Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and other constraints, a piecewise-smooth X-ray computed tomography (CT) can be reconstructed from sparse-view projection data without introducing noticeable artifacts. However, due to the piecewise constant assumption for the image, a conventional TV minimization algorithm often suffers from over-smoothness on the edges of the resulting image. To mitigate this drawback, we present an adaptive-weighted TV (AwTV) minimization algorithm in this paper. The presented AwTV model is derived by considering the anisotropic edge property among neighboring image voxels, where the associated weights are expressed as an exponential function and can be adaptively adjusted by the local image-intensity gradient for the purpose of preserving the edge details. Inspired by the previously-reported TV-POCS (projection onto convex sets) implementation, a similar AwTV-POCS implementation was developed to minimize the AwTV subject to data and other constraints for the purpose of sparse-view low-dose CT image reconstruction. To evaluate the presented AwTV-POCS algorithm, both qualitative and quantitative studies were performed by computer simulations and phantom experiments. The results show that the presented AwTV-POCS algorithm can yield images with several noticeable gains, in terms of noise-resolution tradeoff plots and full width at half maximum values, as compared to the corresponding conventional TV-POCS algorithm.

Purpose: Low-dose x-ray computed tomography (CT) is clinically desired. Accurate noise modeling is a fundamental issue for low-dose CT image reconstruction via statistics-based sinogram restoration or statistical iterative image reconstruction. In this paper, the authors analyzed the statistical moments of low-dose CT data in the presence of electronic noise background. Methods: The authors first studied the statistical moment properties of detected signals in CT transmission domain, where the noise of detected signals is considered as quanta fluctuation upon electronic noise background. Then the authors derived, via the Taylor expansion, a new formula for the mean-variance relationship of the detected signals in CT sinogram domain, wherein the image formation becomes a linear operation between the sinogram data and the unknown image, rather than a nonlinear operation in the CT transmission domain. To get insight into the derived new formula by experiments, an anthropomorphic torso phantom was scanned repeatedly by a commercial CT scanner at five different mAs levels from 100 down to 17. Results:The results demonstrated that the electronic noise background is significant when low-mAs (or low-dose) scan is performed. Conclusions:The influence of the electronic noise background should be considered in low-dose CT imaging.

Dense surface registration, commonly used in computer science, could aid the biological sciences in accurate and comprehensive quantification of biological phenotypes. However, few toolboxes exist that are openly available, non-expert friendly, and validated in a way relevant to biologists. Here, we report a customizable toolbox for reproducible high-throughput dense phenotyping of 3D images, specifically geared towards biological use. Given a target image, a template is first oriented, repositioned, and scaled to the target during a scaled rigid registration step, then transformed further to fit the specific shape of the target using a non-rigid transformation. As validation, we use n = 41 3D facial images to demonstrate that the MeshMonk registration is accurate, with 1.26 mm average error, across 19 landmarks, between placements from manual observers and using the MeshMonk toolbox. We also report no variation in landmark position or centroid size significantly attributable to landmarking method used. Though validated using 19 landmarks, the MeshMonk toolbox produces a dense mesh of vertices across the entire surface, thus facilitating more comprehensive investigations of 3D shape variation. This expansion opens up exciting avenues of study in assessing biological shapes to better understand their phenotypic variation, genetic and developmental underpinnings, and evolutionary history.

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Knowledge of the plantar foot shape alteration under weight bearing can offer implications for the design and construction of a comfortable and functional foot support. The purpose of this study was to quantify the change in threedimensional foot shape under different weight-bearing conditions. The plantar foot shapes of 16 normal feet were collected by an impression casting method under three weight-bearing conditions: non-weight bearing, semi-weight bearing, and fullweight bearing. An optical digitizing system was used to capture the three-dimensional plantar surface shape of the foot cast. Measurements and comparisons from the digitized shapes were conducted for the whole foot and regions of the foot. The data showed that increased weight bearing significantly increased the contact area, foot length, foot width, and rearfoot width, while it decreased average height, arch height, and arch angle. Compared with the non-weight-bearing foot shape, the semi-weight-bearing condition would produce increases in the contact area of 35.1% ± 21.6 %, foot length of 2.7% ± 1.2%, foot width of 2.9% ± 2.4%, and rearfoot width of 5.9% ± 4.8%, and decreases in the arch height of 15.4% ± 7.8% and arch angle of 21.7% ± 17.2%. The full-weight-bearing condition would produce increases in the contact area of 60.4% ± 33.2%, foot length of 3.4% ± 1.3%, foot width of 6.0% ± 2.1%, and rearfoot width of 8.7% ± 4.9%, and decreases in the arch height of 20.0% ± 9.2% and arch angle of 41.2% ± 16.2%. The findings may be useful for considering the change of foot shape in the selection of shoe size and shoe or insole design.Abbreviations: FWB = full-weight bearing, ICC = intraclass correlation coefficient, MTH = metatarsal head, NWB = nonweight bearing, SWB = semi-weight bearing.

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