title: Medical image processing on the GPU - past, present and future.
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.
authors.affiliation: Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA. Electronic address: andek034@gmail.com.
authors.forename: Anders
authors.initials: A
authors.lastname: Eklund
authors.forename: Paul
authors.initials: P
authors.lastname: Dufort
authors.forename: Daniel
authors.initials: D
authors.lastname: Forsberg
authors.forename: Stephen M
authors.initials: SM
authors.lastname: LaConte
mesh_terms: D000465:Algorithms; D003196:Computer Graphics / Q000295:instrumentation* / Q000639:trends*; D004867:Equipment Design; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*
keywords: CUDA; Graphics processing unit (GPU); Image processing; Image reconstruction; Medical imaging