Diffusion Weighted MRI Category

Effects of b-Value and Number of Gradient Directions on Diffusion MRI Measures Obtained with Q-ball Imaging

Nov. 1, 2016—Kurt G. Schilling, Vishwesh Nath, Justin Blaber, Robert L. Harrigan, Zhaohua Ding, Adam W. Anderson, Bennett A Landman. “Effects of b-Value and Number of Gradient Directions on Diffusion MRI Measures Obtained with Q-ball Imaging” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract High-angular-resolution diffusion-weighted imaging (HARDI)...

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...

Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo

Aug. 31, 2016—Kurt Schilling, Yurui Gao, Iwona Stepniewska, Ann S. Choe, Bennett A. Landman, and Adam W Anderson. “Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo”. Magnetic Resonance Imaging. In press August 2015 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27587226 Abstract Purpose: Animal models are needed to better understand the relationship between diffusion...

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

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...

Intermediary results from image synthesis. The cited values for T2-relaxation in gray matter, white matter are approximately 100 and 80 ms respectively. The cited values for T1-relaxation in the gray matter and white matter are approximately 1350 and 800 ms respectively. These values appear consistent with the intermediary results of image synthesis.

One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.

Feb. 15, 2016—Andrew J. Plassard, Robert L. Harrigan, Allen T. Newton, Swati D. Rane, Srivatsan Pallavaram, Pierre F. D’Haese, Benoit M. Dawant, Daniel O. Claassen, Bennett A. Landman. “One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845960/ Abstract T1-weighted...

Qualitative results for the detection of malignant gliomas. Five representative examples are presented. For each example, the target volume, expert labeling, label fusion estimate, and the out-of-atlas likelihood are presented. The first four examples represent cases where the tumor region is correctly identified. The last example represents the outlier case [seen in Fig. Fig.2c]2c] in which the cancerous region was almost completely missed.

Out-of-Atlas Likelihood Estimation using Multi-Atlas Segmentation.

Apr. 15, 2013—Andrew J. Asman, Lola Chambless, Reid Thompson, and Bennett A. Landman. “Out-of-Atlas Likelihood Estimation using Multi-Atlas Segmentation.” Medical Physics. 2013 Apr;40(4) PMC23556928 † Full-Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625241/ Abstract Purpose: Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures...

Each of the 567 DTI datasets was characterized by a 112 element vector of stored outputs from the pipeline. PCA analysis was performed on the resulting data. (A) DTI dataset locations in the first two dimensions of the PCA analysis. Data is symbolized by study Roman numeral (Table 1). Single arrow points to a data quality outlier from study I; subject 3 in Figure 4 and in (B). A double headed arrow points to a cluster representing an isolated protocol sub-group from study VI. (B) FA maps from similar sagittal slice locations in two subjects from Study I. Subject 4 is the indicated outlier in (A) and subject 5 was selected from the center of the study I cluster seen in (A).

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.

Apr. 15, 2013—Carolyn B. Lauzon, Andrew J. Asman, Michael L. Esparza, Scott S. Burns, Qiuyun Fan, Yurui Gao, Adam W. Anderson, Nicole Davis, Laurie E. Cutting, Bennett A. Landman. “Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.” PLoS ONE. 2013 Apr 30;8(4) PMC23637895 † Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640065/ Abstract Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural...

Example qualitative results. Three representative slices from three different brains are presented for comparison. For each example, the target, truth labels, an example atlas, the label fusion estimate and the cancer likelihood estimate are presented. Note the high quality likelihood estimate in the first two examples. On the third example, the cancer is detected, but it is not as well localized as in the first two examples.

Out-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation

Dec. 31, 2012—Andrew J. Asman and Bennett A. Landman, “Out-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation”, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain† Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892947/ Abstract Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs...

Comparison of DTI and HARDI direction estimates. Mean unweighted scans (A, E) and DTI colormap images (B, F) are shown for two representative slices. Enlargements show estimated diffusion tensors (C, G) alongside CFARI directions (D, H) based on two repetitions of 30 directions at a b-value of 700 s/mm2

Resolution of Crossing Fibers with Constrained Compressed Sensing using Diffusion Tensor MRI

Feb. 1, 2012—Bennett A. Landman, John A Bogovic; Hanlin Wan; Fatma El Zahraa ElShahaby; Pierre-Louis Bazin, and Jerry L Prince. “Resolution of Crossing Fibers with Constrained Compressed Sensing using Diffusion Tensor MRI”, NeuroImage. 2012 Feb 1;59(3):2175-86. PMC22019877 Full text: https://www.ncbi.nlm.nih.gov/pubmed/22019877 Abstract Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions...

Flow chart of steps to create SIMEX Estimated Bias and True Bias maps. Variables refer to terms defined explicitly by equations in the Theory section.

Assessment of bias for MRI diffusion tensor imaging using SIMEX

Sep. 30, 2011—Carolyn B. Lauzon, Andrew Asman, Ciprian Crainiceanu, Brian Caffo, Bennett A. Landman. “Assessment of Bias for MRI Diffusion Tensor Imaging Using SIMEX”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Toronto, Canada, September 2011 (30% acceptance rate) NIHMS ID 301343 Full text: https://www.ncbi.nlm.nih.gov/pubmed/21995019 Abstract Diffusion Tensor Imaging (DTI) is a Magnetic Resonance...

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