Biomedical Imaging. Computed Tomography. Patrícia Figueiredo IST

Similar documents
Introduction to Biomedical Imaging

BME I5000: Biomedical Imaging

MEDICAL IMAGING 2nd Part Computed Tomography

MEDICAL IMAGING 2nd Part Computed Tomography

Joint ICTP-TWAS Workshop on Portable X-ray Analytical Instruments for Cultural Heritage. 29 April - 3 May, 2013

Moscow-Bavarian Joint Advanced Student School 2006 / Medical Imaging Principles of Computerized Tomographic Imaging and Cone-Beam Reconstruction

Digital Image Processing

Multi-slice CT Image Reconstruction Jiang Hsieh, Ph.D.

Computed Tomography. Principles, Design, Artifacts, and Recent Advances. Jiang Hsieh THIRD EDITION. SPIE PRESS Bellingham, Washington USA

Spiral CT. Protocol Optimization & Quality Assurance. Ge Wang, Ph.D. Department of Radiology University of Iowa Iowa City, Iowa 52242, USA

Image Acquisition Systems

Computer-Tomography II: Image reconstruction and applications

MEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY. Prof. Yasser Mostafa Kadah

CLASS HOURS: 4 CREDIT HOURS: 4 LABORATORY HOURS: 0

Corso di laurea in Fisica A.A Fisica Medica 4 TC

Tomographic Reconstruction

CT: Physics Principles & Equipment Design

Medical Imaging BMEN Spring 2016

Some reference material

Central Slice Theorem

Principles of Computerized Tomographic Imaging

Enhancement Image Quality of CT Using Single Slice Spiral Technique

Optimization of CT Simulation Imaging. Ingrid Reiser Dept. of Radiology The University of Chicago

A closer look at CT scanning

DEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE

Introduction to Medical Imaging. Lecture 6: X-Ray Computed Tomography. CT number (in HU) = Overview. Klaus Mueller

Radon Transform and Filtered Backprojection

Effect of Scattering on the Image. Reducing Compton Scatter with a Grid

Implementation and evaluation of a fully 3D OS-MLEM reconstruction algorithm accounting for the PSF of the PET imaging system

Introduction to Positron Emission Tomography

ML reconstruction for CT

Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT

8/7/2017. Disclosures. MECT Systems Overview and Quantitative Opportunities. Overview. Computed Tomography (CT) CT Numbers. Polyenergetic Acquisition

Medical Image Reconstruction Term II 2012 Topic 6: Tomography

Ch. 4 Physical Principles of CT

Computer-Tomography I: Principles, History, Technology

Shadow casting. What is the problem? Cone Beam Computed Tomography THE OBJECTIVES OF DIAGNOSTIC IMAGING IDEAL DIAGNOSTIC IMAGING STUDY LIMITATIONS

Image Reconstruction from Projection

Radiology. Marta Anguiano Millán. Departamento de Física Atómica, Molecular y Nuclear Facultad de Ciencias. Universidad de Granada

Fits you like no other

RADIOLOGY AND DIAGNOSTIC IMAGING

MEDICAL IMAGE ANALYSIS

Index. aliasing artifacts and noise in CT images, 200 measurement of projection data, nondiffracting

3/27/2012 WHY SPECT / CT? SPECT / CT Basic Principles. Advantages of SPECT. Advantages of CT. Dr John C. Dickson, Principal Physicist UCLH

DUE to beam polychromacity in CT and the energy dependence

Scatter Correction for Dual source Cone beam CT Using the Pre patient Grid. Yingxuan Chen. Graduate Program in Medical Physics Duke University

Fits you like no other

Diagnostic imaging techniques. Krasznai Zoltán. University of Debrecen Medical and Health Science Centre Department of Biophysics and Cell Biology

Introduction to XCT and its growth

Computed Tomography. Principles of Medical Imaging. Contents. Prof. Dr. Philippe Cattin. MIAC, University of Basel. Sep 26th/Oct 3rd, 2016

