The SIMRI project A versatile and interactive MRI simulator *

Similar documents
Imaging Notes, Part IV

(a Scrhon5 R2iwd b. P)jc%z 5. ivcr3. 1. I. ZOms Xn,s. 1E IDrAS boms. EE225E/BIOE265 Spring 2013 Principles of MRI. Assignment 8 Solutions

Following on from the previous chapter, which considered the model of the simulation

Research Article Simulation of High-Resolution Magnetic Resonance Images on the IBM Blue Gene/L Supercomputer Using SIMRI

White Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space

Diffusion MRI Acquisition. Karla Miller FMRIB Centre, University of Oxford

SIMULATION D'IRM ANGIOGRAPHIQUE PAR EXTENSION DU LOGICIEL JEMRIS

MRI image formation 8/3/2016. Disclosure. Outlines. Chen Lin, PhD DABR 3. Indiana University School of Medicine and Indiana University Health

2.1 Signal Production. RF_Coil. Scanner. Phantom. Image. Image Production

MRI. When to use What sequences. Outline 2012/09/19. Sequence: Definition. Basic Principles: Step 2. Basic Principles: Step 1. Govind Chavhan, MD

Following on from the two previous chapters, which considered the model of the

MRI Physics II: Gradients, Imaging

Slide 1. Technical Aspects of Quality Control in Magnetic Resonance Imaging. Slide 2. Annual Compliance Testing. of MRI Systems.

Midterm Review

A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction

surface Image reconstruction: 2D Fourier Transform

Exam 8N080 - Introduction MRI

Fast Imaging Trajectories: Non-Cartesian Sampling (1)

XI Signal-to-Noise (SNR)

High performance MRI simulations of motion on multi-gpu systems

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

Regularized Estimation of Main and RF Field Inhomogeneity and Longitudinal Relaxation Rate in Magnetic Resonance Imaging

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2006

Module 4. K-Space Symmetry. Review. K-Space Review. K-Space Symmetry. Partial or Fractional Echo. Half or Partial Fourier HASTE

CHAPTER 9: Magnetic Susceptibility Effects in High Field MRI

PHASE-ENCODED, RAPID, MULTIPLE-ECHO (PERME) NUCLEAR MAGNETIC RESONANCE IMAGING

Motion Correction in fmri by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction

Lab Location: MRI, B2, Cardinal Carter Wing, St. Michael s Hospital, 30 Bond Street

Spatially selective RF excitation using k-space analysis

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images

Functional MRI. Jerry Allison, Ph. D. Medical College of Georgia

Field Maps. 1 Field Map Acquisition. John Pauly. October 5, 2005

A Virtual MR Scanner for Education

Histograms. h(r k ) = n k. p(r k )= n k /NM. Histogram: number of times intensity level rk appears in the image

Steen Moeller Center for Magnetic Resonance research University of Minnesota

3-D Gradient Shimming

How to cite this article: emagres,2016,vol5: DOI / emrstm1454

A Novel Iterative Thresholding Algorithm for Compressed Sensing Reconstruction of Quantitative MRI Parameters from Insufficient Data

K-Space Trajectories and Spiral Scan

Computer-generated fmri phantoms with motion distortion interaction

Orthopedic MRI Protocols. Philips Panorama HFO

Module 5: Dynamic Imaging and Phase Sharing. (true-fisp, TRICKS, CAPR, DISTAL, DISCO, HYPR) Review. Improving Temporal Resolution.

Supplementary methods

Improved Spatial Localization in 3D MRSI with a Sequence Combining PSF-Choice, EPSI and a Resolution Enhancement Algorithm

New Technology Allows Multiple Image Contrasts in a Single Scan

COBRE Scan Information

RADIOMICS: potential role in the clinics and challenges

PHASE-SENSITIVE AND DUAL-ANGLE RADIOFREQUENCY MAPPING IN. Steven P. Allen. A senior thesis submitted to the faculty of. Brigham Young University

Phase Difference Reconstruction. Outline

Basic principles of MR image analysis. Basic principles of MR image analysis. Basic principles of MR image analysis

Functional MRI in Clinical Research and Practice Preprocessing

Advanced Imaging Trajectories

MIDAS Signal Calibration

TOPICS 2/5/2006 8:17 PM. 2D Acquisition 3D Acquisition

TEPZZ A_T EP A1 (19) (11) EP A1 (12) EUROPEAN PATENT APPLICATION. (43) Date of publication: Bulletin 2012/36

