A dark band or rim along parts of the subendocardial border of the left ventricle (LV) and the myocardium has been noticed in some dynamic contrast-enhanced MR perfusion studies. The artifact is thought to be due to susceptibility effects from the gadolinium bolus, motion, or resolution, or a combination of these. Here motionless ex vivo hearts in which the cavity was filled with gadolinium are used to show that dark rim artifacts can be consistent with resolution effects alone. Magn Reson Med 54:1295-1299, 2005.

No abstract

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by multichannel blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. Three blind identification algorithms are analyzed here to assess their utility in medical imaging: eigenvector-based algorithm for multichannel blind deconvolution; cross relations; and iterative quadratic maximum-likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. Tissue responses corresponding to a physiological two-compartment model are primarily considered. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that IQML gives more accurate estimates than the other two blind identification methods.

The objective of this study was to validate a deformable image registration technique, termed Hyperelastic Warping, for left ventricular strain measurement during systole using cine-gated, nontagged MR images with strains measured from tagged MRI. The technique combines deformation from high resolution, non-tagged MR image data with a detailed computational model, including estimated myocardial material properties, fiber direction, and active fiber contraction, to provide a comprehensive description of myocardial contractile function. A normal volunteer (male, age 30) with no history of cardiac pathology was imaged with a 1.5 T Siemens Avanto clinical scanner using a TrueFISP imaging sequence and a 32-channel cardiac coil. Both tagged and non-tagged cine MR images were obtained. The Hyperelastic Warping solution was evolved using a series of non-tagged images in ten intermediate phases from end-diastole to end-systole. The solution may be considered as ten separate warping problems with multiple templates and targets. At each stage, an active contraction was initially applied to a finite element model, and then image-based warping penalty forces were utilized to generate the final registration. Warping results for circumferential strain (R2 = 0.75) and radial strain (R2 = 0.78) were strongly correlated with results obtained from tagged MR images analyzed with a Harmonic Phase (HARP) algorithm. Results for fiber stretch, LV twist, and transmural strain distributions were in good agreement with experimental values in the literature. In conclusion, Hyperelastic Warping provides a unique alternative for quantifying regional LV deformation during systole without the need for tags.

Two factor analysis of dynamic structures (FADS) methods for the extraction of time-activity curves (TACs) from cardiac dynamic SPECT data sequences were investigated. One method was based on a least squares (LS) approach which was subject to positivity constraints. The other method was the well known apex-seeking (AS) method. A post-processing step utilizing a priori information was employed to correct for the non-uniqueness of the FADS solution. These methods were used to extract 99mTc-teboroxime TACs from computer simulations and from experimental canine and patient studies. In computer simulations, the LS and AS methods, which are completely different algorithms, yielded very similar and accurate results after application of the correction for non-uniqueness. FADS-obtained blood curves correlated well with curves derived from region of interest (ROI) measurements in the experimental studies. The results indicate that the factor analysis techniques can be used for semi-automatic estimation of activity curves derived from cardiac dynamic SPECT images, and that they can be used for separation of physiologically different regions in dynamic cardiac SPECT studies.