A reproducible evaluation of ANTs similarity metric performance in brain image registration

Avants, Brian B.; Tustison, Nicholas J.; Song, Gang; Cook, Philip A.; Klein, Arno; Gee, James C.

doi:10.1016/j.neuroimage.2010.09.025

Cited by 3,721 publications

(3,376 citation statements)

References 84 publications

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“…The derived coregistration parameters were applied to the subject's CBF and CMRgl images. To allow across‐subject comparisons, each subject's structural MRI was diffeomorphically mapped to an unbiased structural MR image template using advanced normalization tools (ANTs) 44. The derived normalization parameters were applied to the subject's coregistered CBF and CMRgl images.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic utility of ASL‐MRI and FDG‐PET in the behavioral variant of FTD and AD

Tosun

Schuff

Rabinovici

et al. 2016

Ann Clin Transl Neurol

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ObjectiveTo compare the values of arterial spin‐labeled (ASL) MRI and fluorodeoxyglucose (FDG) PET in the diagnosis of behavioral variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD).MethodsPartial least squares logistic regression was used to identify voxels with diagnostic value in cerebral blood flow (CBF) and cerebral metabolic rate of glucose (CMRgl) maps from patients with bvFTD (n = 32) and AD (n = 28), who were compared with each other and with cognitively normal controls (CN, n = 15). Diagnostic values of these maps were compared with each other.ResultsRegions that differentiated each disorder from controls were similar for CBF and CMRgl. For differentiating AD from CN, the areas under the curve (AUC) for CBF (0.89) and CMRgl (0.91) were similar, with similar sensitivity (CBF: 86%, CMRgl: 78%) and specificity (CBF: 92%, CMRgl: 100%). Likewise, for differentiating bvFTD from CN performances of CBF (AUC = 0.83) and CMRgl (AUC = 0.85) were equivalent, with similar sensitivity (CBF: 78%, CMRgl: 79%) and specificity (CBF: 92%, CMRgl: 100%). In differentiating bvFTD from AD, classification was again similar for CBF (AUC = 0.87) and CMRgl (AUC = 0.79), as were sensitivity (CBF: 83%, CMRgl: 89%) and specificity (CBF: 93%, CMRgl: 78%). None of the differences in any performance measure were statistically significant.InterpretationASL‐MRI has similar diagnostic utility as FDG‐PET in the diagnosis of AD and bvFTD. Continued development of ASL‐MRI as a diagnostic tool for neurodegenerative dementias is warranted.

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Section: Methodsmentioning

confidence: 99%

Diagnostic utility of ASL‐MRI and FDG‐PET in the behavioral variant of FTD and AD

Tosun

Schuff

Rabinovici

et al. 2016

Ann Clin Transl Neurol

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“…The algorithm builds an average shaped brain within the diffeomorphic space (see Klein et al, 2010). Afterwards, all images were normalized to this template, using the antsIntroduction script (Avants et al, 2011). For both steps, cross-correlational similarity metric was used and a diffeomorphic image registration using Greedy SyN was applied.…”

Section: Mri Data Acquisitionmentioning

confidence: 99%

Hippocampal volume and functional connectivity changes during the female menstrual cycle

et al. 2015

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Hippocampal volume has been shown to be sensitive to variations in estrogen and progesterone levels across rodents' estrous cycle. However, little is known about the covariation of hormone levels and brain structure in the course of the human menstrual cycle. Here, we examine this covariation with a multi-method approach that includes several brain imaging methods and hormonal assessments. We acquired structural and functional scans from 21 naturally cycling women on four time points during their cycles (early follicular phase, late follicular phase, ovulation and luteal phase). Hormone blood concentrations and cognitive performance in different domains were assessed on each of the measurement occasions. Structural MRI images were processed by means of whole-brain voxel-based morphometry and FreeSurfer. With either method, bilateral increases in hippocampal volume were found in the late follicular phase relative to the early follicular phase. The gray matter probability in regions of hippocampal volume increase was associated with lower mean diffusivity in the same region. In addition, we observed higher functional connectivity between the hippocampi and the bilateral superior parietal lobe in the late follicular phase. We did not find any reliable cycle-related performance variations on the cognitive tasks. The present results show that hormonal fluctuations covary with hippocampal structure and function in the course of the human menstrual cycle.

