X-ray imaging is of paramount importance for clinical and preclinical imaging but it is fundamentally restricted by the attenuation-based contrast mechanism, which has remained essentially the same since Roentgen's discovery a century ago. Recently, based on the Talbot effect, groundbreaking work was reported using 1D gratings for X-ray phase-contrast imaging with a hospital-grade X-ray tube instead of a synchrotron or microfocused source. In this paper, we report an extension using 2D gratings that reduces the imaging time and increases the accuracy and robustness of phase retrieval compared to current grating-based phase-contrast techniques. Feasibility is demonstrated via numerical simulation.
Computed tomographic colonography is a minimally invasive technique for detecting colorectal polyps and colon cancer. Most computed tomographic colonography protocols acquire both prone and supine images to improve the visualization of the lumen wall, reduce false-positives, and improve sensitivity. Comparisons between the prone and supine images can be improved by registration between the scans. In this paper, we propose registering colon lumens, segmented from prone and supine images, using feature matching of the colon centerline and nonrigid registration of the lumen shapes represented as distance functions. Experimental registration results (n = 21 subjects) show a correspondence accuracy of 13.77 +/- 6.20 mm for a range of polyp sizes. The overlap in the registered lumen segmentations show an average Jaccard similarity coefficient of 0.915 +/- 0.07.
1. Directional selectivity of crayfish sustaining fibers was examined with drifting sine wave gratings and with intracellular and extracellular recordings. Directionality was measured for variations in stimulus contrast, spatial frequency, and temporal frequency. 2. Sustaining fibers exhibit directional selectivity in the magnitude of the compound postsynaptic potential (PSP), the impulse frequency modulation response, and the mean firing rate. The mean synaptic potential is insensitive to direction. The directionality of the mean impulse rate appears to arise by rectification in the voltage-to-impulse transduction. 3. The preferred directions of three identified sustaining fibers are similar to those of head-down optomotor neurons to which these sustaining fibers project. 4. The modulatory response, elicited by gratings drifting in the preferred direction, increased linearly with contrast until saturation (typically at a contrast of 0.5), where maximum directional selectivity obtains. 5. The magnitude of the directional response is a band-pass function of spatial and temporal frequency and exhibits reversal of directionality (i.e., aliasing) at high spatial and temporal frequencies. The results imply a spatial sampling interval of 4.5 degrees and a temperature-dependent inhibitory delay of 40-90 ms. The PSP modulation response shares several features with that of neighboring tangential (Tan1) neurons. 6. A qualitative model is proposed for the transformation of a phase-sensitive, linear directional response to a phase-insensitive and nonlinear time-averaged response, based on the functional connections from Tan1 neurons to sustaining fibers to optomotor neurons. The model includes a threshold rectification, a synaptic band-pass filter, and differences in temporal phase among converging modulatory signals.
Image-analysis methods play an important role in helping detect brain changes in and diagnosis of Alzheimer's Disease (AD). In this paper, we propose an automatic unsupervised classification approach to distinguish brain magnetic resonance (MR) images of AD patients from those of elderly normal controls. The symmetric log-domain diffeomorphic demons algorithm, with the properties of symmetry and invertibility, is used to compute the pair-wise registration, whose deformation field is then used to calculate the Riemannian distance between them. The spectral embedding algorithm is performed based on the Riemannian distance matrix to project images onto a low-dimensional space where each image is represented as a point and its neighboring points correspond to images of high anatomical similarity. Finally, the quick shift clustering method is employed in the embedded space to partition the dataset into subgroups. The experiments using the proposed method show very good performance for clustering images into AD and normal aging, using the Clinical Dementia Rating (CDR) scale as a comparison.
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