Role of Parallel Imaging in High Field Functional MRI

Non-Cartesian Parallel Magnetic Resonance Imaging

MRI Imaging Options. Frank R. Korosec, Ph.D. Departments of Radiology and Medical Physics University of Wisconsin Madison

SPM8 for Basic and Clinical Investigators. Preprocessing

Evaluations of k-space Trajectories for Fast MR Imaging for project of the course EE591, Fall 2004

UNIVERSITY OF SOUTHAMPTON

Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories

SPM8 for Basic and Clinical Investigators. Preprocessing. fmri Preprocessing

Breast MRI Accreditation Program Clinical Image Quality Guide

MR Advance Techniques. Vascular Imaging. Class III

Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging

M R I Physics Course

Refractive Index Map Reconstruction in Optical Deflectometry Using Total-Variation Regularization

Parallel Magnetic Resonance Imaging (pmri): How Does it Work, and What is it Good For?

Improvements in Diffusion Weighted Imaging Through a Composite Body and Insert Gradient Coil System

Preprocessing II: Between Subjects John Ashburner

Fmri Spatial Processing

Image Registration + Other Stuff

Lucy Phantom MR Grid Evaluation

Basic fmri Design and Analysis. Preprocessing

Optimizing Flip Angle Selection in Breast MRI for Accurate Extraction and Visualization of T1 Tissue Relaxation Time

Scan Acceleration with Rapid Gradient-Echo

High Fidelity Brain Connectivity Imaging

Institutionen för medicin och hälsa

EPI Data Are Acquired Serially. EPI Data Are Acquired Serially 10/23/2011. Functional Connectivity Preprocessing. fmri Preprocessing

Use of MRI in Radiotherapy: Technical Consideration

Multiresolution analysis: theory and applications. Analisi multirisoluzione: teoria e applicazioni

CORRECTION OF IMAGE DISTORTION IN ECHO PLANAR IMAGE SERIES USING PHASE AND INTENSITY. Ning Xu. Dissertation. Submitted to the Faculty of the

EE225E/BIOE265 Spring 2011 Principles of MRI. Assignment 5. Solutions

Motion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fmri

MR IMAGE SEGMENTATION

Sources of Distortion in Functional MRI Data

Accelerated MRI Techniques: Basics of Parallel Imaging and Compressed Sensing

DEVELOPMENT AND VALIDATION OF ALGORITHMS FOR MRI SIGNAL COMPONENT ESTIMATION

Qualitative Comparison of Conventional and Oblique MRI for Detection of Herniated Spinal Discs

Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI

Filtering, reconstruction and registration of 3D Ultrasound Images

Modified Dixon technique for MRI water-fat separation using jointly amplitude and phase.

Fast Imaging UCLA. Class Business. Class Business. Daniel B. Ennis, Ph.D. Magnetic Resonance Research Labs. Tuesday (3/7) from 6-9pm HW #1 HW #2

PSI Precision, accuracy and validation aspects

Removal of EPI Nyquist Ghost Artifacts With Two- Dimensional Phase Correction

FOCUS. Image Based Automatic Shimming Using B 0 Gradients. Installation and Users Guide Version 0.9

Automatic Recognition of Regions of Interest in Synthetic MRI

Efficient Fourier transformation of unstructured meshes and application to MRI simulation. Alejandro Martinez

Transcription:

COST B21 Meeting, Lodz, 6-9 Oct. 2005 The SIMRI project A versatile and interactive MRI simulator * H. Benoit-Cattin 1, G. Collewet 2, B. Belaroussi 1, H. Saint-Jalmes 3, C. Odet 1 1 CREATIS, UMR CNRS #5515, U 630 Inserm, INSA Lyon, UCB Lyon 2 CEMAGREF, Food process Engineering Research Unit, Rennes 3 LMRMN-MIB, UMR CNRS 5012, UCB Lyon * JMR, 173 (2005) 97-115 1. Context 2. SIMRI overview 3. Simulation results 4. Simulator implementation 5. Perspectives 2/32

