Burks Bradley (dryerheaven6)

Transcranial MRI-guided focused ultrasound (TcMRgFUS) thermal ablation is a noninvasive functional neurosurgery technique. Previous reports have shown that damage in the skull bone marrow can occur at high acoustic energies. While this damage is asymptomatic, it would be desirable to avoid it. Here we examined whether acoustic and thermal simulations can predict where the thermal lesions in the marrow occurred. Post-treatment imaging was obtained at 3-15 months after 40 clinical TcMRgFUS procedures, and bone marrow lesions were observed after 16 treatments. The presence of lesions was predicted by the acoustic energy with a threshold of 18.1-21.1 kJ (maximum acoustic energy used) and 97-112 kJ (total acoustic energy applied over the whole treatment). The size of the lesions was not always predicted by the acoustic energy used during treatment alone. In contrast, the locations, sizes, and shapes of the heated regions estimated by the acoustic and thermal simulations were qualitatively similar to those of the lesions. The lesions generally appeared in areas that were predicted to have high temperatures. While more work is needed to validate the temperature estimates in and around the skull, being able to predict the locations and onset for lesions in the bone marrow could allow for better distribution of the acoustic energy over the skull. Understanding skull absorption characteristics of TcMRgFUS could also be useful in optimizing transcranial focusing.Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.We introduce VA-Point-MVSNet, a novel visibility-aware point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. BGB-8035 purchase This point-based architecture allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts. Furthermore, our visibility-aware multi-view feature aggregation allows the network to aggregate