A review of GPU-based medical image reconstruction

Philippe Després, Xun Jia

Research output: Contribution to journalReview articlepeer-review

57 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)76-92
Number of pages17
JournalPhysica Medica
StatePublished - Oct 2017


  • Graphics Processing Unit (GPU)
  • Medical imaging
  • Tomographic reconstruction

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • General Physics and Astronomy


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