Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

Topic: Brain imaging, connectivity and assessment

Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.

Key findings

• We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version.

Study details

• Participants299
• Evidence strengthLow

Read the original

• DOI: 10.3389/fninf.2013.00050
• PubMed: 24474917
• Original source →

Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.

@article{shamonin2013-24474917,
  author  = {Shamonin, D. P. and Bron, E. E. and Lelieveldt, B. P. and Smits, M. and Klein, S. and Staring, M. and {Alzheimer's Disease Neuroimaging Initiative}},
  title   = {{Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease}},
  journal = {Frontiers in neuroinformatics},
  year    = {2013},
  doi     = {10.3389/fninf.2013.00050},
  pmid    = {24474917},
  url     = {https://doi.org/10.3389/fninf.2013.00050}
}

Related studies in Long-term cohort studies

• CSF α-synuclein improves diagnostic and prognostic performance of CSF tau and Aβ in Alzheimer's disease Acta neuropathologica · 2013-06-29 · usefulness score 61/100
• Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors Alzheimer's & dementia : the journal of the Alzheimer's Association · 2014-05-22 · usefulness score 56/100
• Operationalizing diagnostic criteria for Alzheimer's disease and other age-related cognitive impairment-Part 2 Alzheimer's & dementia : the journal of the Alzheimer's Association · 2011-01-01 · usefulness score 55/100
• Diagnostic accuracy of the MMSE in detecting probable and possible Alzheimer's disease in ethnically diverse highly educated individuals: an analysis of the NACC database The journals of gerontology. Series A, Biological sciences and medical sciences · 2012-03-06 · usefulness score 50/100
• Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI Psychiatry research · 2012-11-10 · usefulness score 65/100
• A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks Journal of medical systems · 2019-12-18 · usefulness score 64/100

Similar studies

• Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database 2011 · Brain scans and imaging
• Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images 2001 · Getting a diagnosis
• Image-driven population analysis through mixture modeling 2009 · Digital health tools
• Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI 2019 · Brain scans and imaging
• Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants 2009 · Brain scans and imaging
• SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry 2023 · Digital health tools

More on Long-term cohort studies

• Newest in this subject Ultra-sensitive Mapping of Cell-free DNA-derived Methylomes Reveals Brain-specific Epigenetic Alterations Implicated in Alzheimer’s Disease medRxiv · 2026-10-26 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Interpretable Phenotype Discovery Using Temporal Risk-Aware Clustering medRxiv · 2026-10-20 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Long-term cohort studies →