Robust image registration for functional magnetic resonance imaging of the brain

Hsu, Chia-Ching; Wu, Ming Ting; Lee, C.

doi:10.1007/bf02345141

Cited by 13 publications

(4 citation statements)

References 28 publications

(33 reference statements)

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“…This process removed hair and other extraneous portions of the 3D meshes that could interfere with the analysis. A shape-based Levenberg–Marquardt curve-fitting algorithm was then applied to automatically register the two manually aligned and trimmed 3D facial surfaces 17–19 . The purpose of the registration process was to establish a correspondence between the two 3D surfaces, ensuring that each point in the 3dMD-generated surface was paired with a corresponding point in the Vectra-generated surface.…”

Section: Methodsmentioning

confidence: 99%

Validation of the Vectra H1 portable three-dimensional photogrammetry system for facial imaging

Camison

Bykowski

Lee

et al. 2018

International Journal of Oral and Maxillofacial Surgery

123

141

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Three-dimensional (3D) surface imaging using stereophotogrammetry has become increasingly popular in clinical settings, offering advantages for surgical planning and outcome evaluation. The handheld Vectra H1 is a low-cost, highly portable system that offers several advantages over larger stationary cameras, but independent technical validation is currently lacking. In this study, 3D facial images of 26 adult participants were captured with the Vectra H1 system and the previously validated 3dMDface system. Using error magnitude statistics, 136 linear distances were compared between cameras. In addition, 3D facial surfaces from each system were registered, heat maps generated, and global root mean square (RMS) error calculated. The 136 distances were highly comparable across the two cameras, with an average technical error of measurement (TEM) value of 0.84mm (range 0.19-1.54mm). The average RMS value of the 26 surface-to-surface comparisons was 0.43mm (range 0.33-0.59mm). In each case, the vast majority of the facial surface differences were within a ±1mm threshold. Areas exceeding ±1mm were generally limited to facial regions containing hair or subject to facial microexpressions. These results indicate that 3D facial surface images acquired with the Vectra H1 system are sufficiently accurate for most clinical applications.

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Section: Methodsmentioning

confidence: 99%

Validation of the Vectra H1 portable three-dimensional photogrammetry system for facial imaging

Camison

Bykowski

Lee

et al. 2018

International Journal of Oral and Maxillofacial Surgery

123

141

View full text Add to dashboard Cite

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“…However, as already shown by Hsu et al (2001), registration results can be enhanced by other methods and modifications.…”

Section: Airmentioning

confidence: 85%

Image Registration of Sectioned Brains

Schmitt

Modersitzki

Heldmann

et al. 2006

Int J Comput Vision

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The physical (microtomy), optical (microscopy), and radiologic (tomography) sectioning of biological objects and their digitization lead to stacks of images. Due to the sectioning process and disturbances, movement of objects during imaging for example, adjacent images of the image stack are not optimally aligned to each other. Such mismatches have to be corrected automatically by suitable registration methods.Here, a whole brain of a Sprague Dawley rat was serially sectioned and stained followed by digitizing the 20 μm thin histologic sections. We describe how to prepare the images for subsequent automatic intensity based registration. Different registration schemes are presented and their results compared to each other from an anatomical and mathematical perspective. In the first part we concentrate on rigid and affine linear methods and deal only with linear mismatches of the images. Digitized images of stained histologic sections often exhibit inhomogenities of the gray level distribution coming from staining and/or sectioning variations. Therefore, a method is developed that is robust with respect to inhomogenities and artifacts. Furthermore we combined this approach by minimizing a suitable distance measure for shear and rotation mismatches of foreground objects after applying the principal axes transform. As a consequence of our investigations, we must emphasize that the combination of a robust principal axes based registration in combination with optimizing translation, rotation and shearing errors gives rise to the best reconstruction results from the mathematical and anatomical view point.Because the sectioning process introduces nonlinear deformations to the relative thin histologic sections as well, an elastic registration has to be applied to correct these deformations.In the second part of the study a detailed description of the advances of an elastic registration after affine linear registration of the rat brain is given. We found quantitative evidence that affine linear registration is a suitable starting point for the alignment of histologic sections but elastic registration must be performed to improve significantly the registration result. A strategy is presented that enables to register elastically the affine linear preregistered rat brain 6 Schmitt et al.sections and the first one hundred images of serial histologic sections through both occipital lobes of a human brain (6112 images). Additionally, we will describe how a parallel implementation of the elastic registration was realized. Finally, the computed force fields have been applied here for the first time to the morphometrized data of cells determined automatically by an image analytic framework.

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“…This weighting function was used to exclude or downweight the contribution of voxels with a low proportion of grey matter. In addition to its use in robust estimation, previous uses of the Tukey bisquare weight function have included edge-finding in noisy images, 66 image registration 67 and image segmentation. 68 Average grey matter MD was calculated for all 66 ROIs in the Desikan-Killiany atlas.…”

Section: Mri Acquisition and Processingmentioning

confidence: 99%

12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

Williams

Elman

McEvoy

et al. 2021

Brain Communications

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Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

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Robust image registration for functional magnetic resonance imaging of the brain

Cited by 13 publications

References 28 publications

Validation of the Vectra H1 portable three-dimensional photogrammetry system for facial imaging

Validation of the Vectra H1 portable three-dimensional photogrammetry system for facial imaging

Image Registration of Sectioned Brains

12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

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