Constructing System Matrices for SPECT Simulations and Reconstructions

Optimisation of Toshiba Aquilion ONE Volume Imaging

Beam Attenuation Grid Based Scatter Correction Algorithm for. Cone Beam Volume CT

Acknowledgments and financial disclosure

Midterm Review. Yao Wang Polytechnic University, Brooklyn, NY 11201

Quality control phantoms and protocol for a tomography system

Emission Computed Tomography Notes

Improvement of Efficiency and Flexibility in Multi-slice Helical CT

Design and performance characteristics of a Cone Beam CT system for Leksell Gamma Knife Icon

Financial disclosure. Onboard imaging modality for IGRT

Corso di laurea in Fisica A.A Fisica Medica 5 SPECT, PET

Computed tomography - outline

Simulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images

ImPACT. Information Leaflet No. 1: CT Scanner Acceptance Testing

X-ray Computed Tomography: Principle and Recent Advancements

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

Spiral ASSR Std p = 1.0. Spiral EPBP Std. 256 slices (0/300) Kachelrieß et al., Med. Phys. 31(6): , 2004

Reconstruction in CT and relation to other imaging modalities

Fundamentals of CT imaging

Iterative and analytical reconstruction algorithms for varying-focal-length cone-beam

System Optimization and Patient Translational Motion Correction for Reduction of Artifacts in a Fan-Beam CT Scanner

GPU implementation for rapid iterative image reconstruction algorithm

CBCT Equivalent Source Generation Using HVL and Beam Profile Measurements. Johnny Little PSM - Medical Physics Graduate Student University of Arizona

Computed tomography (Item No.: P )

GE s Revolution CT MATLAB III: CT. Kathleen Chen March 20, 2018

Tomography. Forward projectionsp θ (r) are known as a Radon transform. Objective: reverse this process to form the original image

CT Basics Principles of Spiral CT Dose. Always Thinking Ahead.

Feldkamp-type image reconstruction from equiangular data

Introduction to Emission Tomography

SPECT QA and QC. Bruce McBride St. Vincent s Hospital Sydney.

Metal Artifact Reduction CT Techniques. Tobias Dietrich University Hospital Balgrist University of Zurich Switzerland

Fujifilm DR Solution. FDR AcSelerate. The new pinnacle in diagnostic imaging from Fujifilm ISS. CsI. Dynamic Visualization. Technology.

Empirical cupping correction: A first-order raw data precorrection for cone-beam computed tomography

An approximate cone beam reconstruction algorithm for gantry-tilted CT

Advanced Image Reconstruction Methods for Photoacoustic Tomography

CT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION

Continuation Format Page

Material for Chapter 6: Basic Principles of Tomography M I A Integral Equations in Visual Computing Material

Digital Laminography and Computed Tomography with 600 kv for Aerospace Applications

CIVA Computed Tomography Modeling

Investigation on reconstruction methods applied to 3D terahertz computed Tomography

S. Guru Prasad, Ph.D., DABR

Basics of treatment planning II

Scaling Calibration in the ATRACT Algorithm

Medical Image Processing: Image Reconstruction and 3D Renderings

COMPARATIVE STUDIES OF DIFFERENT SYSTEM MODELS FOR ITERATIVE CT IMAGE RECONSTRUCTION

Cardiac Dual Energy CT: Technique

Tomography at all Scales. Uccle, 7 April 2014

A prototype table-top inverse-geometry volumetric CT system

Photon counting spectral CT versus conventional CT: comparative evaluation for breast imaging application

Transcription:

Biomedical Imaging Computed Tomography Patrícia Figueiredo IST 2013-2014

Overview Basic principles X ray attenuation projection Slice selection and line projections Projection reconstruction Instrumentation Beam collimation Gas ionization chambers From the 1 st to the 4 th generation Spiral / Helical CT Multi-slice CT Image reconstruction The Radon transform: filtered backprojection Iterative reconstruction methods

