Literature DB >> 23906631

Medical image processing on the GPU - past, present and future.

Anders Eklund1, Paul Dufort, Daniel Forsberg, Stephen M LaConte.   

Abstract

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.
Copyright © 2013 Elsevier B.V. All rights reserved.

Keywords:  CUDA; Graphics processing unit (GPU); Image processing; Image reconstruction; Medical imaging

Mesh:

Year:  2013        PMID: 23906631     DOI: 10.1016/j.media.2013.05.008

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  47 in total

1.  Toward real-time remote processing of laparoscopic video.

Authors:  Zahra Ronaghi; Edward B Duffy; David M Kwartowitz
Journal:  J Med Imaging (Bellingham)       Date:  2015-12-14

2.  Fast GPU-based computation of spatial multigrid multiframe LMEM for PET.

Authors:  Moulay Ali Nassiri; Jean-François Carrier; Philippe Després
Journal:  Med Biol Eng Comput       Date:  2015-04-08       Impact factor: 2.602

3.  FAST: framework for heterogeneous medical image computing and visualization.

Authors:  Erik Smistad; Mohammadmehdi Bozorgi; Frank Lindseth
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-02-17       Impact factor: 2.924

4.  Hydra image processor: 5-D GPU image analysis library with MATLAB and python wrappers.

Authors:  Eric Wait; Mark Winter; Andrew R Cohen
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

Review 5.  Survey of Non-Rigid Registration Tools in Medicine.

Authors:  András P Keszei; Benjamin Berkels; Thomas M Deserno
Journal:  J Digit Imaging       Date:  2017-02       Impact factor: 4.056

6.  Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.

Authors:  Nicole Labra; Pamela Guevara; Delphine Duclap; Josselin Houenou; Cyril Poupon; Jean-François Mangin; Miguel Figueroa
Journal:  Neuroinformatics       Date:  2017-01

7.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates.

Authors:  Anders Eklund; Thomas E Nichols; Hans Knutsson
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-28       Impact factor: 11.205

8.  PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

Authors:  Haixiao Du; Mingrui Xia; Kang Zhao; Xuhong Liao; Huazhong Yang; Yu Wang; Yong He
Journal:  Hum Brain Mapp       Date:  2018-02-07       Impact factor: 5.038

Review 9.  A survey of GPU-based acceleration techniques in MRI reconstructions.

Authors:  Haifeng Wang; Hanchuan Peng; Yuchou Chang; Dong Liang
Journal:  Quant Imaging Med Surg       Date:  2018-03

10.  Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

Authors:  Felipe Gutierrez-Barragan; Vamsi K Ithapu; Chris Hinrichs; Camille Maumet; Sterling C Johnson; Thomas E Nichols; Vikas Singh
Journal:  Neuroimage       Date:  2017-07-15       Impact factor: 6.556

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