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Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.

Invest Radiol

January 2025

From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).

Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.

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Introduction: The World Health Organization (WHO) has emphasized the importance of ensuring respectful and dignified childbirth experiences. However, many countries, including Rwanda, have documented negative experiences during childbirth. Identifying best practices can help uncover sustainable solutions for resource-limited settings rather than focusing solely on the challenges and negative aspects.

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This study examines whether the detrimental effects of the COVID-19 pandemic on the affectivity of the population extend one year after the outbreak. In an online-mobile session, participants completed surveys (i.e.

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Aim: To comprehensively investigate the effects of antioxidant nutrients on muscle mass, strength and function in chronic obstructive pulmonary disease (COPD) patients.

Methods: PubMed, Embase, Cochrane Library, and Web of Science were comprehensively searched from the inception to January 3, 2024. The quality of randomized controlled trials (RCTs) was measured using the Jadad scale.

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This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency.

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