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.

Determining and Validating Population Differences in Magnetic Resonance Angiography Using Sparse Representation.

Goal: Identifying population differences can serve as an insightful tool for diagnostic radiology. To do so, a reliable preprocessing framework and data representation are vital.

Methods: We build a machine learning model to visualize gender differences in the circle of Willis (CoW), an integral part of the brain's vasculature.

Induction of Short-Term Sensitization by an Aversive Chemical Stimulus in Zebrafish Larvae.

Larval zebrafish possess a number of molecular and genetic advantages for rigorous biological analyses of learning and memory. These advantages have motivated the search for novel forms of memory in these animals that can be exploited for understanding the cellular and molecular bases of vertebrate memory formation and consolidation. Here, we report a new form of behavioral sensitization in zebrafish larvae that is elicited by an aversive chemical stimulus [allyl isothiocyanate (AITC)] and that persists for ≥30 min.