Medical-image Analysis and Statistical Interpretation (MASI) Lab

Simulations with traditional random raters. Coronal sections of the three-dimensional volume show the high resolution MRI image (A), manually drawn truth model (B), an example delineation from one random traditional rater (C), and the results of a STAPLE recombination of three label sets (D). STAPLER enables fusion of label sets when raters provide only partial datasets, but performance suffers with decreasing overlap (E). With training data (F), STAPLER improved the performance even with each rater labeling only a small portion of the dataset. Box plots in E and F show mean, quartiles, range up to 1.5σ, and outliers. The highlighted plot in E indicates the simulation for which STAPLER was equivalent to STAPLE--i.e., all raters provide a complete set of labels.

Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data

Mar. 1, 2010—A. Landman, H. Wan, J. Bogovic, and J. L. Prince. “Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2917119 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917119/ Abstract Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric...

Intra-voxel orientations estimated with in vivo data show patterns consistent with anatomy, which can be clearly appreciated in the region of the corpus callosum (cc) and internal capsule (ic).

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

Feb. 12, 2010—A. Landman, H. Wan, J. Bogovic, P.-L. Bazin, and J. L. Prince. “Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2854412 Full text Abstract Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture...

Simulations of free diffusion. Error between theoretical and simulated diffusion probability-density functions was minimized with 5–10 time-steps when the number of spins × the number of time-steps was held constant. Curves show mean error ± standard error for three diffusion times.

Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter

Jan. 31, 2010—. B. A. Landman, J. A.D. Farrell, S. A. Smith, D. Reich, P. Calabresi, and P. C.M. van Zijl, “Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter”, NMR in Biomed, Volume 23 Issue 2, Pages 152 – 162. (2010). PMC2838925 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2838925/ Abstract Which aspects of tissue microstructure...

Simulated impact of thresholds on bias in SVNF estimation. 5,000 Monte Carlo iterations were performed for each of 10 Rician distributed observations and for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). Second, 5,000 Monte Carlo draws of 1 Rayleigh distributed observation (i.e., an “outlier” with a Rician distribution with SNR=0:1) and 9 Rician distributed observations for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). The standard error on all reported estimates is less than 0.005 (for clarity, error bars are not shown).

Robust Estimation of Spatially Variable Noise Fields

Aug. 31, 2009—B. A. Landman, P-L Bazin, S. A. Smith, and J. L. Prince, “Robust Estimation of Spatially Variable Noise Fields”, Magnetic Resonance in Medicine, Aug;62(2):500-9. 2009 PMC2806192 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2806192/ Abstract Consideration of spatially variable noise fields is becoming increasing necessary in magnetic resonance imaging given recent innovations in artifact identification and statistically-driven image processing. Fast...

Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Feb. 28, 2009—B. A. Landman, P-L Bazin, and J. L. Prince, “Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging”, Magnetic Resonance Imaging, Volume 27, Issue 6, Pages 741-751. (2009) PMC2733233 Full text: https://www.ncbi.nlm.nih.gov/pubmed/19250784 Abstract Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically...

Interpolation of the genu of the Corpus Callosum (CC) and longitudinal fasciculus
(LF) near the lateral ventricle (V): region of interest (ROI) selected on an
orientation color coded anisotropy map (A,B). MD (C) and log-determinant (D) are
shown at the reconstructed resolution. Upsampled (4 × 4) and interpolated contrasts
are shown for linear (E-G), Log-Euclidean (H-J), and CLEAR (K-M) tensor interpolation.
While the linear interpolation has smooth FA and MD (E,F), it exhibits abrupt
changes in the determinant (G). The Log-Euclidean interpolation exhibits FA artifacts
in regions of high anisotropy and curvature (H). CLEAR avoids all these problems

Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation

Sep. 1, 2008—A. Landman, P-L. Bazin, and J. L. Prince. “Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=265 Abstract In diffusion tensor imaging, tensors represent the...

CFARI identified consistent intra-voxel structure in the crossing fibers between
the corpus callosum (CC, lower left, red) and internal capsule (IC, blue, lower right).
Conventional tensor modeling does not preserve the connectivity of the corpus callosum
to the lateral hemispheres

Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI

Sep. 1, 2008—A. Landman, J. Bogovic, and J. L. Prince. “Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=183 Abstract Diffusion tensor imaging (DTI) is widely used to...

Schematic of reliability estimation with experimental data (upper panel) and simulated data (lower panel).

Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts

Jul. 31, 2008—B. A. Landman, J. Farrell, H. Huang, J. Prince, and S. Mori. “Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts”, Magnetic Resonance Imaging. 26(6):790-800. July 2008 PMC2583784 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2583784/ Abstract Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in tissue micro-architecture in...

Slices at the level of C4 are shown for a control (left) and a MS patient with a LC lesion (center). Mean PDF's within the indicated regions are presented at right.

Robust Maximum Likelihood Estimation in Q-space MRI

May. 1, 2008—A. Landman, J. A.D. Farrell, S. A. Smith, P. A. Calabresi, P. C.M. van Zijl, and J. L. Prince. “Robust Maximum Likelihood Estimation in Q-space MRI”, In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging, Paris, France, May 2008 PMC2872926 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872926/ Abstract Q-space imaging is an emerging diffusion weighted MR imaging technique...

Orthogonal views of the Forceps Major delineated (a–b) manually and with our (c–d) automated processing. The semi-transparent cortical outer surface is also shown.

Automatically identifying white matter tracts using cortical labels

May. 1, 2008—A. Bogovic, A. Carass, J. Wan, B. A. Landman, and J. L. Prince. “Automatically identifying white matter tracts using cortical labels,” In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging, Paris, France, May 2008 PMC2812932 Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812932/ Abstract Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health...

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