1. Context MR Imaging Recent and complex technique Based on protons magnetization High static field + RF excitations Contrast imaging adapted to soft tissue Images +- artéfacts Objects (Chemical shift, susceptibility, motion) Imaging device (Field, RF inhomogeneity, gradient non linearity) 3/32 MRI simulation Better understanding of MRI images Pedagogic purposes Conception, calibration and test of MRI sequences MRI Images with a «ground truth» Artifacts impact and correction Image processing assessment (segmentation, quantification) Main works 1D MRI simulation [Bittoun-81] 2D MRI simulation [Olsson-95] Simulation with a distributed implementation [Brenner-97] 3D brain MRI simulation [Kwan-99] Susceptibility and MRI simulation [Yoder-02-04] 4/32

2. Simulator overview 2.1. SIMRI overview 2.2. Virtual object description 2.3. Static field and field inhomogeneity 2.4. MRI sequence 2.5. Magnetization kernel 2.6. T2* effect 2.7. Noise and filtering 5/32 2.1. SIMRI overview Noise Virtual object (i,ρ,t 1,T 2 ) Magnetization computation kernel k space (RF signals) B 0 +( B 0 map) MRI Sequence RF Pulse Gradient Precession Acquisition Filtering Reconstruction algorithm MR image 6/32

2.2. Virtual object description 3D volume of physical parameters ρ, proton density T1, T2, spin relaxation constant 1 to N components (Chem. Shift, partial volume effect) Object dimension, component resonance frequency B s : susceptibility associated field Synthetic objects perfectly known Sphere, ellipse, cube Adapted McGill brain phantom 256 3 Real images segmentation + (T1,T2 maps) objects Virtual object (i,ρ,t 1,T 2 ) 7/32 2.3. Static field and field inhomogeneity r r r r r r B ) z = B ( ) z + B ( ) z ( s 0 r B s (r ) B ( r ) linked to the susceptibility variation linked to the main field inhomogeneity B i 0 r linked to the intra-voxel inhomogeneity, It induces a T2* FID weighting e γ B i t T 1 * 2 1 = + γ T 2 B i 8/32

2.4. MRI sequence 4 types of event within C language functions Free precession (duration) Precession + gradient (x,y,z) RF pulse +- gradient - Constant pulse (duration, flip angle, rotation axis) - Sinc shaped pulse (duration, number of lobes, number of point) - User shaped pulse (file, constant pulse list) 1D signal acquisition step (number of points, bandwidth, readout gradient, k space position) 9/32 2.5. Magnetization kernel Do Pulse Do Gradient M r Do Waiting (t) M r Magnetic ( t 1 ) event RF acquisition k-space filling M r ( t 2 ) Kernel is based on the Bloch Equation r M x / T2 dm r r = γ.( M B) M y / T2 dt ( M z M 0 ) / T1 and on their discrete time solution r r r r M (, t + t) = Rot ( θ ). Rot ( θ ). R. R. M (, t) z g z i relax RF 10/32

2.6. T2* effect, limited spin number Lorenzian distribution of isochromats 1 1 FID weighting e γ B i t = + γ T * T 2 2 B i Spin refocusing, Spin Echo α1 TE/2 α2 TE/2 exp(- B τ) i a) b) c) τ=0 τ=-τ (τ=+t) τ (τ=+t) τ (τ=+t) τ t 11/32 2.7. Noise and filtering Thermal noise White Gaussian noise in the k-space K-space filtering Hamming (or any digital filter), prevent ringing Reconstruction FFT 12/32

3. Simulation results 3.1. Echo train 3.2. Contrast in GE and SE imaging 3.3. True Fisp imaging 3.4. Chemical shift artifact 3.5. Susceptibility artifact 13/32 3.1. Echo train and T2* 6000 3500 5000 3000 4000 2500 M xy 3000 T2 M xy 2000 1500 T* 2 T2 2000 1000 1000 500 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 t(s) te/2 te 90 180 180 180 180 180 0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 90 Gx te/2 te t(s) Simulated signal obtained after a CPMG sequence. It is composed of a train of spin echoes T2 weighted. The intra-voxel inhomogeneity is set to 10-6 T and the main field to 1 T. Simulated signal obtained after a gradient echo pulse sequence. It is composed of a train of T2* weighted gradient echoes. The intra-voxel inhomogeneity is set to 10-6 T and the main field to 1 T. 14/32

3.2. Contrast in SE and GE imaging SE sequence GE sequence 15/32 McGill Brain phantom 10 tissues profiles 16/32

3.3. True Fisp imaging 17/32 3.4. Chemical shift artifact 2 π f TE = (2k + 1)π Water and fat signals in phase opposition! 18/32

