Neuroimaging Category

Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Aug. 28, 2017—Citation: Gray Matter Surface based Spatial Statistics (GS-BSS) in Diffusion Microstructure. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer. (2017). Accepted. Abstract Tract-based spatial statistics (TBSS) has proven to be...

Gray Matter Surface based Spatial Statistics in Neuroimaging Studies

Jun. 1, 2017—Citation: Gray Matter Surface based Spatial Statistics in Neuroimaging Studies. Authors: Prasanna Parvatheni, Baxter P. Rogers, Yuankai Huo, Kurt G. Schilling, Allison E. Hainline, Adam W. Anderson, Neil D. Woodward, Bennett A. Landman. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstract In this study, we propose gray matter surface based spatial statistics (GS-BSS)...

Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features

May. 15, 2017—Citation: Bermudez, C. et.al. Accurate Age Estimation in a Pediatric Population Using Deep Learning on T1‐weighted MRI Structural Features. Frontiers in Biomedical Imaging Science VI. May 2017. Abstract. Abstrract It is well known that there are structural changes that occur in the brain with age. However, there are insufficient imaging biomarkers that reliably describe structural...

$QBI glyphs in crossing fiber (left) and single fiber (right) regions, for various b-values, number of gradient directions, and maximum SH order fit. Glyphs are shown min-max normalized and are displayed on top of fractional anisotropy maps.$

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging

May. 5, 2017—Kurt G. Schilling, Vishwesh Nath, Justin Blaber Prasanna Parvathaneni, Adam W. Anderson, Bennett A. Landman. “Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging” Magnetic Resonance Imaging. Submitted November 2016. (In Press). Full Text: https://www.ncbi.nlm.nih.gov/pubmed/28438712 Abstract Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used...

Schematized figure of the tasks performed in the MR Scan.

Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks

Nov. 14, 2016—Victoria Villalta-Gil, Kendra E Hinton, Bennett A Landman, Benjamin C Yvernault, Scott F Perkins, Allison S Katsantonis, Courtney L Sellani, Benjamin B Lahey, David H Zald. “Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks.” NeuroImage. In Press November 2016 Open Data Resource: https://www.nitrc.org/projects/amygdalamapping Full text: NIHMSID Abstract...

Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation

Nov. 1, 2016—Andrew J. Plassard, Pierre F. D’Haese, Srivatsan Pallavaram, Allen T. Newton, Daniel O. Claassen, Benoit M. Dawant, Bennett A. Landman. “Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract The basal ganglia and limbic system comprise a relevant network for Parkinson’s...

Qualitative results comparing multi-atlas segmentation methods with semi-manual segmentation. The red contours represent the spatial location of the semi-manual segmentation. The white color indicates the negative error, in which the estimated segmentation is smaller than the semi-manual reference. The green and purple color outside the red contours indicates the positive error, in which the estimated segmentation is larger than reference.

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

Oct. 31, 2016—Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243 Abstract Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of...

The large-scale cross-sectional framework on 5111 multi-site MR 3D images.

Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI

Oct. 30, 2016—Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E. Cutting, Bennett A. Landman. “Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI”. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016. Oral Presentation. Full text: NIHMSID 826509 Abstract Understanding brain volumetry is essential to understand neurodevelopment...

$(A) Significant associations with plasma C22:0 and subsequent FA. (A) Red–yellow indicates higher plasma C22:0 associated with lower FA in white matter regions, while cyan–blue indicates higher plasma C22:0 associated with higher FA. Regional statistics are overlaid onto an FA image from a single subject; results are not corrected for multiple comparisons. (B) ACR FA by mean centered Cer 22:0. (B) Association between FA in ACR and Cer 22:0. The residual FA of ACR was calculated by regressing standardized FA on baseline age, sex, interval between blood draw and DTI, diabetes, BMI, scanner, and ratio of white matter lesions to intracranial volume. This is shown plotted against mean centered Cer 22:0 with a linear fit overlaid. Abbreviations: ACR, anterior corona radiata; ALIC, anterior limb of internal capsule; BMI, body mass index; CG, cingulum of cingulate gyrus; DTI, diffusion tensor imaging; FA, fractional anisotropy; Scc, splenium of corpus callosum; SLF, superior longitudinal fasciculus; SS, sagittal stratum. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)$

Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.

Jul. 31, 2016—Christopher E. Gonzalez, Vijay K. Venkatraman, Yang An, Bennett A. Landman, Christos Davatzikos, Veera Venkata Ratnam Bandaru, Norman J. Haughey, Luigi Ferruci, Michelle M. Mielke, Susan M. Resnick. “Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.” Neurobiology of Aging. July 2016. Volume 43, Pages 156–163 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/27255825 Abstract Sphingolipids...

This figure shows the failures for seven problematic volumes. Both whole brain segmentations and cortical surfaces on axial slices are provided. The areas in red rectangles show the global failures while the areas in blue rectangles show the local failures. MaCRUISE did not exhibit such failures in any images.

Combining Multi-atlas Segmentation with Brain Surface Estimation

Mar. 1, 2016—Yuankai Huo, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, Bennett A. Landman.2016 “Combining Multi-atlas Segmentation with Brain Surface Estimation,” In Proceedings of the SPIE Medical Imaging Conference 2016. Oral presentation. Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2506157 Pubmed Abstract Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential...

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