Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans

Ardekani, Babak A.; Guckemus, Stephen; Bachman, Alvin H.; Hoptman, Matthew J.; Wojtaszek, M.; Nierenberg, Jay

doi:10.1016/j.jneumeth.2004.07.014

Cited by 225 publications

(185 citation statements)

References 28 publications

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“…This registration quality may sound unexpectedly high, compared to previous reports based on cortical registration (Salmond et al, 2002;Thompson and Toga, 1996;Van Essen and Drury, 1997). However, this result is in line with previous registration studies measuring deep brain structures (Ardekani et al, 2005;Grachev et al, 1999).…”

Section: Wmpm-based Quantification and Registration Qualitysupporting

confidence: 87%

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

Mori¹,

Oishi²,

Jiang³

et al. 2008

NeuroImage

1,589

1,513

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Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely-used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and nonlinear transformation was measured, and automated template-based white matter parcellation was tested. The results showed high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

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Section: Wmpm-based Quantification and Registration Qualitysupporting

confidence: 87%

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

Mori¹,

Oishi²,

Jiang³

et al. 2008

NeuroImage

1,589

1,513

View full text Add to dashboard Cite

show abstract

“…Voxel-wise analysis-The calculated multi-slice T 2 maps (48 slices) for each mouse (n = 12) were spatially registered to a common template (determined from the entire dataset average) using a non-linear image registration algorithm [4]. Clusters of voxels from the Ts65Dn or Ts1Cje experimental groups exhibiting significant differences from the 2N group were determined as follows: group mean registered images from each experimental group were compared to the 2N group by means of a voxel-wise analysis using two-tailed t tests (p < 0.05).…”

Section: Post-acquisition Image Analysismentioning

confidence: 99%

In vivo MRI identifies cholinergic circuitry deficits in a Down syndrome model

Chen

Dyakin

Branch

et al. 2009

Neurobiology of Aging

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In vivo quantitative magnetic resonance imaging (MRI) was employed to detect brain pathology and map its distribution within control, disomic mice (2N) and in Ts65Dn and Ts1Cje trisomy mice with features of human Down syndrome (DS). In Ts65Dn, but not Ts1Cje mice, transverse proton spinspin (T 2 ) relaxation time was selectively reduced in the medial septal nucleus (MSN) and in brain regions that receive cholinergic innervation from the MSN, including the hippocampus, cingulate cortex, and retrosplenial cortex. Basal forebrain cholinergic neurons (BFCNs) in the MSN, identified by choline acetyltransferase (ChAT) and nerve growth factor receptors p75 NTR and TrkA immunolabeling were reduced in Ts65Dn brains and in situ acetylcholinesterase (AChE) activity was depleted distally along projecting cholinergic fibers, and selectively on pre-and post-synaptic profiles in these target areas. T 2 effects were negligible in Ts1Cje mice that are diploid for APP and lack BFCN neuropathology, consistent with the suspected relationship of this pathology to increased App dosage. These results establish the utility of quantitative MRI in vivo for identifying Alzheimer's disease-relevant cholinergic changes in animal models of DS and the selective vulnerability of cholinergic neuron subpopulations.

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“…& SADG: Convex combination of the sum of absolute differences (SAD) and sum of gradient inner products (GRAD) & SART: The inter-subject registration algorithm [52] uses the similarity metric…”

Section: Riumentioning

confidence: 99%

“…It was developed by Babak Ardekani at the Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, USA, and it is released under a BFree For Non-Commercial Use Only^license. It provides a nonparametric curved image registration method for inter-subject 3D MRI brain image registration [52]. and handles 2D images.…”

Section: Art 3dwarpermentioning

confidence: 99%

Survey of Non-Rigid Registration Tools in Medicine

2016

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We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using nonsystematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

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Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans

Cited by 225 publications

References 28 publications

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

In vivo MRI identifies cholinergic circuitry deficits in a Down syndrome model

Survey of Non-Rigid Registration Tools in Medicine

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