A review of GPU-based medical image reconstruction |
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| Institution: | 1. Département de physique, de génie physique et d''optique, Université Laval – 1045, avenue de la Médecine, Québec (Québec) G1V 0A6, Canada;2. Department of Radiation Oncology, University of Texas Southwestern Medical Center, 2280 Inwood Rd., MC 9303, Dallas, TX 75390, USA;3. Département de radio-oncologie, CHU de Québec - Université Laval, 11 Côte du Palais, Québec (Québec), G1R 2J6, Canada;1. Department of Medical Physics, Centre Oscar Lambret and University Lille 1, France;2. Academic Department of Radiation Oncology, Centre Oscar Lambret and University Lille 2, France;1. CELIA, Centre Laser Intenses et Applications, Université de Bordeaux-CNRS-CEA, F-33400 Talence, France;2. Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Center, F-33076 Bordeaux, France;1. Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway;2. Department of Physics and Technology, University of Bergen, Bergen, Norway;3. Department of Medical Physics, Aarhus University/Aarhus University Hospital, Aarhus, Denmark;1. Dpto. Fisiología Médica y Biofísica, Universidad de Sevilla, Seville, Spain;2. Instituto de Biomedicina de Sevilla, IBIS, Sevilla, Spain;3. Medical Radiation Physics, Stockholm University, Karolinska Institutet, Stockholm, Sweden;4. Dept. Radiation Oncology, Maastricht University Medical Center, The Netherlands;5. Hospital Universitario Virgen Macarena, Servicio de Radioterapia, Seville, Spain.;6. Hospital Universitario Virgen Macarena, Servicio de Medicina Nuclear, Seville, Spain. |
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| Abstract: | Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing. |
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| Keywords: | Tomographic reconstruction Medical imaging Graphics Processing Unit (GPU) |
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