Author

Figure 1 shows the results of ST analysis in a region with in-plane fiber crossings. We find that, in both MRI and histology, the percent of voxels with crossing fibers actually increases as we increase the resolution. The
middle and bottom rows highlight “single” fiber regions in red and “crossing” fibers as green, and qualitatively
show that many regions with a “single” fiber population at a coarse resolution exhibit crossing patterns when
processed at a finer resolution.

Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?

Sep. 1, 2016—Kurt G. Schilling, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, Adam W Anderson. “Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?” In Proceedings of the ISMRM Workshop on Diffusion MRI. Lisbon, Portugal, September 2016. Oral presentation. Full text: PDF Abstract It is now widely recognized that voxels with crossing...

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

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...

(A) A standard fully connected neural network where each layer's node is connected to each node from the
previous layer.7 (B) A convolutional neural network connecting a covolutional layer to a pooling layer.

Deep Learning for Brain Tumor Classification

Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...

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...

DICOM can be converted into research file formats viewed with SPM[7], MIPAV[8]. 3D Slicer[9]. MRIcron[10], MATLAB (Mathworks, Natick, MA)

Validating DICOM transcoding with an open multi-format resource

Oct. 31, 2014—Benjamin C. Yvernault, Charles D. Theobald, Jr., Jolinda C. Smith, Victoria Villalta, David H. Zald, Bennett A. Landman. “Validating DICOM transcoding with an open multi-format resource.” Neuroinformatics. 2014 Oct;12(4):615-7. † PMC4391369 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24777387 Abstract The Digital Imaging and Communications in Medicine (DICOM) standard has allowed wide-scale interoperability between medical imaging devices allowed for construction...