Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.

Background: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used in gynecologic MRI. However, their imaging qualities are limited compared to Turbo Spin Echo (TSE) with antispasmodics. Even with antispasmodics, TSE can be artifact-affected, necessitating a rapid backup sequence.

Healthy food diversity and the risk of major chronic diseases in the EPIC-Potsdam study.

Practicing a diverse diet may reduce chronic disease risk, but clear evidence is scarce and previous diet diversity measures rarely captured diet quality. We investigated the effect of the Healthy Food Diversity (HFD)-Index on incident type 2 diabetes (T2D), myocardial infarction (MI) and stroke among a middle-aged German population. The EPIC-Potsdam study recruited 27,548 participants from 1994 to 1998.

Fatty acid composition evaluation of abdominal adipose tissue using chemical shiftencoded MRI: Association with diabetes.

This study investigated the association between the fatty acid composition of abdominal adipose tissue in NAFLD patients using chemical shift-encoded MRI and the development of insulin resistance and T2DM. We enrolled 231 subjects with NAFLD who underwent both abdominal magnetic resonance spectroscopy and chemical shift-encoded MRI: comprising of 49 T2DM patients and 182 subjects without. MRI- and MRS-based liver fat fraction was measured from a circular region of interest on the right lobe of the liver.

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation.

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training.