Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping.

Purpose: To develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM).

Materials And Methods: This prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes.

Region Expansion of Background Field Removal with Local Spherical Harmonics Approximation for Whole-brain Quantitative Susceptibility Mapping.

Purpose: Quantitative susceptibility mapping (QSM) is useful for obtaining biological information. To calculate susceptibility distribution, it is necessary to calculate the local field caused by the differences of susceptibility between the tissues. The local field can be obtained by removing a background field from a total field acquired by MR phase image.

Efficacy of Quantitative Susceptibility Mapping with Brain Surface Correction and Vein Removal for Detecting Increase Magnetic Susceptibility in Patients with Alzheimer's Disease.

Purpose: Studies on quantitative susceptibility mapping (QSM) have reported an increase in magnetic susceptibilities in patients with Alzheimer's disease (AD). Despite the pathological importance of the brain surface areas, they are sometimes excluded in QSM analysis. This study aimed to reveal the efficacy of QSM analysis with brain surface correction (BSC) and/or vein removal (VR) procedures.

A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer's disease.

Objectives: Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM.

Methods: Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included.

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy.

Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images.

Our purpose in this study was to develop an automated method for segmentation of white matter (WM) and gray matter (GM) regions with multiple sclerosis (MS) lesions in magnetic resonance (MR) images. The brain parenchymal (BP) region was derived from a histogram analysis for a T1-weighted image. The WM regions were segmented by addition of MS candidate regions, which were detected by our computer-aided detection system for the MS lesions, and subtraction of a basal ganglia and thalamus template from "tentative" WM regions.

Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging.

It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image.