Biomedical subjects

R Mark Henkelman

Publications and source records attributed to R Mark Henkelman.

4 recordsLinked to original sources

PubMed2003

Multiple-mouse MRI.

Several theoretical parallel-imaging approaches are evaluated that seek to improve the efficiency of an MRI experiment involving multiple small samples, such as mice. The best method for our mouse phenotyping application is chosen in terms of efficiency and ease of implementation, and the approach is demonstrated at 1.5 T on a clinical scanner with an array of four shielded birdcage coils with four parallel receivers. Electronic interactions between the receiver channels in the system are quantified and a novel sensitivity-encoding (SENSE)-like postprocessing method is described to remove the resulting image ghosts. In parallel imaging with a four-coil array, the time required for three-dimensional (3D) high-resolution imaging of four mice is reduced to one-fourth the time that it would take to image the mice sequentially.

Animals↗

PubMed2002

Why does MTR change with neuronal depolarization?

T1 and T2 relaxation, and magnetization transfer (MT) of the rat brain were measured during experimentally induced spreading depression (SD). All measured MR parameters changed during SD: T1 relaxation increased by approximately 13%, whereas the T2 increase was substantially larger (88%). MT results showed an MT ratio (MTR) decrease of 9%. The lack of change in the MT exchange rate indicated that the MT processes between water and macromolecular protons are not affected by neuronal depolarization. The observed decrease in MTR was only caused by changes in T1 and T2 relaxation.

Animals↗

PubMed2002

Pulse-wave velocity measured in one heartbeat using MR tagging.

A noninvasive method for measuring the aortic pulse-wave velocity (PWV) in a single heartbeat is introduced. The method sinusoidally tags a column of blood within the vessel, and rapidly acquires a series of 1D projections of the tags as they move (in practice, 64 projections at 4-ms intervals). From these projections, the relative motion of blood at different positions along the vessel is measured. The PWV is obtained by fitting a mathematical model of blood flow to the tag trajectories. Tests of this method in a pulsatile flow phantom are presented using latex and polyurethane tubes. The PWV measured in these tubes was (mean +/- standard deviation) 4.4 +/- 0.5 m/s and 2.3 +/- 0.2 m/s, respectively. The distensibility of each tube was calculated from the PWV (latex = (7 +/- 2) 10(-3) mm Hg(-1), poly. = (25 +/- 4) 10(-3)mmHg(-1)) and found to agree within error with distensibility measurements based on the change of tube area with pressure (latex = (6.3 +/- 0.3) 10(-3)mmHg(-1), poly. = (27 +/- 1) 10(-3) mmHg(-1)). To test its feasibility, the PWV measurement was applied to four normal volunteers. The measured PWV values were 3.9 +/- 0.8 m/s, 3.6 +/- 0.9 m/s, 3.9 +/- 0.5 m/s, and 5.3 +/- 0.8 m/s. By acquiring an independent PWV measurement each heartbeat, errors introduced by arrhythmia and trigger variability appear to be avoided with this method.

Aorta↗

PubMed2002

Orientational diffusion reflects fiber structure within a voxel.

Several new MR techniques have been introduced to infer direction through diffusion in multiple nerve fiber bundles within a voxel. To date, however, there has been no physical model reported to evaluate these methodologies and their ability to determine fiber orientation. In this article a model of diffusion analogous to nerve fibers is presented. Diffusion measurements at multiple closely spaced angles of 15 degrees in samples with different fiber orientations are compared with theoretical calculations for restricted diffusion in cylindrical geometry. Orientational diffusion measurements are shown to reflect fiber geometry and theoretical predictions to within 10%. Simulations of fiber crossings within a voxel suggest fiber orientation does not correspond to the direction of the largest measured diffusion coefficient, but theoretical knowledge of signal decay curves can predict the shape of these diffusion coefficient contours for given fiber orientation probabilities.

Anisotropy↗