Statistical Image Reconstruction Algorithms Using Paraboloidal Surrogates for PET Transmission Scans PDF Download
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Author: Hakan Erdoğan
Publisher:
ISBN:
Category :
Languages : en
Pages : 380
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Author: Hakan Erdoğan
Publisher:
ISBN:
Category :
Languages : en
Pages : 380
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Author: Mehmet Yavuz
Publisher:
ISBN:
Category :
Languages : en
Pages : 410
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Author: Matthew W. Jacobson
Publisher:
ISBN:
Category :
Languages : en
Pages : 316
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Author: Lin Fu
Publisher:
ISBN: 9781124025315
Category :
Languages : en
Pages :
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Positron emission tomography (PET) is a radionuclide imaging modality that plays important roles in visualizing, targeting, and quantifying functional processes in vivo. High-resolution and quantitative PET images are reconstructed by solving large-scale inverse problems with iterative methods that incorporate accurate physics and noise modeling of the imaging process. The computation demands of PET image reconstruction are rapidly increasing as higher-resolution detectors, larger imaging field-of-view, and dynamic or adaptive data acquisition modes are being adopted by modern PET scanners. The trend of the increase in the computation demands is even faster than Moore's law that describes the exponential growth in the number of transistors placed on an integrated circuit. In this project a residual correction mechanism is introduced to PET image reconstruction to create computationally efficient yet accurate tomographic reconstruction algorithms. By using residual correction, reconstruction methods are able to adopt a more simplified physical model for fast computation while retaining the accuracy of the final solution. Residual correction can accelerate existing image reconstruction packages. It allows iterative reconstruction with more accurate physical models which are currently impractical due to the high computation cost. Two illustrative applications of the residual correction approach are provided. One is image reconstruction with an object-dependent Monte Carlo based physics model. The other is image reconstruction using an ultra fast GPU-accelerated simplified geometric model.
Author: Sangtae Ahn
Publisher:
ISBN:
Category :
Languages : en
Pages : 378
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![Image Reconstruction Algorithms for Volume-imaging Pet Scanners [microform] PDF](https://books.google.com/books/content/images/frontcover/pCIsqAAACAAJ?fife=w80-h100)
Author: Kinahan, P. E. (Paul E.)
Publisher: Ann Arbor, Mich. : University Microfilms International
ISBN:
Category :
Languages : en
Pages : 472
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Author: Feng Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 338
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Author: Daniele Panetta
Publisher: CRC Press
ISBN: 100017588X
Category : Medical
Languages : en
Pages : 97
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Book Description
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. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 782
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Author: Alexander Mihlin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Positron emission tomography (PET) is an imaging modality that can detect a contrast agent that preferentially accumulates on or inside diseased cells with concentrations as low as pico-mol/L. Since diseases typically begin on molecular and cellular levels, PET's sensitivity to fine molecular changes makes it essential for detection, staging, and treatment of oncological, cardiovascular, and neurological diseases. Moreover, PET is indispensable for basic research of biological processes, and pharmaceutical development. This dissertation presents mathematical and algorithmic techniques for increasing the safety, ac- curacy, and affordability of PET imaging. Particularly, it presents the first ever maximum likelihood expectation maximization (MLEM) algorithm for photon attenuation correction from PET emission data alone. This is the only existing technique that guarantees monotonic increase of PET image likelihood with estimation iterations. Moreover, the dissertation presents advances in stochastic modeling, inverse problems with incomplete data, numerical optimization, parallel computing, and graphics processing unit (GPU)-based formulation of the method, that reduce image estimation du- ration from 5 days to under an hour, by accelerating the algorithm by over 200-fold compared with single CPU-based formulation, and reducing its memory usage by 5-fold. Furthermore, the disser- tation shows how these advances could benefit other algorithms that model the imaging system in PET, SPECT, and CT. Particularly, it shows how they can accelerate single scatter simulation (SSS) by over 100-fold compared with single CPU-based formulation, and increase PET's geometrical sys- tem matrix compression used in the image reconstruction process by over 800-fold compared with today's state of the art methodology. Finally, using the advances described above, the dissertation presents the first ever MLEM algorithm for joint correction of photon attenuation and tissue-scatter from PET emission data alone.
