Graphics Processing Unit-Based High Performance Computing in Radiation Therapy  book cover
1st Edition

Graphics Processing Unit-Based High Performance Computing in Radiation Therapy

Edited By

Xun Jia


Steve B. Jiang

ISBN 9781138894327
Published September 18, 2018 by CRC Press
414 Pages 9 Color & 105 B/W Illustrations

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Book Description

Use the GPU Successfully in Your Radiotherapy Practice

With its high processing power, cost-effectiveness, and easy deployment, access, and maintenance, the graphics processing unit (GPU) has increasingly been used to tackle problems in the medical physics field, ranging from computed tomography reconstruction to Monte Carlo radiation transport simulation. Graphics Processing Unit-Based High Performance Computing in Radiation Therapy collects state-of-the-art research on GPU computing and its applications to medical physics problems in radiation therapy.

Tackle Problems in Medical Imaging and Radiotherapy

The book first offers an introduction to the GPU technology and its current applications in radiotherapy. Most of the remaining chapters discuss a specific application of a GPU in a key radiotherapy problem. These chapters summarize advances and present technical details and insightful discussions on the use of GPU in addressing the problems. The book also examines two real systems developed with GPU as a core component to accomplish important clinical tasks in modern radiotherapy.

Translate Research Developments to Clinical Practice

Written by a team of international experts in radiation oncology, biomedical imaging, computing, and physics, this book gets clinical and research physicists, graduate students, and other scientists up to date on the latest in GPU computing for radiotherapy. It encourages you to bring this novel technology to routine clinical radiotherapy practice.

Table of Contents

Xun Jia and Steve B. Jiang

Digitally Reconstructed Radiographs
Michael M. Folkerts

Analytic Cone-Beam CT Reconstructions
Bongyong Song, Wooseok Nam, Justin C. Park, and William Y. Song

Iterative Cone-Beam CT Reconstruction on GPUs: A Computational Perspective
Wei Xu, Ziyi Zheng, Eric Papenhausen, Sungsoo Ha, and Klaus Mueller

4DCT and 4D Cone-Beam CT Reconstruction Using Temporal Regularizations
Hao Gao, Minghao Guo, Ruijiang Li, and Lei Xing

Multi-GPU Cone-Beam CT Reconstruction
Hao Yan and Xiaoyu Wang

Tumor Tracking and Real-Time Volumetric Imaging via One Cone-Beam CT Projection
Ruijiang Li and Steve B. Jiang

GPU Denoising for Computed Tomography
Andreas Maier and Rebecca Fahrig

GPU-Based Unimodal Deformable Image Registration in Radiation Therapy
Sanjiv S. Samant, Soyoung Lee, and Sonja S.A. Samant

Inter-Modality Deformable Registration
Yifei Lou and Allen Tannenbaum

CT-to-Cone-Beam CT Deformable Registration
Xin Zhen and Xuejun Gu

Reconstruction in Positron Emission Tomography
Franck P. Vidal and Jean-Marie Rocchisani

Implementation of Convolution Superposition Methods on a GPU
Todd R. McNutt and Robert A. Jacques

Photon and Proton Pencil Beam Dose Calculation
Xuejun Gu

Photon Monte Carlo Dose Calculation
Sami Hissoiny

Monte Carlo Dose Calculations for Proton Therapy
Xun Jia

Treatment Plan Optimization for Intensity-Modulated Radiation Therapy (IMRT)
Chunhua Men

Treatment Plan Optimization for Volumetric-Modulated Arc Therapy (VMAT)
Fei Peng, Zhen Tian, H. Edwin Romeijn, and Chunhua Men

Non-Voxel-Based Broad Beam Framework: A Summary
Weiguo Lu and Mingli Chen

Gamma Index Calculations
Xuejun Gu

SCORE System for Online Adaptive Radiotherapy
Zhen Tian, Quentin Gautier, Xuejun Gu, Chunhua Men, Fei Peng, Masoud Zarepisheh, Yan Jiang Graves, Andres Uribe-Sanchez, Xun Jia, and Steve B. Jiang

TARGET: A GPU-Based Patient-Specific Quality Assurance System for Radiation Therapy
Yan Jiang Graves, Michael M. Folkerts, Zhen Tian, Quentin Gautier, Xuejun Gu, Xun Jia, and Steve B. Jiang

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Dr. Xun Jia is an assistant professor and medical physicist in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. Dr. Jia has published over 60 peer-reviewed research articles and is a section editor of the Journal of Applied Clinical Medical Physics. He has conducted productive research on developing numerical algorithms and implementations for low-dose cone-beam CT reconstruction and Monte Carlo radiation transport simulation on the GPU platform. He earned his MS in mathematics and PhD in physics from the University of California, Los Angeles.

Dr. Steve B. Jiang is the Barbara Crittenden Professor in cancer research, vice chair of the Radiation Oncology Department, and director of the Medical Physics and Engineering Division at the University of Texas Southwestern Medical Center. He is a fellow of the Institute of Physics and the American Association of Physicists in Medicine, serves on the editorial board of Physics in Medicine and Biology, and is an associate editor of Medical Physics. He has published more than 130 peer-reviewed papers on various areas of cancer radiotherapy. He received his PhD in medical physics from the Medical College of Ohio.


"The use of graphics processing units (GPU) is of significant interest to the medical physics community, due to its potential for dramatic advances in parallel computing. This is driven by the relatively low costs, high processing power and the ease of installing these cards in the clinic…This book brings together various research groups to review the state-of-the-art for GPUs in radiotherapy. The book initially starts with an overview of the current state of GPU technology, demonstrating the increase in performance over recent years and how the GPU is controlled by the CPU. It then systematically approaches various uses for the GPUs, from increasing the speed of filtered back projection reconstruction for CBCT to dose calculation via Monte Carlo or collapsed cone superposition methods. The book concludes with a look at more quality assurance uses, such as a chapter dedicated to GPU enhanced calculations of the gamma index.
The editors achieve their aim of illustrating the vast utility for the GPUs. Each chapter of the book provides useful and generally easy to understand summaries of the main algorithms used in radiotherapy, such as the CBCT reconstruction algorithm, deformable registration algorithms and Monte Carlo methods. In all cases the authors demonstrate potential performance improvement, which in many cases leads one to wonder why these technologies aren’t already in use…Overall this a good book, which effectively demonstrates the uses and the associated performance benefits of using the GPU for radiotherapy, something that will no doubt become more important as we move into the era of adaptive radiotherapy where fast reconstruction, deformable registration and dose calculations will be essential."
—Dr David Nash, Queen Alexandra Hospital, in RAD Magazine, October 2016

"Graphics Processing Unit-Based High Performance Computing in Radiation Therapy provides comprehensive and timely information on state-of-the-art GPU techniques and is certainly a must-have book for medical physicists, engineers, and students engaged in research and development involving high performance computing."
Lei Xing, Jacob Haimson Professor of Medical Physics, Stanford University

"With adaptive radiation therapy and personalized treatments becoming more and more important in radiation therapy, improving computational efficiency is highly significant. This excellent book covers high-performance computing in a comprehensive manner. All aspects of cutting-edge computing in radiation therapy are discussed, namely, diagnostic imaging for treatment planning, on-line imaging, treatment plan optimization, as well as dose calculation for treatment planning. This book is a rich source of information for medical physicists interested in translational research aiming at improving clinical workflow and accuracy. At the same time, it is an excellent textbook for students in the field. Highly recommended!"
Harald Paganetti, PhD, FAAPM, Professor and Director of Physics Research, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School