Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

Background: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-like symptoms in female offspring.

Purpose: To identify mechanisms of neuronal abnormalities in lactational Poly I:C offspring using quantitative MRI (qMRI) tools.

Study Type: Prospective.

Fast and accurate T mapping using Bloch simulations and low-rank plus sparse matrix decomposition.

Purpose: MRI's T relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T-maps from highly undersampled multi-echo spin-echo (MESE) MRI data.

Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images.

Background: Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology.

Purpose: To compare conventional (i.

Correcting for imaging gradients-related bias of T relaxation times at high-resolution MRI.

Purpose: High-resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T relaxation times (qT ) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High-resolution T mapping, however, suffers from severe underestimation of T values due to molecular diffusion.

Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features.

Purpose: Multicomponent analysis of MRI T relaxation time (mcT ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T values. This voxel-based approach is challenging due to the large ambiguity in the multi-T space and the low SNR of MRI signals. Herein, we present a data-driven mcT analysis, which utilizes the statistical strength of identifying spatially global mcT motifs in white matter segments before deconvolving the local signal at each voxel.

Diagnosing and monitoring pleural effusion using parametric electrical impedance tomography - a computational 3D model and preliminary experimental results.

Purpose: Diagnosing and monitoring pleural effusion (PE) is challenging due unsuitability of existing modalities. In the present study, a novel parametric electrical impedance tomography (pEIT) technique, tailored to a clinically feasible system to diagnose PE is presented.

Methods: An electrical impedance tomography (EIT) numeric solver was applied to a 3D realistic normal model and five PE models to simulate sets of surface measurements.

Changes in High-Frequency Intracardiac Electrogram Indicate Cardiac Ischemia.

High-frequency QRS (HFQRS) analysis of surface ECG is a reliable marker of cardiac ischemia (CI). This study aimed to assess the response of HFQRS signals from standard intracardiac electrodes (iHFQRS) to CI in swine and compare them with conventional ST-segment deviations. Devices with three intracardiac leads were implanted in three swine in a controlled environment.