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Functional T1ρ mapping has been proposed as a method to assess pH and metabolism dynamics in the brain with high spatial and temporal resolution. The purpose of this work is to describe and evaluate a variant of the spin-locked echo-planar imaging sequence for functional T1ρ mapping at 3T. The proposed sequence rapidly acquires a time series of T1ρ maps with 4.0second temporal resolution and 10 slices of volumetric coverage. Simulation, phantom, and in vivo experiments are used to evaluate many aspects of the sequence and its implementation including fidelity of measured T1ρ dynamics, potential confounds to the T1ρ response, imaging parameter tradeoffs, time series analysis approaches, and differences compared to blood oxygen level dependent functional magnetic resonance imaging. It is shown that the high temporal resolution and volumetric coverage of the sequence are obtained with some expense including underestimation of the T1ρ response, sensitivity to T1 dynamics, and reduced signal-to-noise ratio. In vivo studies using a flashing checkerboard functional magnetic resonance imaging paradigm suggest differences between T1ρ and blood oxygen level dependent activation patterns. Possible sources of the functional T1ρ response and potential sequence improvements are discussed. The capability of T1ρ to map whole-brain pH and metabolism dynamics with high temporal and spatial resolution is potentially unique and warrants further investigation and development., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The authors previously demonstrated the feasibility of graph-searching-based automated edge detection in cardiac magnetic resonance (MR) imaging. To further assess the clinical utility of this method, unselected images from 11 consecutive subjects undergoing clinically indicated (except for one healthy volunteer) short-axis spin-echo MR imaging were analyzed. A total of 142 images from the 11 subjects, encompassing the left ventricle from apex to outflow tract, were analyzed. The computer algorithm correctly identified complete endocardial and epicardial contours in 121 of 142 images (85%). Correlations between observer-traced and computer-derived epicardial areas for all images were good (r = .71 for epicardium, r = .83 for endocardium); they improved for a subset of higher-quality images (r = .82 for epicardium, r = .92 for endocardium). The authors conclude that the current data further support the usefulness of computer digital image processing in geometric analysis of cardiac MR image data.

Gated cardiac magnetic resonance imaging (MRI) permits detailed evaluation of cardiac anatomy, including the calculation of left ventricular volume and mass. Current methods of deriving this information, however, require manual tracing of boundaries in several images; such manual methods are tedious, time consuming, and subjective. The purpose of this study is to apply a new computerized method to automatically identify endocardial and epicardial borders in MRIs. The authors obtained serial, short-axis, spin-echo MRIs of 13 excised animal hearts. Also obtained were selected short-axis, spin-echo ventricular images of 11 normal human volunteers. A method of automated edge detection based on graph-searching principles was applied to the ex vivo and in vivo images. Endocardial and epicardial areas were used to compute left ventricular mass and were compared with the anatomic left ventricular mass for the images of excised hearts. The endocardial and epicardial areas calculated from computer-derived borders were compared with areas from observer tracing. There was very close correspondence between computer-derived and observer tracings for excised hearts (r = 0.97 for endocardium, r = 0.99 for epicardium) and in vivo scans (r = 0.92 for endocardium, r = 0.90 for epicardium). There also was a close correspondence between computer-generated and actual left ventricular mass in the excised hearts (r = 0.99). These data suggest the feasibility of automated edge detection in MRIs. Although further validation is needed, this method may prove useful in clinical MRI.