Q-space imaging (QSI), a diffusion MRI technique, can provide quantitative tissue architecture information at cellular dimensions not amenable by conventional diffusion MRI. By exploiting regularities in molecular diffusion barriers, QSI can estimate the average barrier spacing such as the mean axon diameter in white matter (WM). In this work, we performed ex vivo QSI on cervical spinal cord sections from healthy C57BL/6 mice at 400MHz using a custom-designed uniaxial 50T/m gradient probe delivering a 0.6 µm displacement resolution capable of measuring axon diameters on the scale of 1 µm. After generating QSI-derived axon diameter maps, diameters were calculated using histology from seven WM tracts (dorsal corticospinal, gracilis, cuneatus, rubrospinal, spinothalamic, reticulospinal, and vestibulospinal tracts) each with different axon diameters. We found QSI-derived diameters from regions drawn in the seven WM tracts (1.1 to 2.1 µm) to be highly correlated (r 2 = 0.95) with those calculated from histology (0.8 to 1.8 µm). The QSI-derived values overestimated those obtained by histology by approximately 20%, which is likely due to the presence of extracellular signal. Finally, simulations on images of synthetic circular axons and axons from histology suggest that QSI-derived diameters are informative despite diameter and axon shape variation and the presence of intra-cellular and extra-cellular signal. QSI may be able to quantify nondestructively changes in WM axon architecture due to pathology or injury at the cellular level.

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The origins of HBV are unclear. The new orthohepadnavirus species from Brazilian capuchin monkeys resembled HBV in elicited infection patterns and could infect human liver cells using the same receptor as HBV. Evolutionary analyses suggested that primate HBV-related viruses might have emerged in African ancestors of New World monkeys millions of years ago. HBV was associated with hominoid primates, including humans and apes, suggesting evolutionary origins of HBV before the formation of modern humans. HBV genotypes found in American natives were divergent from those found in American monkeys, and likely introduced along prehistoric human migration. Our results elucidate the evolutionary origins and dispersal of primate HBV, identify a new orthohepadnavirus reservoir, and enable new perspectives for animal models of hepatitis B.

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The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance.

In tomographic imagery, partial volume effects (PVEs) cause several artifacts in volume renditions. In X-ray computed tomography (CT), for example, soft-tissue-like pseudo structures appear in bone-to-air and bone-to-fat interfaces. Further, skin, which is identical to soft tissue in terms of CT number, obscures the rendition of the latter. The purpose of this paper is to demonstrate these phenomena and to provide effective solutions that yield significantly improved renditions. We introduce two methods that detect and classify voxels with PVE in X-ray CT. Further, a method is described to automatically peel skin so that PVE-resolved renditions of bone and soft tissue reveal considerably more detail. In the first method to address PVE, called the fraction measure (FM) method, the fraction of each tissue material in each voxel v is estimated by taking into account the intensities of the voxels neighboring v. The second method, called uncertainty principle (UP) method, is based on the following postulate (Saha and Udupa, 2001): In any acquired image, voxels with the highest uncertainty occur in the vicinity of object boundaries. The removal of skin is achieved by means of mathematical morphology. Volume renditions have been created before and after applying the methods for several patient CT datasets. A mathematical phantom experiment involving different levels of PVE has been conducted by adding different degrees of noise and blurring. A quantitative evaluation is done utilizing the mathematical phantom and clinical CT data wherein an operator carefully masked out voxels with PVE in the segmented images. All results have demonstrated the enhanced quality of display of bone and soft tissue after applying the proposed methods. The quantitative evaluations indicate that more than 98% of the voxels with PVE are removed by the two methods and the second method performs slightly better than the first. Further, skin peeling vividly reveals fine details in the soft tissue structures.

Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition.Approach: This work presents a dual convolutional neural network approach, one operating in the sinogram domain and one in the reconstructed image domain, that is specifically designed for the physics and setting of intraoperative CBCT to address the sources of beam hardening and sparse view sampling that contribute to metal artifacts. The networks were trained with images from cadaver scans with simulated metal hardware. Results:The trained networks were tested on images of cadavers with surgically implanted metal hardware, and performance was compared with a method operating in the image domain alone. While both methods removed most image artifacts, superior performance was observed for the dual-convolutional neural network (CNN) approach in which beam-hardening and view sampling effects were addressed in both the sinogram and image domain. Conclusion:The work demonstrates an innovative approach for eliminating metal and sparsity artifacts in CBCT using a dual-CNN framework which does not require a metal segmentation.