologist: Bengt Petré, University Hospital in Linköping, Sweden Date: 3/3/2008 A number of factors made the radiologist experience SyMRI Brain Studio insufficient in comparison to conventional contrast-weighted MRI. The radiologist had the opinion that these shortcomings should be rectified before further evaluation of the software. A list of eight major drawbacks were found, which were analysed in order to conclude the technical changes needed (Section 7.1.1). Additionally, the radiologist pointed out some clear advantages with SyMRI (Section 7.1.2) and a list of possible future applications was made (Section 7.1.3). The complete document from the meeting can be found in [10]. Furthermore, the day resulted in a number of development suggestions on the SyMRI Brain Studio interface and the SyMRI plugin as a whole. 7.1.1 Drawbacks Insufficient Pixel Resolution The radiologist found the images displayed in SyMRI Brain Studio to have bad pixel resolution in comparison to the conventional MRI images (Fig 7.1.1). One reason for this is the slightly decreased spatial resolution of the SyMRI images, but the main reason is that no interpolation was used when displaying images in SyMRI Brain Studio v.1.0.0. In IDS 5 interpolation is used when displaying enlarged pixels. A function for image interpolation was implemented in the SyMRI framework using bilinear interpolation. 43

44 Radiologist The complete brain is not covered in the examination In order to keep down the scan time, the complete head of the patient was not included in the FoV. The radiologist found this insufficient as it is important to get a general overview of the complete brain when examining a patient, even when the searched disease is present in restricted parts. By increasing the number of slices acquired in the QRAPMASTER sequence, data from the complete brain could be measured. The most posterior image slice has insufficient image quality In the slice closest to the neck deviating pixel intensities and several artefacts are present. The artefacts appear due to the REST slab placed at the neck of the patient. To reduce the artefacts the REST slab where moved further away from the most posterior image slice. Lack of anatomic details The radiologist experienced a lack of anatomic detail in the synthetic images in cerebellum, tractus opticus and some other parts of the brain (Fig 7.2(b)D). The lack of anatomic detail can be caused by insufficient spatial resolution, insufficient SNR or errors introduced in any of the post-examination calculations, i.e. the fitting algorithm or the synthetic calculations. The resolution in the image can theoretically be improved, but there is a strong correlation between the resolution, SNR and scan time. For a spin echo sequence such as QRAPMASTER, the SNR can be explained through Eq 7.1. The effect on the SNR when changing the imaging parameters can be explained as a ratio, given that the FoV is kept constant and N i = F ovi δi, Eq 7.1 gives Eq 7.2. SNR δxδyδz N acq Nx N y N z δt (7.1) where δx, δy, δz is the resolution in each axis, N acq = number of acquisitions, N x, N y, N z = number of data points sampled in each direction, δt = acquisition time at each echo. SNR ABratio = δx3/2 A δy3/2 δx 3/2 B A δz A δta (7.2) δtb δy3/2 B δz B As shown, increased resolution will decrease SNR and additionally increase the scan time as there are additional number of lines to be measured in the phase encoding direction. Scans with improved resolution were made using an in-plane resolution of 0.7 mm rather than 1 mm. The scan time was kept constant but the SNR were affected. Artefacts around the cranium and the dorsal parts of the Brain The artefacts in Fig 7.2(a)B are appearing due to a water-fat shift in the frequency encoding direction. Looking at the images the radiologist concludes that the chemical shift interfere with the signals of the brain. The water-fat

