MRI-based pre- and postprandial flow in the mesenteric vasculature of patients with suspected chronic mesenteric ischemia.

PURPOSE: The physiological increase of mesenteric blood flow after a meal is impaired in patients with occlusive chronic mesenteric ischemia (CMI). This principle could be used to develop a highly desired diagnostic test assessing the sufficiency of the collateral mesenteric circulation. This study assesses the potential to identify CMI patients using two-dimensional time-resolved phase-contrast magnetic resonance imaging (2D PC-MRI) flow measurements. METHOD: This prospective cohort study included patients with suspected CMI, based on: typical history, imaging, and functional testing. Cardiac gated 2D PC-MRI flow measurements (expressed as ml/min/kg) were performed in mesenteric arteries and veins during inspiration and expiration, after six hours of fasting and 20, 30, and 40 min after a meal challenge with a high caloric drink. RESULTS: Flow measurements were obtained in 19 patients: 8 CMI and 11 non-CMI. CMI patients showed a significantly smaller increase in postprandial blood flow in the superior mesenteric artery (SMA) at 30 and 40 min (30 min CMI 1.27(0.12-2.44) vs. non-CMI 7.82(6.28-10.90); 40 min CMI 0.30(-0.26-3.16) vs. non-CMI 7.94(6.32-10.90)) and a lower total arterial flow at 40 min (CMI 3.21(-0.72-5.05) vs. non-CMI 9.31(5.58-13.83)). Repeated flow measurements showed normalization of impaired postprandial venous flow after mesenteric artery stenting in one patient. CONCLUSIONS: The significantly lower increase in postprandial mesenteric blood flow in CMI patients confirms the promise of mesenteric blood flow measurements, before and 30-40 min after a meal, as a future diagnostic test to identify CMI patients among patients with a high clinical suspicion of CMI and mesenteric artery stenosis.

Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification.

OBJECTIVES: The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. METHODS: Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. RESULTS: DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). CONCLUSIONS: LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. KEY POINTS: • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning-based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.