Basic principles Image orientation: z x y

Basic principles X ray attenuation projection: Coronal image z x y

Basic principles X ray attenuation projection: Coronal image Computed tomography: (Trans)axial image x z y x z y

Basic principles X ray attenuation projection: { } ( z) I ( z) exp ( x z) I =, dx 0 µ { } ( y, z) I0 ( y, z) exp µ ( x, y z) I =, dx -The object consists of a distribution of attenuation coefficients. -The intensity of the detected X ray beam reflects the projection of the attenuation coefficients across the beam direction z µ 11 µ 12 µ 13 µ 14 µ 15 µ 16 I 0 (y,z) y µ 21 µ 22 µ 23 µ 24 µ 25 µ 26 µ 31 µ 32 µ 33 µ 34 µ 35 µ 36 µ 41 µ 42 µ 43 µ 44 µ 45 µ 46 µ 51 µ 52 µ 53 µ 54 µ 55 µ 56 µ 61 µ 62 µ 63 µ 64 µ 65 µ 66 I(y,z) x

Basic principles Line projections L 1 L{ θ 1 } L 2 L{ θ 2 } y p L { f ( x, y) } f x( l), y( l) L ( )dl z x

Basic principles Radon transform: Object space Projection space

Basic principles { } π Projection reconstruction: fˆ 1 ( r, θ ) = p ( xʹ ) h( xʹ ) dθ = R p ( xʹ ) 0 φ φ Object Image 1 ang. 2 ang. 4 ang. 8 ang. 16 ang. 32 ang.

Basic principles Radon transform:, p ( xʹ ) { f ( x, y) } R { f ( x y) } f ( x( l), y( l) )dl L φ R φ The sinogram Symmetry at π: p ( xʹ ) = p ( xʹ ) φ ± π φ Object space Projection space Periodicity at 2π: p ( xʹ ) = p ( xʹ ) φ + 2π φ f(x,y) R φ p φ (x ) φ φ 4 φ 3 θ φ 2 φ 0 φ 1 -r r x xʹ = rcos ( φ θ)

Image reconstruction Projection reconstruction 2D 3D Filtered backprojection (FB) Backprojection filtering (BF) True Three-Dimensional Reconstruction (TTR) Generalized TTR (GTTR) Parallel beam mode Fan beam mode Parallel beam mode Fan beam mode Parallel beam mode Cone beam mode Planar-Integral Projection Reconstruction (PPR) Iterative reconstruction Fourier reconstruction Algebraic Reconstruction Technique (ART) Maximum Likelihood (ML) or Expectation Maximization (EM) Direct Fourier Reconstruction (DFR) Direct Fourier Imaging (in MRI)

Image reconstruction Backprojection: effect of a finite number of projections Object Image 1 ang. 2 ang. 4 ang. 8 ang. 16 ang. 32 ang.

Image reconstruction Backprojection: effect of a finite number of projections streak artifacts

Image reconstruction Backprojection: coverage π ( r, φ) p ( xʹ ) h( x ) dφ fˆ ʹ = 0 φ y y x Symmetry at π: p ( xʹ ) p ( xʹ φ ± π = φ ) p ( xʹ ) = p ( xʹ ) Periodicity at 2π: y φ + 2π φ p θ (x ) L φ x r φ θ q x x f(x,y) Sampling requirements for φ: Coverage of a total scan angle of 180º, usually 360º to reduce partial volume effects.