7.1 Radiologist Interaction I 45 shift is caused by an always-present difference in the Larmor frequency between the protons in water and the protons in lipids, even though exposed the same magnetic field strength. The difference of 3.4 ppm is caused by the differences in molecular structure between the two compounds [4]. Since the spatial encoding in the frequency encoding direction is based on frequency differences as described in Eq. 2.1, the difference in the Larmor frequencies will lead to a misregistration of the signal. The signal from fat, which will have the lower frequency, will be mapped to a pixel that is not corresponding to the voxel the signal comes from. The water-fat shift in mm can be calculated as W F shift = f diff N RO BW rec F ov RO N RO (7.3) W F shift = the water-fat shift in mm, f diff = the water-fat shift in Hz, N RO = number of pixels in the frequency encoding direction, F ov RO =FoV in the frequency encoding direction, BW rec = receiver bandwidth. To reduce the water-fat shift BW rec was decreased resulting in a shorter acquisition time for each echo. A larger number of echoes were then needed in order to collect all data points and avoid a negative effect on the SNR. The scan time was not affected as other parameters of the scan where slightly changed. This resulted in a water-fat shift reduced by a factor 2. Problem with visualization of tissue with high water content In the synthetic FLAIR images, the visualization of the CSF appears insufficient as it is speckled rather than black which is the case and purpose of a conventional FLAIR image (Fig 7.2(a)A). The artefacts are probably caused by a too low SNR or by miscalculations in the fitting algorithm or the synthetic equations. The low SNR is apparent due to the long T 1 and T 2 of CSF. The large values will be at the edge of the dynamic range of measurements, making the measurements uncertain and noisy. The visualizations might be improved with an increased dynamic range or increased SNR, but as already explained in Eq 7.1, there is a trade-off between SNR, scan time and resolution. The fitting algorithm where improved in attempt to get an improved visualization. Artefacts make healthy matter look diseased The artefacts (Fig 7.2(a)C) make healthy tissue appear diseased, as the high pixel intensity would indicate inflammation in a conventional MRI image. The artefacts are mainly seen in the synthetic FLAIR images but are also present in the left dorsal part of the brain in other images. The artefacts are present in and around wrinkles and in the boundaries between CSF and other tissue, especially in the anterior parts of the brain. Noise, motion artefacts, water-fat shift or partial volume could be the reason to the artefacts. Insufficient contrast in T 1 w images When comparing the synthetic T 1 w-images to conventional T 1 w-images the

46 Radiologist contrast between white and grey matter is insufficient in the synthetic images. The reason to the lack of contrast is not yet clarified. The method used for the synthetic calculations is following published equations for reconstructed T 1 w-images [3]. At the moment, research is done in order to evaluate whether the scanner sequence or any of the post-examination calculations are the reason for the lack of contrast. In some synthetic images visualized in Contrast Predictor using data sets from 2007 there are a rather good contrast between white and grey matter. The same images also show improved visualizations of fluids in FLAIR images. One thesis is that the enhanced contrast is caused by the fact that a quadrate head coil rather than a SENSE head coil was used during these examinations. Theoretically, the SENSE coil should have a higher SNR, since it allows SENSE, shorter scan times can be achieved. Since the coil has passed the manufacturer s quality assessment it is hard to affect this aspect of the problem. In order to compensate for the lack of contrast between white and grey matter, the T 1 enhancement was introduced in Brain Studio (Section 6.2.2). Figure 7.1. The pixel resolution in SyMRI Brain Studio is low compared to the images in IDS 5. 7.1.2 Advantages SyMRI Short Scan Time In a conventional image protocol each contrast-weighted image takes between two to five minutes to acquire. With a single scan that provides enough information to recreate all possible contrast-weighted images there are possibilities for a clear advantage using SyMRI, not only in terms of scan time but also in terms of stored information. Improved information A single pixel in a conventional MRI image does not contain any absolute information regarding the underlying tissue. SyMRI adds a quantitative

7.1 Radiologist Interaction I 47 Figure 7.2. A) Noisy visualization of CSF, B) Large water-fat shift, C) Healthy tissue appear diseased, D) Lack of anatomic detail. dimension to the data which supplies additional information with potential to improve the diagnosis. Easy to change between images In IDS 5 it is not possibility to shift between different contrast-weights while keeping the visual point of the eye. In SyMRI you can easy look and compare images in the same viewport shifting between different weights using a single bottom press or continuous adjustments. 7.1.3 Future Potential of SyMRI Acute Examination of Patients The decreased scan time might allow the possibility of an increased patient throughput giving shorter patient queues and also the possibility to examine patients that have not been able to be examined using MRI before due to the long examination times. Easy tissue nulling Using either simulated inversion pre-pulses or tissue classification, tissues can easily be cancelled out as in conventional Short TI Inversion Recovery (STIR) and FLAIR images. This is done with a mouse click rather than a new scan. Segmentation as screening By using tissue segmentation all normal appearing tissue could be selected leaving non-healthy or unknown tissue. By presenting this information as an overlay to a contrast-weighted image, the data could be used as screening in order to make the radiologist observant on critical parts of the image. Examination of tumours In today s clinic it is hard to distinguish brain tumours from oedema. Looking at the SyMRI data sets, tissue nulling seems to have the potential to