(Ebook PDF) 3D Image Reconstruction for CT and PET A Practical Guide with Python 1st edition by Daniele Panetta, Niccolo Camarlinghi 100017588X 9781000175882 full chapters

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ISBN-10 ‏ : ‎100017588X

ISBN-13 ‏ : ‎9781000175882

Author :   Daniele Panetta, Niccolo Camarlinghi  

This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. 

3D Image Reconstruction for CT and PET; A Practical Guide with Python 1st Table of contents:

CHAPTER 1: Preliminary notions
1.1 IMAGE RECONSTRUCTION FROM PROJECTION
1.1.1 Purpose of image reconstruction
1.1.2 Families of reconstruction methods
1.2 TOMOGRAPHIC IMAGING MODALITIES (RELEVANT FOR THIS BOOK)
1.2.1 Computed Tomography (CT)
1.2.2 Positron Emission Tomography (PET)
1.2.3 Single-photon Emission Computed Tomography (SPECT)
1.3 NOTIONS COMMON FOR ALL RECONSTRUCTION METHODS
1.3.1 Object function and image function
1.4 RELEVANT NOTIONS FOR ANALYTICAL RECONSTRUCTION METHODS
1.4.1 Line integral
1.4.2 Radon transform
1.4.3 Sinogram
1.4.4 Exact and approximated reconstruction
1.4.5 Central section theorem
1.5 RELEVANT NOTIONS FOR ITERATIVE RECONSTRUCTION METHODS
1.5.1 Object vector and data vector
1.5.2 System matrix
1.5.3 Discrete forward projection
1.5.4 Discrete back projection
CHAPTER 2: Short guide to Python samples
2.1 INSTALLATION
2.2 PROJECT ORGANIZATION
2.3 CODING CONVENTIONS
2.4 DEFINITION OF AN EXPERIMENTAL SETUP
2.4.1 Definition of a radiation detector
2.4.2 Definition of the image matrix
2.4.3 PET experimental setup
2.4.4 CT experimental setup
2.4.5 Parallel beam CT
2.4.6 Fan beam CT
2.4.7 Cone beam CT
2.4.8 Serialization/de-serialization of objects
2.4.9 Rendering an experimental setup
2.4.10 3D stack visualization
CHAPTER 3: Analytical reconstruction algorithms
3.1 2D RECONSTRUCTION IN PARALLEL BEAM GEOMETRY
3.1.1 Direct Fourier Reconstruction (DFR)
3.1.2 Filtered Backprojection (FBP)
3.1.2.1 Filtered Backprojection vs. Convolution Backprojection
3.1.2.2 Ramp filter and apodisation windows
3.1.2.3 The backprojection step
3.1.3 High-level Python implementation of the FBP
3.2 2D FBP IN FAN BEAM GEOMETRY
3.2.1 Rebinning
3.2.2 Full-scan (2p) FBP reconstruction in native fan beam geometry
3.2.3 Python implementation of the fan beam FBP
3.2.4 Data redundancy and short-scan reconstruction
3.3 RECONSTRUCTION OF FAN BEAM DATA FROM HELICAL SCANS
3.4 3D FBP IN CONE BEAM GEOMETRY
3.4.1 The Feldkamp-Davis-Kress (FDK) method
3.4.2 Python implementation of the FDK algorithm
3.5 OTHER FOURIER-BASED METHODS
3.5.1 Backprojection-Filtration (BPF)
3.6 SUGGESTED EXPERIMENTS
CHAPTER 4: Iterative reconstruction algorithms
4.1 SYSTEM MATRIX
4.2 IMPLEMENTATION OF THE FORWARD AND BACK PROJECTION
4.3 HADAMARD PRODUCT AND DIVISION
4.4 ALGEBRAIC RECONSTRUCTION TECHNIQUE (ART)
4.5 SIMULTANEOUS ITERATIVE RECONSTRUCTION TECHNIQUE (SIRT)
4.6 MAXIMUM-LIKELIHOOD EXPECTATION MAXIMIZATION (MLEM)
4.7 ORDERED-SUBSET EXPECTATION MAXIMIZATION (OSEM)
4.8 A STEP-BY-STEP EXAMPLE USING ARTIFICIAL NOISELESS PROJECTION DATA
4.9 A STEP-BY-STEP EXAMPLE USING ARTIFICIAL POISSON NOISE AFFECTED DATA
4.9.1 Study of convergence properties of the algorithms
4.10 SUGGESTED EXPERIMENTS
CHAPTER 5: Overview of methods for generation projection data
5.1 ANALYTICAL PROJECTION OF IDEAL ELLIPSOIDAL PHANTOMS
5.2 NUMERICAL PROJECTION OF VOXELIZED PHANTOMS
5.2.1 Siddon’s Algorithm

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