The framework provides a versatile and reusable tool for the simulation of any MRI experiment including physiological fluids and arbitrarily complex flow motion.

Purpose To quantitatively evaluate a superresolution technique for 3D, one‐millimeter isotropic diffusion‐weighted imaging (DWI) of the whole breasts. Methods Isotropic 3D DWI datasets are obtained using a combination of (i) a readout‐segmented diffusion‐weighted‐echo‐planar imaging (DW‐EPI) sequence (rs‐EPI), providing high in‐plane resolution, and (ii) a superresolution (SR) strategy, which consists of acquiring 3 datasets with thick slices (3 mm) and 1‐mm shifts in the slice direction, and combining them into a 1 × 1 × 1‐mm3 dataset using a dedicated reconstruction. Two SR reconstruction schemes were investigated, based on different regularization schemes: conventional Tikhonov or Beltrami (an edge‐preserving constraint). The proposed SR strategy was compared to native 1 × 1 × 1‐mm3 acquisitions (i.e. with 1‐mm slice thickness) in 8 healthy subjects, in terms of signal‐to‐noise ratio (SNR) efficiency, using a theoretical framework, Monte Carlo simulations and region‐of‐interest (ROI) measurements, and image sharpness metrics. Apparent diffusion coefficient (ADC) values in normal breast tissue were also compared. Results The SR images resulted in an SNR gain above 3 compared to native 1 × 1 × 1‐mm3 using the same acquisition duration (acquisition gain 3 and reconstruction gain >1). Beltrami‐SR provided the best results in terms of SNR and image sharpness. The ADC values in normal breast measured from Beltrami‐SR were preserved compared to low‐resolution images (1.91 versus 1.97 ×10–3 mm2/s, P = .1). Conclusion A combination of rs‐EPI and SR allows 3D, 1‐mm isotropic breast DWI data to be obtained with better SNR than a native 1‐mm isotropic acquisition. The proposed DWI protocol might be of interest for breast cancer monitoring/screening without injection.

BackgroundHemodialysis patients with COVID-19 have been reported to be at higher risk for death than the general population. Several prognostic factors have been identified in the studies from Asian, European or American countries. This is the first national Lebanese study assessing the factors associated with SARS-CoV-2 mortality in hemodialysis patients.MethodsThis is a cross-sectional study that included all chronic hemodialysis patients in Lebanon who were tested positive for SARS-CoV-2 from 31st March to 1st November 2020. Data on demographics, comorbidities, admission to hospital and outcome were collected retrospectively from the patients' medical records. A binary logistic regression analysis was performed to assess risk factors for mortality.ResultsA total of 231 patients were included. Mean age was 61.46 ± 13.99 years with a sex ratio of 128 males to 103 females. Around half of the patients were diabetics, 79.2% presented with fever. A total of 115 patients were admitted to the hospital, 59% of them within the first day of diagnosis. Hypoxia was the major reason for hospitalization. Death rate was 23.8% after a median duration of 6 (IQR, 2 to 10) days. Adjusted regression analysis showed a higher risk for death among older patients (odds ratio=1.038; 95% confidence interval: 1.013, 1.065), patients with heart failure (odds ratio=4.42; 95% confidence interval: 2.06, 9.49), coronary artery disease (odds ratio=3.27; 95% confidence interval: 1.69, 6.30), multimorbidities (odds ratio=1.593; 95% confidence interval: 1.247, 2.036), fever (odds ratio=6.66; 95% confidence interval: 1.94, 27.81), CRP above 100 mg/L (odds ratio=4.76; 95% confidence interval: 1.48, 15.30), and pneumonia (odds ratio=19.18; 95% confidence interval: 6.47, 56.83).ConclusionsThis national study identified older age, coronary artery disease, heart failure, multimorbidities, fever and pneumonia as risk factors for death in patients with COVID-19 on chronic hemodialysis. The death rate was comparable to other countries and estimated at 23.8%.

Kitware SAS, France / USA Abstract. Angiographic imaging is a crucial domain of medical imaging. In particular, Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source / open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies.