Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.

High dimensional optical data - varifocal multiview imaging, compression and evaluation.

Varifocal multiview (VFMV) is an emerging high-dimensional optical data in computational imaging and displays. It describes scenes in angular, spatial, and focal dimensions, whose complex imaging conditions involve dense viewpoints, high spatial resolutions, and variable focal planes, resulting in difficulties in data compression. In this paper, we propose an efficient VFMV compression scheme based on view mountain-shape rearrangement (VMSR) and all-directional prediction structure (ADPS). The VMSR rearranges the irregular VFMV to form a new regular VFMV with mountain-shape focusing distributions. This special rearrangement features prominently in enhancing inter-view correlations by smoothing focusing status changes and moderating view displacements. Then, the ADPS efficiently compresses the rearranged VFMV by exploiting the enhanced correlations. It conducts row-wise hierarchy divisions and creates prediction dependencies among views. The closest adjacent views from all directions serve as reference frames to improve the prediction efficiency. Extensive experiments demonstrate the proposed scheme outperforms comparison schemes by quantitative, qualitative, complexity, and forgery protection evaluations. As high as 3.17 dB gains of peak signal-to-noise ratio (PSNR) and 61.1% bitrate savings can be obtained, achieving the state-of-the-art compression performance. VFMV is also validated could serve as a novel secure imaging format protecting optical data against the forgery of large models.

Virtual Training Is More Cost-Effective Than In-Person Training for Preparing Staff to Implement Contingency Management.

Behavior therapy implementation relies in part on training to foster counselor skills in preparation for delivery with fidelity. Amidst Covid-19, the professional education arena witnessed a rapid shift from in-person to virtual training, yet these modalities' relative utility and expense is unknown. In the context of a cluster-randomized hybrid type 3 trial of contingency management (CM) implementation in opioid treatment programs (OTPs), a multi-cohort design presented rare opportunity to compare cost-effectiveness of virtual vs. in-person training. An initial counselor cohort (n = 26) from eight OTPs attended in-person training, and a subsequent cohort (n = 31) from ten OTPs attended virtual training. Common training elements were the facilitator, learning objectives, and educational strategies/activities. All clinicians submitted a post-training role-play, independently scored with a validated fidelity instrument for which performances were compared against benchmarks representing initial readiness and advanced proficiency. To examine the utility and expense of in-person and virtual trainings, cohort-specific rates for benchmark attainment were computed, and per-clinician expenses were estimated. Adjusted between-cohort differences were estimated via ordinary least squares, and an incremental cost effectiveness ratio (ICER) was calculated. Readiness and proficiency benchmarks were attained at rates 12-14% higher among clinicians attending virtual training, for which aggregated costs indicated a $399 per-clinician savings relative to in-person training. Accordingly, the ICER identified virtual training as the dominant strategy, reflecting greater cost-effectiveness across willingness-to-pay values. Study findings document greater utility, lesser expense, and cost-effectiveness of virtual training, which may inform post-pandemic dissemination of CM and other therapies.

Medical thoracoscopy: rigid thoracoscopy or flexi-rigid pleuroscopy?

PURPOSE OF REVIEW: In managing pleural diseases, medical thoracoscopy is often performed as a diagnostic and/or therapeutic procedure, particularly in undiagnosed pleural effusions. Flexi-rigid pleuroscopes are now widely available as an alternative to conventional rigid thoracoscopes. There is an ongoing debate on which is the better instrument. This review analyses the current literature that compared rigid and flexi-rigid approaches, and outlines the medical advances that may influence the future role of thoracoscopy. RECENT FINDINGS: Both rigid and flexi-rigid thoracoscopies are well tolerated. Although biopsies are smaller with flexi-rigid biopsy forceps, two small randomized trials reported similar diagnostic yield using either instrument. No studies have specifically examined patient comfort or the outcome of talc poudrage using the two devices. New techniques (e.g. insulated-tip knife and cryobiopsy) have been used as adjuncts with flexi-rigid pleuroscopy to overcome the difficulties in sampling thickened pleura. SUMMARY: The rigid and flex-rigid instruments have different merits and limitations. Both approaches provide comparable diagnostic yields in the overall patient population undergoing diagnostic thoracoscopy, though their performances specifically in patients with fibrotic pleural thickening have not been examined. Operators using the flexi-rigid approach should have alternative strategies for sampling thickened pleura. Advances in cytopathology and imaging-guided biopsy will likely reduce the need of medical thoracoscopy in the future.

Discriminative BoW framework for mobile landmark recognition.

This paper proposes a new soft bag-of-words (BoW) method for mobile landmark recognition based on discriminative learning of image patches. Conventional BoW methods often consider the patches/regions in the images as equally important for learning. Amongst the few existing works that consider the discriminative information of the patches, they mainly focus on selecting the representative patches for training, and discard the others. This binary hard selection approach results in underutilization of the information available, as some discarded patches may still contain useful discriminative information. Further, not all the selected patches will contribute equally to the learning process. In view of this, this paper presents a new discriminative soft BoW approach for mobile landmark recognition. The main contribution of the method is that the representative and discriminative information of the landmark is learned at three levels: patches, images, and codewords. The patch discriminative information for each landmark is first learned and incorporated through vector quantization to generate soft BoW histograms. Coupled with the learned representative information of the images and codewords, these histograms are used to train an ensemble of classifiers using fuzzy support vector machine. Experimental results on two different datasets show that the proposed method is effective in mobile landmark recognition.

Potential drug-drug interactions between anti-cancer agents and community pharmacy dispensed drugs.

OBJECTIVE OF THE STUDY: To identify the prevalence of potential drug-drug interactions between hospital pharmacy dispensed anti-cancer agents and community pharmacy dispensed drugs. SETTING: A retrospective cohort study was conducted on the haematology/oncology department of the internal medicine ward in a large teaching hospital in Amsterdam, the Netherlands. METHOD: Prescription data from the last 100 patients treated with anti-cancer agents were obtained from Paracelsus, the chemotherapy prescribing system in the hospital. The community pharmacy dispensed drugs of these patients were obtained by using OZIS, a system that allows regionally linked pharmacies to call up active medication on any patient. Both medication lists were manually screened for potential drug-drug interactions by using several information sources on interactions, e.g. Pubmed, the Flockhart P450 table, Micromedex and Dutch reference books. MAIN OUTCOME MEASURE: Prevalence of potential drug-drug interactions between anti-cancer agents provided by the hospital pharmacy and drugs dispensed by the community pharmacy. RESULTS: Ninety-one patients were included in the study. A total of 31 potential drug-drug interactions were found in 16 patients, of which 15 interactions were clinically relevant and would have required an intervention. Of these interactions 1 had a level of severity ≥ D, meaning the potential drug-drug interaction could lead to long lasting or permanent damage, or even death. The majority of the interactions requiring an intervention (67%) had a considerable level of evidence (≥ 2) and were based on well-documented case reports or controlled interaction studies. Most of the potential drug-drug interactions involved the antiretroviral drugs (40%), proton pump inhibitors (20%) and antibiotics (20%). The anti-cancer drug most involved in the drug-drug interactions is methotrexate (33%). CONCLUSION: This study reveals a high prevalence of potential drug-drug interactions between anti-cancer agents provided by the hospital pharmacy and drugs dispensed by the community pharmacy. It shows us there is need for an optimal medication surveillance mechanism to detect potential drug-drug interactions between these two groups of medication, especially because of the high toxicity of anticancer drugs and thus the severe consequences these interactions can have for the patient.

A nonlinear least square technique for simultaneous image registration and super-resolution.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.

A soft double regularization approach to parametric blind image deconvolution.

This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.