Trajectory correction enables free-running chemical shift encoded imaging for accurate cardiac proton-density fat fraction quantification at 3T.

Background: Metabolic diseases can negatively alter epicardial fat accumulation and composition, which can be probed using quantitative cardiac chemical shift encoded (CSE) cardiovascular magnetic resonance (CMR) by mapping proton-density fat fraction (PDFF). To obtain motion-resolved high-resolution PDFF maps, we proposed a free-running cardiac CSE-CMR framework at 3T. To employ faster bipolar readout gradients, a correction for gradient imperfections was added using the gradient impulse response function (GIRF) and evaluated on intermediate images and PDFF quantification.

Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation.

Purpose: To develop an inline automatic quality control to achieve consistent diagnostic image quality with subject-specific scan time, and to demonstrate this method for 2D phase-contrast flow MRI to reach a predetermined SNR.

Methods: We designed a closed-loop feedback framework between image reconstruction and data acquisition to intermittently check SNR (every 20 s) and automatically stop the acquisition when a target SNR is achieved. A free-breathing 2D pseudo-golden-angle spiral phase-contrast sequence was modified to listen for image-quality messages from the reconstructions.

Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging.

Purpose: To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) and quantification using an open-source multi-language toolbox.

Methods: Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B off-resonance, SNR and TEs.

Ultra-high field magnetic resonance imaging of the quadriceps tendon enthesis in healthy subjects.

Purpose: Although enthesitis is a hallmark of several rheumatologic conditions, current imaging methods are still unable to characterize entheses changes because of the corresponding short transverse relaxation times (T2). A growing number of MR studies have used Ultra-High Field (UHF) MRI in order to assess low-T2 tissues e.g.

Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging.

In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects-comprising healthy, obese, and diabetic patients-who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss.

Semiautomatic quantification of abdominal wall muscles deformations based on dynamic MRI image registration.

Quantitative analysis of abdominal organs motion and deformation is crucial to better understand biomechanical alterations undermining respiratory, digestive or perineal pathophysiology. In particular, biomechanical characterization of the antero-lateral abdominal wall is central in the diagnosis of abdominal muscle deficiency. Here, we present a dedicated semiautomatic dynamic MRI postprocessing method enabling the quantification of spatial and temporal deformations of the antero-lateral abdominal wall muscles.