3.5. Susceptibility artifact Geometric distortions + Intensity loss 19/32 4. Simulator implementation 3.1. Code organization 3.2. Sequence programming 3.3. Acquisition programming 3.4. 1D interactive simulation 3.5. Distributed implementation 20/32

3.1. Code organization ANSI C language Running on Linux and windows OS Independent module organization Linked in a dll wrapped for being used with python for the 1D interface Parallelized using MPI to run on grid, cluster, multiprocessors. 21/32 3.2. Sequence programming 22/32

3.3. Acquisition programming 23/32 3.4. 1D interactive simulation 24/32

The Spin Player 25/32 3.5. Distributed implementation Time is the enemy! Simulation time T Texc + Tacq = c X. Y. Z).[( M. N. P) + N ] s.( ex 2D Image (NxN) -Nx2 time x16-1024 2 = 2.3 days - High resolution : small cluster 3D image(nxnxn) -Nx2 time x 64-128 3 = 9.3 days / 512 3 = 104 years! - High resolution large scale data grid 26/32

A Divide & Conquer parallelisation scheme Based on the free library MPI, transparent at a user level Allowing run on single PC, PC cluster, grid architecture, massively parallel machine Simulation web portal Work done in the context of European grid projects - DATAGRID Project (2001-03) - EGEE Project (2003-05) 27/32 5. Conlusion An operational MRI simulator 1D-2D-3D Sequences (SE, GE, Turbo, TFisp, STIR) Artifacts : Chemical shift, susceptibility, static field B 0, T2*, Off-On resonance Versatile, Parallelized, Interactive and GPL! 28/32

6. Perspectives Main perspectives Anatomic object design Sequence tuning Image processing evaluation Molecular imaging susceptibility and Cell. Con. agent New sequences / Interface with ODIN project RF / Antennas Artifact correction Flow, Diffusion & Perfusion? 29/32 30/32

B s Object ( ki ) Field map estimation Boundary element [Yoder02] Acquisition [Kanayama_96] - 2 EG phase images with TE 1, TE 2 - Φ + unwrap [Jenkinson_03] > B 0 r B s (r) SIMRI Boundary Integral [Balac04] B (P) NbFace 1 = Bo Xm(P) δω(p) Xm n Bo C(P, q)dsq 4π k= 1 Face k 31/32 t+ t r r θ = γ.. G( τ ) dτ g r θ = γ. B( ). t i t gradient effect Inhomogeneity effect cosθ sinθ Rot z ( θ ) = sinθ cosθ 0 0 0 0 1 R relax t r T2 ( e = 0 0 ) e 0 t r T2 ( ) 0 0 0 1 e t r T1 ( ) Relaxation effect R RF = z x z Rot ( φ). Rot ( α). Rot ( φ ) Pulse effect α' = τ. ( ω) 2 α + τ 2 Off-resonance effect 32/32

K-space acquisition - One point is obtained by summation all over the object r r r s[ t] = M (, t). x + j r r r r r M (, t). y - Next point is obtained after a time step t, the sampling rate 33/32 3.5. Distributed implementation Time is the enemy! Simulation time T Texc + Tacq = c X. Y. Z).[( M. N. P) + N ] s.( ex 2D Image (NxN) -Nx2 time x16-1024 2 = 2.3 days - High resolution : small cluster 3D image(nxnxn) -Nx2 time x 64-128 3 = 9.3 days / 512 3 = 104 years! - High resolution large scale data grid 34/32

A Divide & Conquer parallelisation scheme Based on the free library MPI Transparent at a user level Allowing run on single PC, PC cluster, grid architecture, massively parallel machine Virtual object portions RF signal contributions Virtual object 1 Seq. Computing node N 1 s 1 MRI Seq. Master node i N-1 Seq. Computing node N i Computing node N N Seq. s i s N-1 Rec. Σ FFT k space Master node MRI image 35/32 Some test results : - PC cluster CREATIS : 8 (PIII-1Ghz) + 10 (PIV-2,6 Ghz) - CINES parallel machine : 64 to 128 nodes, RI 4000-500 Mhz - IN2P3 through EGEE grid interface : 9 AMD Opteron 2.2 Ghz -128 3 : 8h30 on a 128 proc. CINES machine Work done in the context of European grid projects - DATAGRID Project (2001-03) - EGEE Project (2003-05) 36/32

37/32