Poster: ECR 2015 / B-0106 / Model-based iterative reconstruction in CT thorax: first quantitative clinical results by: C. Brussaard1, B. Ilsen1, J. Milles2, W. Giepmans2, J. de Mey1; 1Brussels/BE, 2Eindhoven/NL
Catheter ablation is an important option to treat ventricular tachycardias (VT). Scar-related VT is among the most difficult to treat, because myocardial scar, which is the underlying arrhythmogenic substrate, is patient-specific and often highly complex. The scar image from preprocedural late gadolinium enhancement magnetic resonance imaging (LGE- MRI) can provide high-resolution substrate information and, if integrated at the early stage of the procedure, can largely facilitate the procedure with image guidance. In clinical practice, however, early MRI integration is difficult because available integration tools rely on matching the MRI surface mesh and electroanatomical mapping (EAM) points, which is only possible after extensive EAM has been performed. In this paper, we propose to use a priori information on patient posture and a multi-sequence MRI integration framework to achieve accurate MRI integration that can be accomplished at an early stage of the procedure. From the MRI sequences, the left ventricular (LV) geometry, myocardial scar characteristics, and an anatomical landmark indicating the origin of the left main coronary artery are obtained preprocedurally using image processing techniques. Thereby the integration can be realized at the beginning of the procedure after acquiring a single mapping point. The integration method has been evaluated postprocedurally in terms of LV shape match and actual scar match. Compared to the iterative closest point (ICP) method that uses high-intensity mapping (225±49 points), our method using one mapping point reached a mean point-to-surface distance of 5.09±1.09 mm (vs. 3.85±0.60 mm, p<0.05), and scar correlation of -0.51±0.14 (vs. -0.50±0.14, p=NS).
This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a strict need for temporal correspondence across the image sequences. We also investigate which combination of spatial and temporal features yields the best segmentation performance. Our evaluation criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity projection feature and gradient orientation map yields the best segmentation performance, with an average point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal features does not necessarily increase performance.
The new SinMod method extracts motion from magnetic resonance imaging (MRI)-tagged (MRIT) image sequences. Image intensity in the environment of each pixel is modeled as a moving sine wavefront. Displacement is estimated at subpixel accuracy. Performance is compared with the harmonic-phase analysis (HARP) method, which is currently the most common method used to detect motion in MRIT images. SinMod can handle line tags, as well as speckle patterns. In artificial images (tag distance six pixels), SinMod detects displacements accurately (error < 0.02 pixels). Effects of noise are suppressed effectively. Sharp transitions in motion at the boundary of an object are smeared out over a width of 0.6 tag distance. For MRIT images of the heart, SinMod appears less sensitive to artifacts, especially later in the cardiac cycle when image quality deteriorates. For each pixel, the quality of the sine-wave model in describing local image intensity is quantified objectively. If local quality is low, artifacts are avoided by averaging motion over a larger environment. Summarizing, SinMod is just as fast as HARP, but it performs better with respect to accuracy of displacement detection, noise reduction, and avoidance of artifacts.
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