Notes

Related diagnosis in various neonatal histidine-tryptophan-ketoglutarate medication dosage management.
Metal artifacts commonly appear in computed tomography (CT) images of the patient body with metal implants and can affect disease diagnosis. Known deep learning and traditional metal trace restoring methods did not effectively restore details and sinogram consistency information in X-ray CT sinograms, hence often causing considerable secondary artifacts in CT images. In this paper, we propose a new cross-domain metal trace restoring network which promotes sinogram consistency while reducing metal artifacts and recovering tissue details in CT images. Our new approach includes a cross-domain procedure that ensures information exchange between the image domain and the sinogram domain in order to help them promote and complement each other. Under this cross-domain structure, we develop a hierarchical analytic network (HAN) to recover fine details of metal trace, and utilize the perceptual loss to guide HAN to concentrate on the absorption of sinogram consistency information of metal trace. To allow our entire cross-domain network to be trained end-to-end efficiently and reduce the graphic memory usage and time cost, we propose effective and differentiable forward projection (FP) and filtered back-projection (FBP) layers based on FP and FBP algorithms. We use both simulated and clinical datasets in three different clinical scenarios to evaluate our proposed network's practicality and universality. Both quantitative and qualitative evaluation results show that our new network outperforms state-of-the-art metal artifact reduction methods. In addition, the elapsed time analysis shows that our proposed method meets the clinical time requirement.We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. AZD-5153 6-hydroxy-2-naphthoic Some of the most popular segmentation methods (e.g. based on convolutional neural networks or random forest classifiers) incorporate additional post-processing steps to ensure that the resulting masks fulfill expected connectivity constraints. These methods operate under the hypothesis that contiguous pixels with similar aspect should belong to the same class. Even if valid in general, this assumption does not consider more complex priors like topological restrictions or convexity, which cannot be easily incorporated into these methods. Post-DAE leverages the latest developments in manifold learning via denoising autoencoders. First, we learn a compact and non-linear embedding that represents the space of anatomically plausible segmentations. Then, given a segmentation mask obtained with an arbitrary method, we reconstruct its anatomically plausible version by projecting it onto the learnt manifold. The proposed method is trained using unpaired segmentation mask, what makes it independent of intensity information and image modality. We performed experiments in binary and multi-label segmentation of chest X-ray and cardiac magnetic resonance images. We show how erroneous and noisy segmentation masks can be improved using Post-DAE. With almost no additional computation cost, our method brings erroneous segmentations back to a feasible space.Compactness, among several others, is one unique and very attractive feature of a scanning fiber-optic two-photon endomicroscope. To increase the scanning area and the total number of resolvable pixels (i.e., the imaging throughput), it typically requires a longer cantilever which, however, leads to a much undesired, reduced scanning speed (and thus imaging frame rate). Herein we introduce a new design strategy for a fiber-optic scanning endomicroscope, where the overall numerical aperture (NA) or beam focusing power is distributed over two stages 1) a mode-field focuser engineered at the tip of a double-clad fiber (DCF) cantilever to pre-amplify the single-mode core NA, and 2) a micro objective of a lower magnification (i.e., ∼ 2× in this design) to achieve final tight beam focusing. This new design enables either an ~9-fold increase in imaging area (throughput) or an ~3-fold improvement in imaging frame rate when compared to traditional fiber-optic endomicroscope designs. The performance of an as-designed endomicroscope of an enhanced throughput-speed product was demonstrated by two representative applications (1) high-resolution imaging of an internal organ (i.e., mouse kidney) in vivo over a large field of view without using any fluorescent contrast agents, and (2) real-time neural imaging by visualizing dendritic calcium dynamics in vivo with sub-second temporal resolution in GCaMP6m-expressing mouse brain. This cascaded NA amplification strategy is universal and can be readily adapted to other types of fiber-optic scanners in compact linear or nonlinear endomicroscopes.Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME.
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