Deep Learning Denoising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study.

Background And Purpose: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies.

Multi-phase 3D arterial spin labeling brain MRI in assessing cerebral blood perfusion and arterial transit times in children at 3T.

Background: 3D pseudocontinuous arterial spin labeling (pCASL) with a single post-labeling delay time is commonly used to measure cerebral blood flow (CBF). Multi-phase pCASL has been developed to simultaneously estimate CBF and arterial transit time (ATT).

Purpose: To evaluate the clinical feasibility of multi-phase 3D pCASL in pediatric patients, and to compare the estimation of ATT and CBF via linear weighted-delay and traditional non-linear iterative curve-fitting routines.