Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial experience with artificial intelligence.

Background: This study evaluated a deep learning (DL) algorithm for detecting vessel steno-occlusions in patients with peripheral arterial disease (PAD). It utilised a private dataset, which was acquired and annotated by the authors through their institution and subsequently validated by two blinded readers.

Methods: A single-centre retrospective study analysed 105 magnetic resonance angiography (MRA) images using an EfficientNet B0 DL model.

Hypodense liver lesions in patients with hepatic steatosis: do we profit from dual-energy computed tomography?

Purpose: To evaluate dual-energy CT (DECT) imaging of hypodense liver lesions in patients with hepatic steatosis, having a high incidence in the general population and among cancer patients receiving chemotherapy.

Methods: One hundred and five patients with hepatic steatosis (liver parenchyma <40 HU) underwent contrast-enhanced DECT with reconstruction of pure iodine (PI), optimum contrast (OC), 80 kVp, and 120 kVp-equivalent data sets. Image noise (IN), lesion to liver signal to noise (SNR) and contrast to noise (CNR) ratios were quantitatively analysed; image quality was rated on a 5-point scale (1, excellent; 2, good; 3, fair; 4, poor; 5, non-diagnostic) by two independent reviewers.