An Immersive Virtual Reality Environment for Diagnostic Imaging

Alireza Sedghi, Lauren J O'Donnell, Tina Kapur, Erik Learned-Miller, Parvin Mousavi, and William M Wells. 4/2021. “Image Registration: Maximum Likelihood, Minimum Entropy and Deep Learning.” Med Image Anal, 69, Pp. 101939.Abstract

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.

Lipeng Ning, Filip Szczepankiewicz, Markus Nilsson, Yogesh Rathi, and Carl-Fredrik Westin. 1/2021. “Probing Tissue Microstructure by Diffusion Skewness Tensor Imaging.” Sci Rep, 11, 1, Pp. 135.Abstract

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

Angel Torrado-Carvajal, Nicola Toschi, Daniel S Albrecht, Ken Chang, Oluwaseun Akeju, Minhae Kim, Robert R Edwards, Yi Zhang, Jacob M Hooker, Andrea Duggento, Jayashree Kalpathy-Cramer, Vitaly Napadow, and Marco L Loggia. 4/2021. “Thalamic Neuroinflammation as a Reproducible and Discriminating Signature for Chronic Low Back Pain.” Pain, 162, 4, Pp. 1241-49.Abstract

Using positron emission tomography, we recently demonstrated elevated brain levels of the 18kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HC). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8±12.5 y/o; 18 HC, 48.2±12.8 y/o). We then trained random forest (RF) machine learning algorithms based on TSPO imaging features combining discovery and replication cohorts (totaling 25 cLBP, 42.4±13.2 y/o; 27 HC, 48.9±12.6 y/o), in order to explore whether image features other than the mean contain meaningful information that might contribute to the discrimination of cLBP patients and HC. Feature importance was ranked usind SHapley Additive exPlanations (SHAP) values, and the classification performance (in terms of AUC values) of classifiers containing only the mean, other features, or all features was compared using the DeLong test. Both region-of-interest (ROI) and voxelwise analyses replicated the original observation of thalamic TSPO signal elevations in cLBP patients compared to HC (p's<0.05). The RF-based analyses revealed that while the mean is a discriminating feature, other features demonstrate similar level of importance, including the maximum, kurtosis and entropy.Our observations suggest that thalamic neuroinflammatory signal is a reproducible and discriminating feature for cLBP, further supporting a role for glial activation in human chronic low back pain, and the exploration of neuroinflammation as a therapeutic target for chronic pain. This work further shows that TSPO signal contains a richness of information that the simple mean might fail to capture completely.

Filip Szczepankiewicz, Jens Sjölund, Erica Dall'Armellina, Sven Plein, Jürgen E Schneider, Irvin Teh, and Carl-Fredrik Westin. 4/2021. “Motion-Compensated Gradient Waveforms for Tensor-Valued Diffusion Encoding by Constrained Numerical Optimization.” Magn Reson Med, 85, 4, Pp. 2117-26.Abstract

PURPOSE: Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime. METHODS: Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo. RESULTS: The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source. CONCLUSION: Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.

Joseph M Gullett, Andrew O'Shea, Damon G Lamb, Eric C Porges, Deirdre M O'Shea, Ofer Pasternak, Ronald A Cohen, and Adam J Woods. 10/2020. “The Association of White Matter Free Water With Cognition in Older Adults.” Neuroimage, 219, Pp. 117040.Abstract

BACKGROUND: Extracellular free water within cerebral white matter tissue has been shown to increase with age and pathology, yet the cognitive consequences of free water in typical aging prior to the development of neurodegenerative disease remains unclear. Understanding the contribution of free water to cognitive function in older adults may provide important insight into the neural mechanisms of the cognitive aging process. METHODS: A diffusion-weighted MRI measure of extracellular free water as well as a commonly used diffusion MRI metric (fractional anisotropy) along nine bilateral white matter pathways were examined for their relationship with cognitive function assessed by the NIH Toolbox Cognitive Battery in 47 older adults (mean age = 74.4 years, SD = 5.4 years, range = 65-85 years). Probabilistic tractography at the 99th percentile level of probability (Tracts Constrained by Underlying Anatomy; TRACULA) was utilized to produce the pathways on which microstructural characteristics were overlaid and examined for their contribution to cognitive function independent of age, education, and gender. RESULTS: When examining the 99th percentile probability core white matter pathway derived from TRACULA, poorer fluid cognitive ability was related to higher mean free water values across the angular and cingulum bundles of the cingulate gyrus, as well as the corticospinal tract and the superior longitudinal fasciculus. There was no relationship between cognition and mean FA or free water-adjusted FA across the 99th percentile core white matter pathway. Crystallized cognitive ability was not associated with any of the diffusion measures. When examining cognitive domains comprising the NIH Toolbox Fluid Cognition index relationships with these white matter pathways, mean free water demonstrated strong hemispheric and functional specificity for cognitive performance, whereas mean FA was not related to age or cognition across the 99th percentile pathway. CONCLUSIONS: Extracellular free water within white matter appears to increase with normal aging, and higher values are associated with significantly lower fluid but not crystallized cognitive functions. When using TRACULA to estimate the core of a white matter pathway, a higher degree of free water appears to be highly specific to the pathways associated with memory, working memory, and speeded decision-making performance, whereas no such relationship existed with FA. These data suggest that free water may play an important role in the cognitive aging process, and may serve as a stronger and more specific indicator of early cognitive decline than traditional diffusion MRI measures, such as FA.

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An Immersive Virtual Reality Environment for Diagnostic Imaging

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