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Background: Studies have shown the clinical effects of repetitive transcranial magnetic stimulation (rTMS) on depression in Alzheimer's disease (AD). However, the underlying mechanisms remain poorly understood. The measurement of brain activation links neurobiological and functional aspects but is challenging in patients with dementia.

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Left Ventricular Hemodynamic Forces Changes in Fabry Disease: A Cardiac Magnetic Resonance Study.

J Magn Reson Imaging

January 2025

Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Background: Hemodynamic force (HDF) from cardiac MRI can indicate subclinical myocardial dysfunction, and help identify early cardiac changes in patients with Fabry disease (FD). The hemodynamic change in FD patients remains unclear.

Purpose: To explore HDF changes in FD and the potential of HDF measurements as diagnostic markers indicating early cardiac changes in FD.

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Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed.

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Magnetic Resonance Acoustic Radiation Force Imaging (MR-ARFI).

J Magn Reson Imaging

January 2025

Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA.

This review covers the theoretical background, pulse sequence considerations, practical implementations, and multitudes of applications of magnetic resonance acoustic radiation force imaging (MR-ARFI) described to date. MR-ARFI is an approach to encode tissue displacement caused by the acoustic radiation force of a focused ultrasound field into the phase of a MR image. The displacement encoding is done with motion encoding gradients (MEG) which have traditionally been added to spin echo-type and gradient recalled echo-type pulse sequences.

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Purpose: In magnetic resonance-guided focused ultrasound (MRgFUS) breast therapies, the focal location must be characterized to guide successful treatment. Focal characterization is difficult because heterogeneous breast tissues introduce phase aberrations that blur and shift the focus and traditional guidance methods do not work in adipose tissues. The purpose of this work is to evaluate numerical simulations of MRgFUS that predict the focal location.

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