Reproducibility of quantitative ADC, T1, and T2 measurement on the cerebral cortex: Utility of whole brain echo-planar DWI with compressed SENSE (EPICS-DWI): A pilot study.

Purpose: To assess the reproducibility of ADC, T1, T2, and proton density (PD) measurements on the cortex across the entire brain using high-resolution pseudo-3D diffusion-weighted imaging using echo-planar imaging with compressed SENSE (EPICS-DWI) and 3D quantification with an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) in normal healthy adults.

Methods: Twelve healthy participants (median age, 33 years; range, 28-51 years) were recruited to evaluate the reproducibility of whole-brain EPICS-DWI and synthetic MRI. EPICS-DWI utilizes a compressed SENSE reconstruction framework while maintaining the EPI sampling pattern.

The Feasibility of Deep Learning-Based Reconstruction for Low-Tube-Voltage CT Angiography for Transcatheter Aortic Valve Implantation.

Objective: The purpose of this study is to evaluate the efficacy of deep learning reconstruction (DLR) on low-tube-voltage computed tomographic angiography (CTA) for transcatheter aortic valve implantation (TAVI).

Methods: We enrolled 30 patients who underwent TAVI-CT on a 320-row CT scanner. Electrocardiogram-gated coronary CTA (CCTA) was performed at 100 kV, followed by nongated aortoiliac CTA at 80 kV using a single bolus of contrast material.

Effect of iodine concentration and body size on iodine subtraction in virtual non-contrast imaging: A phantom study.

Introduction: Dual-energy computed tomography (DECT) can generate virtual non-contrast (VNC) images. Herein, we sought to improve the accuracy of VNC images by identifying the optimal slope of contrast media (SCM) for VNC-image generation based on the iodine concentration and subject's body size.

Methods: We used DECT to scan a multi-energy phantom including four iodine concentration rods (15, 10, 5, and 2 mg/mL), and 240 VNC images (eight SCM ranging from 0.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

Purpose: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation.

Methods: A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge.

Assessment of cerebral perfusion in moyamoya disease with dynamic pseudo-continuous arterial spin labeling using a variable repetition time scheme with optimized background suppression.

Purpose: Accurate assessment of cerebral perfusion in moyamoya disease is necessary to determine the indication for treatment. We aimed to investigate the usefulness of dynamic PCASL using a variable TR scheme with optimized background suppression in the evaluation of cerebral perfusion in moyamoya disease.

Methods: We retrospectively analyzed the images of 24 patients (6 men and 18 women, mean age 31.