Filtered back-projection π fˆ ʹ Filtering: simple backprojection (no filter) ( r, φ) pφ ( x ) dφ = 0

Filtered back-projection π fˆ ʹ ʹ Filtering: filtered backprojection ( r, φ) p ( x ) h( x ) dφ = 0 φ

Filtered back-projection Filtering: simple vs filtered backprojection π π fˆ ( r, φ) p ( xʹ ) dφ fˆ ( r, φ) p ( xʹ ) h( xʹ ) dφ = 0 φ = 0 φ

Filtered back-projection Filtering: effect of noise

Filtered back-projection CONTINUAR AQUI - MEBiom Filtering: effect of different filter functions

Image reconstruction Iterative reconstruction The ray-by-ray method: p m N = n = 1 W mn f n To estimate the value of the image cell f n from the projection data p m = to solve the inverse problem of M linear equations with N unknowns Iterative method: Mean square error (MSE) Expectation maximization (EM) Maximum likelihood (ML)

Image reconstruction Iterative reconstruction Jean-Baptiste Thibault et al., GE Medical Systems

Instrumentation

Instrumentation Beam collimators: 1 st collimator: width ~45º 2 nd collimator, perpendicular to 1 st : thickness ~1-5 mm Slice profile: -Δz/2 +Δz/2

Instrumentation 1 st generation systems Parallel beam 1 pencil beam source 1 detector translating together rotating together scanning time ~4-5 min

Instrumentation 2 nd generation systems Fan beam: equilinear geometry 1 thin fan beam source multiple detectors translating together rotating together But fewer rotations required: scanning time ~20 s

Instrumentation 3 rd generation systems Fan beam: equiangular geometry 1 wide (~30-45 ) fan beam source ~512-1000 detectors covering object no translations rotating together scanning time ~1-3 s Use pulsed X ray sources to take advantage of significant dead time

Instrumentation 4 th generation systems Fan beam: equiangular geometry 1 wide fan beam source complete ring of detectors source rotating detector ring stationary ~ scanning time No cumulative detector drift, But very expensive (BGO-PMT detectors)

Instrumentation 3 rd generation CT T = X ray tube D = Detectors X = X ray fan beam R = Rotation direction Typical parameters: kvp 140 kv E eff ma Pulse f 70-80 kev 70-320 ma 2-4 ms 0.6 1.6 mm Thickness 1-5 mm Matrix 512 1024 (Resolution~0.35 mm) Nb detectors~1000

Instrumentation X ray detectors: X rays must be converted into radiation accessible to human vision Type of X ray detectors: - itensifying screen + photographic emulsion - cassette of photostimulable phosphor + laser scanner - scintillation detectors - crystals: NaI (Tl), CsI(Tl), BGO coupled to a photo-multiplier tube (PMT) or a photodiode array (e.g. TFT) - gas ionizing detectors: - ionizing chamber, proportional counter, Geiger-Muller counter Main characteristics of X ray detectors : - Sensitivity - Efficiency - Linearity - Energy resolution - Dead time

Instrumentation Ionization chamber: X rays gas ionization electron-ion pairs electron/ions attracted to cathode/anode electric current amplifier Array of interlinked Xenon-filled (~1000) ionization chambers (Z Xe = 66, P = 20 atm) ~1mm X X - + Xe+ e- 10cm - spatial resolution ADC - simplicity - more compact X - efficiency - Role of antiscatter grid X

Instrumentation Scintillation detectors: X rays crystal excitation electron-hole pairs electron-hole pairs collected at p-n junctions electric current pre-amplifier X rays crystal excitation optical photons photocathode ionization photoelectrons electron multiplication electric current

Instrumentation Conventional CT configurations One slice at a time: time inefficient susceptible to artifacts due to motion between slices

Instrumentation Spiral / Helical CT Data are acquired as the patient table moves continuously along z, simultaneously with the source/detectors rotation, tracing out a spiral/helix for the X ray trajectory. Continuous scanning requirements: - source: high heat capacity and efficient cooling - detectors: high efficiency Only one projection is acquired exactly in the image plane. All other projections have to be interpolated.

Instrumentation Spiral / Helical CT Spiral pitch: p = d / S p d = table feed per rotation S = collimated slice thickness A. p<1: slice overlap higher dose B. p>2: slice gaps lower resolution blurring 1<p<2: typical values

Instrumentation Multislice CT An array of detectors is incorporated along z: Spiral pitch: p ms = d / S single d = table feed per rotation S single = single slice collimated beam width 4-slice: p ms <8 8-slice: p ms <16 16-slice : p ms <32

Instrumentation Multislice CT - Multislice helical scans produce a set of interleaved helices interpolation is (even) more difficult to visualize - Images are reconstructed at optimized oblique planes and are then filtered to produce axial images.

