Accelerating image registration of MRI by GPU-based parallel computation

Huang, Tseng-Chieng; Tang, Yunping; Ju, Shiun-Ying

doi:10.1016/j.mri.2011.02.027

Cited by 26 publications

(16 citation statements)

References 20 publications

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“…Other recent examples include GPU acceleration for registration of MRI volumes (Ha et al, 2010;Oh et al, 2011;Huang et al, 2011), diffeomorphic registration algorithms (Han et al, 2010;Huang et al, 2010), free-form deformation , motion tracking of video microscopy and mass-conserving image registration (Castillo et al, 2012). Lee et al (2012) described how to optimize GPU implementations that are compute-or memory-bound and apply it to image registration.…”

Section: Image Registrationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

347

198

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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.

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Section: Image Registrationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

347

198

View full text Add to dashboard Cite

show abstract

“…By using a GPU, it is not uncommon to achieve a speedup of a factor 4-20 compared to an optimized CPU implementation. As an example, Huang, Tang, and Ju (2011) accelerated image registration within the SPM software package and obtained a speedup of a factor 14.…”

Section: Spatial Normalizationmentioning

confidence: 99%

Harnessing graphics processing units for improved neuroimaging statistics

Eklund

Villani

LaConte

2013

Cogn Affect Behav Neurosci

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Harnessing GPUs for improved neuroimaging statistics 2 Abstract Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging (fMRI), voxel based morphometry (VBM), and diffusion tensor imaging (DTI). Non-parametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multi-subject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.

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“…Originally aimed for gaming, graphics processing units (GPUs) have evolved as accelerators into a gamut of compute-intensive scientific applications including bioinformatics and biomedical signal processing [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The GPU is what translates binary data from the central processing unit (CPU) and converts it into a picture.…”

Section: Introductionmentioning

confidence: 99%

An accelerated framework for the classification of biological targets from solid-state micropore data

Hanif

Hafeez

Suleman

et al. 2016

Computer Methods and Programs in Biomedicine

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Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis.

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Accelerating image registration of MRI by GPU-based parallel computation

Cited by 26 publications

References 20 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Harnessing graphics processing units for improved neuroimaging statistics

An accelerated framework for the classification of biological targets from solid-state micropore data

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