[Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering].

Purpose: To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver.

Methods: The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning.

[Feasibility of Cerebrovascular Reserve Assessment Using Stretched Exponential Model in Major Cerebral Artery Steno-occlusive Disease: Comparison with SPECT].

Purpose: To clarify whether diffusion-weighted imaging using stretched exponential model can assess cerebrovascular reserve (CVR) in patients with major cerebral artery steno-occlusive disease, we compared stretched exponential parameters and single-photon emission computed tomography (SPECT).

Methods: Twenty-nine patients with unilateral major cerebral artery steno-occlusive disease (25 men and 4 women; age, 69±11 years) were analyzed in this study. The patients were divided into three groups: normal CVR (CVR≥30%), moderate CVR (10%≤CVR<30%), and severe CVR (CVR<10%).

Quantitative evaluation of focal liver lesions with T1 mapping using a phase-sensitive inversion recovery sequence on gadoxetic acid-enhanced MRI.

Purpose: To determine the usefulness of T1 values measured using a phase-sensitive inversion recovery (PSIR) sequence for the diagnosis of focal liver lesions.

Method: The study enrolled 87 patients who underwent gadoxetic acid-enhanced magnetic resonance imaging (MRI) for assessment of 38 hepatocellular carcinomas, 33 hepatic hemangiomas, 30 metastatic liver tumors, and 14 hepatic cysts. PSIR was performed before and 15 min after contrast agent administration, and then the respective T1 values were measured and the T1 reduction rate was calculated.

[Comparison of Arterial Spin-labeling Perfusion Images at Different Spatial Normalization Methods Based on Voxel-based Statistical Analysis].

Purpose: Spatial normalization is a significant image pre-processing operation in statistical parametric mapping (SPM) analysis. The purpose of this study was to clarify the optimal method of spatial normalization for improving diagnostic accuracy in SPM analysis of arterial spin-labeling (ASL) perfusion images.

Methods: We evaluated the SPM results of five spatial normalization methods obtained by comparing patients with Alzheimer's disease or normal pressure hydrocephalus complicated with dementia and cognitively healthy subjects.