4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolleʼs duality-based algorithm Inverse Problems Inverse Problems 31 (2015) 115007 (21pp)
Objective:This meta-analysis aims to determine whether hemoglobin A1c (HbA1c) and perioperative hyperglycemia are associated with the increased risk of periprosthetic joint infection following total knee and hip arthroplasty.Methods:A systematic search is performed in Medline (1966–October 2017), PubMed (1966–October 2017), Embase (1980–October 2017), ScienceDirect (1985–October 2017), and the Cochrane Library. Only high-quality studies are selected. A meta-analysis is performed using Stata 11.0 software.Results:Six retrospective studies including 26,901 patients meet the inclusion criteria. The present meta-analysis indicates that there are significant differences between groups in terms of perioperative random blood glucose level [weighted mean difference (WMD) = 2.365, 95% confidence interval (95% CI): 1.802–2.929, P = .000] and perioperative hemoglobin A1c level (WMD = 3.266, 95% CI: 2.858–3.674, P = .000). No significant difference is found regarding body mass index (BMI) condition between groups (WMD = 0.027, 95% CI: -0.487 to 0.541, P = .919).Conclusion:The present meta-analysis shows that high HbA1c and perioperative hyperglycemia are associated with a higher risk of periprosthetic joint infection following total joint arthroplasty. Screening of HbA1c and perioperative blood glucose is therefore an effective method to predict deep infection.
This work develops a material reconstruction method for spectral CT, namely Total Image Constrained Material Reconstruction (TICMR), to maximize the utility of projection data in terms of both spectral information and high signal-to-noise ratio (SNR). This is motivated by the following fact: when viewed as a spectrally-integrated measurement, the projection data can be used to reconstruct a total image without spectral information, which however has a relatively high SNR; when viewed as a spectrally-resolved measurement, the projection data can be utilized to reconstruct the material composition, which however has a relatively low SNR. The material reconstruction synergizes material decomposition and image reconstruction, i.e., the direct reconstruction of material compositions instead of a two-step procedure that first reconstructs images and then decomposes images. For material reconstruction with high SNR, we propose TICMR with nonlocal total variation (NLTV) regularization. That is, first we reconstruct a total image using spectrally-integrated measurement without spectral binning, and build the NLTV weights from this image that characterize nonlocal image features; then the NLTV weights are incorporated into a NLTV-based iterative material reconstruction scheme using spectrally-binned projection data, so that these weights serve as a high-SNR reference to regularize material reconstruction. Note that the nonlocal property of NLTV is essential for material reconstruction, since material compositions may have significant local intensity variations although their structural information is often similar. In terms of solution algorithm, TICMR is formulated as an iterative reconstruction method with the NLTV regularization, in which the nonlocal divergence is utilized based on the adjoint relationship. The alternating direction method of multipliers is developed to solve this sparsity optimization problem. The proposed TICMR method was validated using both simulated and experimental data. In comparison with FBP and total-variation-based iterative method, TICMR had improved image quality, e.g., contrast-to-noise ratio and spatial resolution.
Different from conventional computed tomography (CT), spectral CT using energy-resolved photon-counting detectors is able to provide the unprecedented material compositions. However accurate spectral CT needs to account for the detector response function (DRF), which is often distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. The simulation results suggest that the proposed methods reconstructed more accurate material compositions than the conventional method without DRF. Moreover, the proposed linearized method with linear data fidelity from spectral resampling had improved reconstruction quality from the nonlinear method directly based on nonlinear data fidelity.
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