Purpose: To adapt the so‐called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise).Materials and Methods: Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity‐encoded), intensity inhomogeneity‐corrected images, or surface coil‐based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method.Results: The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases.Conclusion: The new noise‐adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. J. Magn. Reson. Imaging 2010;31:192–203.

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical

Context: Ovarian hyperstimulation syndrome (OHSS) results from increased vascular permeability (VP) caused by ovarian hypersecretion of vascular endothelial growth factor (VEGF), which activates its receptor-2. In animals, the dopamine receptor 2 agonist cabergoline (Cb2) inactivates VEGF receptor-2 and prevents increased VP. Objective: Our objective was to test whether Cb2 reduces VP and prevents OHSS in humans. Design: We conducted a prospective, randomized, double-blind study on oocyte donors at risk of developing OHSS (>20 follicles, >12 mm developed, and >20 oocytes retrieved). Interventions: Cb2 0.5 mg/d (n = 37) or a placebo (n = 32) was administered from the day of human chorionic gonadotropin (d 0) until d 8. Ascites (a pocket of peritoneal fluid > 9 cm² in lithotomy position), hemoconcentration, and serum prolactin were recorded. Pharmacokinetic studies with magnetic resonance employing the transfer constant rate (K^trans, measure of permeability) and the extravascular extracellular space (υ_e, marker of cellular leakage) were performed to measure VP objectively. Results: Hematocrit (P < 0.01), hemoglobin (P = 0.003), and ascites (P = 0.005) were significantly lower on d 4 and 6 after treatment with Cb2 as compared with placebo. The incidence of moderate OHSS was 20.0 and 43.8%, respectively (P = 0.04). Magnetic resonance studies showed an increase in VP and extravascular leakage of fluid 5 d after human chorionic gonadotropin injection that was significantly prevented with Cb2 (K^transP = 0.04 and υ_eP = 0.001, respectively). Conclusions: Given that Cb2 is a well-established and safe medication, this study provides proof of concept for the use of dopamine agonists in the prevention of OHSS in women undergoing assisted reproduction.

Based on the available literature and the collective expertise of this consensus panel, confounder-corrected R2*-based liver iron concentration (LIC) is the preferred method with the strongest leve... Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, f...