Neuroimaging Category

$Results from the application analysis. Heritability estimates from the twin, nuclear, and grandnuclear families of 80 subjects overlayed on the ENIGMA-DTI fractional anisotropy template and skeleton. The color scale reflects the heritability estimate, with warmer colors suggesting greater heritability. The ground truth heritabilities were derived from the previous literature.$

The impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using SOLAR

Jun. 20, 2014—Mary Ellen Koran, Bo Li, Neda Jahanshad, Tricia A. Thornton-Wells, David C. Glahn, Paul M. Thompson, John Blangero, Thomas E. Nichols, Peter Kochunov, Bennett A. Landman, “The impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using SOLAR”. Journal of Medical Imaging, 2014 Jun 27;1(1):014005 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25558465 Abstract Imaging...

Representative one voxel t-values as data are randomly decimated. An rs-fMRI dataset is randomly diminished into N data size levels with M subsets in each level. Inference methods are applied to each subset to estimate voxel-wise t-maps. The highlighted point (left) indicates the rs-fMRI seed region.

Evaluation of Statistical Inference on Empirical Resting State fMRI.

May. 31, 2014—Xue Yang, Hakmook Kang, Allen T. Newton, Bennett A. Landman, “Evaluation of Statistical Inference on Empirical Resting State fMRI.” IEEE Transactions on Biomedical Engineering. IEEE Trans Biomed Eng. 2014 Apr;61(4):1091-9. PMC24658234† Full text: https://www.ncbi.nlm.nih.gov/pubmed/24658234 Abstract Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging...

Figure 4
Rendering of the manually labeled cortical regions ().

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

Feb. 1, 2014—Yurui Gao, Shweta P. Khare, Swetasudha Panda, Ann S Choe, Iwona Stepniewska, Xia Li, Zhoahua Ding, Adam Anderson, Bennett A. Landman, “A brain MRI atlas of the common squirrel monkey, Saimiri sciureus.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817811 Abstract The common squirrel...

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...

Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX

Mar. 2, 2013—Carolyn B. Lauzon, Ciprian Crainiceanu, Brian C. Caffo, Bennett A. Landman, “Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX”, Magnetic Resonance in Medicine. 2013 Mar 1;69(3):891-902. PMC22611000 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/22611000 Abstract: Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the...

Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.

Mar. 2, 2013—Carolyn B. Lauzon and Bennett A. Landman, “Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.” Magnetic Resonance Imaging. 2013 Mar 2. pii: S0730-725X(13) PMC23465764 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Correcting+Power+and+p-Value+Calculations+for+Bias+in+Diffusion+Tensor+Imaging. Abstract: Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and...

Overview of the integrated system. Multi-modal DICOM scans (upper left) are pushed to the XNAT/PACS server (upper right) via dcm4che tools. Verified DICOM scans are pulled and distributed to the cluster (bottom right) for further conversion, processing, and analysis by PyXNAT package scripts. The output results are pushed back for storage on XNAT. The online database REDCap (bottom left) provides/stores related information to/from XNAT. Black arrows indicate the directions of the data flow.

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis

Feb. 1, 2013—Yurui Gao, Scott S. Burns, Carolyn B. Lauzon, Andrew Fong, David A. Twillie, Michael Wirt, Marc A. Zola, Bret W. Logan, Adam W. Anderson, Bennett A. Landman. “Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full...

System for Integrated Neuroimaging Analysis and Processing of Structure.

Jan. 11, 2013—Bennett A. Landman, John A. Bogovic, Aaron Carass, Min Chen, Snehashis Roy, Navid Shiee, Zhen Yang, Bhaskar Kishore, Dzung Pham, Pierre-Louis Bazin, Susan M. Resnick, and Jerry L. Prince. “System for Integrated Neuroimaging Analysis and Processing of Structure.” Neuroinformatics. 2013 Jan;11(1):91-10 PMC22932976 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/22932976 Abstract: Mapping brain structure in relation to neurological development,...

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...

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