Titlebook: Marine Pelagic Cyanobacteria: Trichodesmium and other Diazotrophs; E. J. Carpenter,D. G. Capone,J. G. Rueter Book 1992 Springer Science+Bu

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K. G. Sellnerccuracy was evaluated using both synthetic and clinical data. The former comprised CBCT images, acquired using a deformable anthropomorphic brain phantom. The latter meanwhile, consisted of four 3D digital subtraction angiography (DSA) images of one patient, acquired before, during and after surgica

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Tracy A. Villarealme processing steps like whole brain tractography, atlas registration or clustering. We compare it to four state of the art bundle recognition methods on 20 different bundles in a total of 105 subjects from the Human Connectome Project. Results are anatomically convincing even for difficult tracts,

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Gary A. Borstad,Edward J. Carpenter,Jim F. R. Gower segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with

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Edward J. Carpenter,Douglas G. Caponesuch training data are often unavailable. This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of

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David M. Karl,Ricardo Letelier,Dale V. Hebel,David F. Bird,Christopher D. Winnopose a novel image reconstruction method for breast cancer DOT imaging. Our method is highlighted by two components: (i) a deep learning network with a novel hybrid loss, and (ii) a distribution transfer learning module. Our model is designed to focus on lesion specific information and small recons