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This paper aims to develop new and fast algorithms for recovering a sparse vector from a small number of measurements, which is a fundamental problem in the field of compressive sensing (CS). Currently, CS favors incoherent systems, in which any two measurements are as little correlated as possible. In reality, however, many problems are coherent, and conventional methods such as L1 minimization do not work well. Recently, the difference of the L1 and L2 norms, denoted as L1–L2, is shown to have superior performance over the classic L1 method, but it is computationally expensive. We derive an analytical solution for the proximal operator of the L1–L2 metric, and it makes some fast L1 solvers such as forward–backward splitting (FBS) and alternating direction method of multipliers (ADMM) applicable for L1–L2. We describe in details how to incorporate the proximal operator into FBS and ADMM and show that the resulting algorithms are convergent under mild conditions. Both algorithms are shown to be much more efficient than the original implementation of L1–L2 based on a difference-of-convex approach in the numerical experiments. [ABSTRACT FROM AUTHOR]

An important problem of neuroimaging data analysis for traumatic brain injury (TBI) is the task of coregistering MR volumes acquired using distinct sequences in the presence of widely variable pixel movements which are due to the presence and evolution of pathology. We are motivated by this problem to design a numerically stable registration algorithm which handles large deformations. To this end, we propose a new measure of probability distributions based on the Bhattacharyya distance, which is more stable than the widely used mutual information due to better behavior of the square root function than the logarithm at zero. Robustness is illustrated on two TBI patient datasets, each containing 12 MR modalities. We implement our method on graphics processing units (GPU) so as to meet the clinical requirement of time-efficient processing of TBI data. We find that 6 sare required to register a pair of volumes with matrix sizes of 256 \times 256 \times 60 on the GPU. In addition to exceptional time efficiency via its GPU implementation, this methodology provides a clinically informative method for the mapping and evaluation of anatomical changes in TBI. [ABSTRACT FROM PUBLISHER]

Objective: This pilot study was designed to investigate the antidepressant effects of dexmedetomidine (DEX), a selective α2-adrenergic receptor agonist, in patients with treatment-resistant depression (TRD). The antidepressant effects of dexmedetomidine was compared with ECT, which is widely used in clinical practice for treatment of patients with TRD., Methods: Seventy six patients with TRD were randomly assigned to receive 10 sessions of DEX infusions or electroconvulsive therapy (ECT) treatment. The primary outcome was the changes of depression severity determined by the improvement of 24-item Hamilton Depression Rating Scale (HDRS-24). The second outcomes were the rates of therapeutic response (reduction in HDRS-24 ≥ 50 %) and remission (HDRS-24 ≤ 10 and reduction in HDRS-24 ≥ 60 %) at posttreatment and after 3 months of follow-up visits., Results: We found that 10 sessions of DEX infusions or ECT treatments significantly improved HDRS-24 scores at posttreatment and after 3 months of follow-up visits compared with the baseline. In addition, there was no significant difference between DEX infusions and ECT treatments regarding HDRS-24 at these evaluating points. Furthermore, the depression severity dropped to mild after 2 sessions of DEX infusion. In contrast, at least 6 sessions of ECT treatment were needed to achieve a same level. Finally, the rates of therapeutic response and remission were comparable between the two groups. No serious adverse events were observed., Conclusions: Based on current published evidence, we conclude that DEX exhibits rapid and durable antidepressant properties similar to ECT but with fewer side effects., Competing Interests: Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2023. Published by Elsevier B.V.)

In this paper, we study the L 1 /L 2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L 1 /L 2 is better than the L 1 norm when approximating the L 0 norm to promote sparsity. Consequently, we postulate that applying L 1 /L 2 on the gradient is better than the classic total variation (the L 1 norm on the gradient) to enforce the sparsity of the image gradient. Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) under certain conditions. Experimentally, we demonstrate visible improvements of L 1 /L 2 over L 1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction. Finally, we reveal some empirical evidence on the superiority of L 1 /L 2 over L 1 when recovering piecewise constant signals from low-frequency measurements to shed light on future works.

We here report a double amplification strategy to construct a fluorescence anisotropy sensor for microRNA analysis in practical biological samples. In this strategy, one target can trigger cyclic catalyzed hairpin assembly (CHA), with streptavidin incorporated as an amplifier of molar mass to enhance the signal intensity. The proposed strategy has a good linearity in the range of 5 pM - 0.5 nM with a detection limit down to 2.3 pM. More importantly, by using fluorescence anisotropy as the signal output, the strategy can be used directly for detection of miRNA in practical samples without any tedious sample pretreatment, holding the practical value in real biological systems., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Coherent ptychographic imaging experiments often discard the majority of the flux from a light source to define the coherence of the illumination. Even when the coherent flux is sufficient, the stability required during an exposure is another important limiting factor. Partial coherence analysis can considerably reduce these limitations. A partially coherent illumination can often be written as the superposition of a single coherent illumination convolved with a separable translational kernel. This article proposes the gradient decomposition of the probe (GDP), a model that exploits translational kernel separability, coupling the variances of the kernel with the transverse coherence. An efficient first-order splitting algorithm (GDP-ADMM) for solving the proposed nonlinear optimization problem is described. Numerical experiments demonstrate the effectiveness of the proposed method with Gaussian and binary kernel functions in fly-scan measurements. Remarkably, GDP-ADMM using nanoprobes produces satisfactory results even when the ratio between the kernel width and the beam size is more than one, or when the distance between successive acquisitions is twice as large as the beam width.

Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.