Instrumentation Multislice CT - Multislice helical scans produce a set of interleaved helices interpolation is (even) more difficult to visualize - Images are reconstructed at optimized oblique planes and are then filtered to produce axial images.

Instrumentation Multislice vs single-slice CT Advantages: - Same acquisition in shorter time - Larger volumes in same time - Thinner slices: better spatial resolution - Can get isotropic volumes Disadvantages: -Larger beam width (relative to slice) -Higher dose for same quality -Cone beam artefacts

Instrumentation

Image characteristics Estimated object function CT numbers (Hounsfield units, HU): CT ij µ ij µ H = µ H 2 O 2 O 1000

Image characteristics Dosimetric quantities CT Dose Index: D z is absorbed dose at position z T is slice thickness CTDI = 1 T + 7T 7T D z dz Effective dose:

Image characteristics Spatial resolution: X ray tube effective focal spotsize f (~0.6-1.6mm) Scanner (translation and) rotation steps (~512x512-1024x1024: ~0.35x0.35mm 2 ) Collimated single-slice thickness (~0.5-5mm) and table feed Signal to noise ratio (SNR): - X ray tube voltage (~140kV): kvp SNR - X ray tube current and exposure time (~2-4ms): ma s SNR - X ray filtration (effective energy ~70-80 kev): filtration SNR Contrast to noise ratio (CNR): - X ray energy: E I scatt /I primary CNR - Object size (thickness): thickness I scatt /I primary CNR - Field-of-view: FOV I scatt CNR - Artefacts!

Image characteristics Artefacts: - Streak artefacts: undersampling due to finite number of projections (interaction owith motion, beam hardening or scatter) increase nb rotation steps / decrease rotation step. - Ring artefacts: imbalances in detector sensitivity calibration is performed using spatially uniform test objects. - Beam hardening: results in more attenuation in the center of the object than around the edge, but algorithms assume monochromatic X ray beams and hence uniform attenuation coefficient X ray beam filtering; calibration; reconstruction corrections. - Partial volume effects: thick slices can include, and mix up, different tissue types decrease slice thickness (using multislice systems).

Image characteristics Streak artefacts: Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Ring artefacts: when a detector is out of calibration: Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Beam hardening effects: Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Beam hardening effects Monoenergetic X rays: Polichromatic X rays: Beam hardening: I i = I i 0 exp µ ij j I i0 = Ii0 i i0 µ ij 0 j Ema x ( E) I = I ( E) exp de µ ij = µ ij i 0i exp µ ij 0 j Ema x ( E) I = I ( E) ( E) de Non-linear relation between p and µ artefacts p i = ln I I i i0 I i = ln 0 E max I 0i ( E) exp µ ( E) 0 E max I 0i j ( ) E de ij de

Image characteristics Beam hardening effects - minimized by: Filtration: a flat piece of attenuating material is used to pre-harden the beam before it passes through the patient (so that it becomes closer to monochromatic). Monochromatic X ray Polychromatic X ray Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Beam hardening effects - minimized by: Calibration correction: using phantoms in a range of sizes. Uncalibrated Calibrated Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Beam hardening effects - minimized by: Reconstruction: an iterative correction algorithm may be applied when images of bony regions are being reconstructed. Uncorrected Corrected Uncorrected Corrected Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Partial volume effects Thick slice Thin slice Barrett J F, and Keat N Radiographics 2004;24:1679-1691

Image characteristics Partial volume effects: multi-slice vs single-slice CT

Image characteristics Scout image for CT Head CT

References Webb, Introduction to Biomedical Imaging, Wiley 2003. Cho, Foundations of Medical Imaging, Wiley 1993. Hendee, Medical Imaging Physics, Wiley 2002.