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“…The pCT (moving image) was first rigidly registered to the CBCTs (fixed images) using mutual information 18 , 19 as a metric. Deformable image registration was then applied from the pCT to the CBCTs.…”

Section: Methodsmentioning

confidence: 99%

“…The similarity metric used in this stage was mutual information. For the second stage, the deformable registration was applied to the soft tissue only using the mean squared difference metric (19) . The deformed planning CT, which we refer to as the intensity‐corrected CBCT (cCBCT), is geometrically similar to the CBCT but with HUs that are well calibrated for WET calculations.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, two noncontour‐based metrics were used in this study. The first metric is the cross‐correlation (CC) defined as:

C C false(x false) = \frac{true \sum_{i} {false(false(I false(x_{i} false) - μ_{I false(x false)} false) false(J false(x_{i} false) - μ_{J false(x false)} false) false)}^{2}}{true \sum_{i} {false(I false(x_{i} false) - μ_{I false(x false)} false)}^{2} true \sum_{i} {false(J false(x_{i} false) - μ_{J false(x false)} false)}^{2}}

where I and J represent the moving and fixed images, x is at the center of

normalN \times normalN

square window (in two dimensions), μ is the mean value within the window centered at x , and

x_{i}

iterates through that window (19) . This method computes a single number after the multi‐iteration calculation and provides a measure of the similarity between the moving and fixed images.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

Wang

Yin

Zhang

et al. 2016

J Applied Clin Med Phys

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The aim of this work is to demonstrate the feasibility of using water‐equivalent thickness (WET) and virtual proton depth radiographs (PDRs) of intensity corrected cone‐beam computed tomography (CBCT) to detect anatomical change and patient setup error to trigger adaptive head and neck proton therapy. The planning CT (pCT) and linear accelerator (linac) equipped CBCTs acquired weekly during treatment of a head and neck patient were used in this study. Deformable image registration (DIR) was used to register each CBCT with the pCT and map Hounsfield units (HUs) from the planning CT (pCT) onto the daily CBCT. The deformed pCT is referred as the corrected CBCT (cCBCT). Two dimensional virtual lateral PDRs were generated using a ray‐tracing technique to project the cumulative WET from a virtual source through the cCBCT and the pCT onto a virtual plane. The PDRs were used to identify anatomic regions with large variations in the proton range between the cCBCT and pCT using a threshold of 3 mm relative difference of WET and 3 mm search radius criteria. The relationship between PDR differences and dose distribution is established. Due to weight change and tumor response during treatment, large variations in WETs were observed in the relative PDRs which corresponded spatially with an increase in the number of failing points within the GTV, especially in the pharynx area. Failing points were also evident near the posterior neck due to setup variations. Differences in PDRs correlated spatially to differences in the distal dose distribution in the beam's eye view. Virtual PDRs generated from volumetric data, such as pCTs or CBCTs, are potentially a useful quantitative tool in proton therapy. PDRs and WET analysis may be used to detect anatomical change from baseline during treatment and trigger further analysis in adaptive proton therapy.PACS number(s): 87.55‐x, 87.55.‐D, 87.57.Q‐

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A reproducible evaluation of ANTs similarity metric performance in brain image registration

Cited by 3,721 publications

References 84 publications

Diagnostic utility of ASL‐MRI and FDG‐PET in the behavioral variant of FTD and AD

Diagnostic utility of ASL‐MRI and FDG‐PET in the behavioral variant of FTD and AD

Hippocampal volume and functional connectivity changes during the female menstrual cycle

Quantitative assessment of anatomical change using a virtual proton depth radiograph for adaptive head and neck proton therapy

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