Quantification of Gaps in Ablation Lesions Around the Pulmonary Veins in Delayed Enhancement MRI
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The pathogenesis of atrial fibrillation (AF) is closely related to the fibrotic tissues in left atrial (LA). Delay-enhancement magnetic resonance imaging (DE-MRI) has been widely used in the ablation of atrial fibrillation, which can accurately describe the distribution of myocardial fibrosis and postoperative scars. Combining EM algorithm, level-set and graph-cut, this paper proposes a method to segment the fibrotic tissues and postoperative scars and also quantify their proportion in left atrial from DE-MRI. In 4 clinical cases, our method can accomplish the extraction of heart, the segmentation of left atrium and sequentially the quantification of the fibrotic tissues nearby with little manual intervention. Experimental results show that accurate segmentation of LA is achieved in 55 slices with 96 slices containing LA among 4 cases in total. With manual correction in the rest slices, the final results about the proportion of fibrotic tissues in LA are 14.78%, 21.02%, 25.17%, 14.77% respectively which are consistent with the clinical diagnosis. Evaluated by the clinician, our method is robust against different resolution and can provide auxiliary function for ablation of AF.
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Ostium
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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).
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Left Pulmonary Vein
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A patient-specific left atrium (LA) model extracted from intra-operative C-arm CT plays an important role in planning for transcatheter left atrial fibrillation ablation. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during the intervention. However, automatic segmentation of the LA, together with the left atrial appendage (LAA) and the pulmonary vein (PV) trunks, is challenging due to the large structural variations and imaging artifacts. In this paper we exploit a part based LA model to handle the structural variations and different parts are then merged into a consolidated mesh. The connection region between the PV/LAA and the LA chamber is segmented precisely by enforcing both image boundary delineation accuracy and mesh smoothness. Furthermore, the boundary between parts is optimized to improve the mesh part labeling accuracy.
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Catheter ablation is an increasingly important curative procedure for atrial fibrillation. Knowledge of the local wall thickness is essential to determine the proper ablation energy. This paper presents the first semi-automatic atrial wall thickness measurement method for ablation guidance. It includes both endocardial and epicardial atrial wall segmentation on CT image data. Segmentation is based on active contours, Otsu's multiple threshold method and hysteresis thresholding. Segmentation results were compared to contours manually drawn by two experts, using repeated measures analysis of variance. The root mean square differences between the semi-automatic and the manually drawn contours were comparable to intra-observer variation (endocardium: p = 0.23, epicardium: p = 0.18). Mean wall thickness difference is significant between one of the experts on one side, and the presented method and the other expert on the other side (p <; 0.001). Wall thicknesses found were in the range of 0.5-5.5mm, corresponding to values presented in literature.
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Segmentation of the left atrium is vital for pre-operative assessment of its anatomy in radio-frequency catheter ablation (RFCA) surgery. RFCA is commonly used for treating atrial fibrillation. In this paper we present an semi-automatic approach for segmenting the left atrium and the pulmonary veins from MR angiography (MRA) data sets. We also present an automatic approach for further subdividing the segmented atrium into the atrium body and the pulmonary veins. The segmentation algorithm is based on the notion that in MRA the atrium becomes connected to surrounding structures via partial volume affected voxels and narrow vessels, the atrium can be separated if these regions are characterized and identified. The blood pool, obtained by subtracting the pre- and post-contrast scans, is first segmented using a region-growing approach. The segmented blood pool is then subdivided into disjoint subdivisions based on its Euclidean distance transform. These subdivisions are then merged automatically starting from a seed point and stopping at points where the atrium leaks into a neighbouring structure. The resulting merged subdivisions produce the segmented atrium. Measuring the size of the pulmonary vein ostium is vital for selecting the optimal Lasso catheter diameter. We present a second technique for automatically identifying the atrium body from segmented left atrium images. The separating surface between the atrium body and the pulmonary veins gives the ostia locations and can play an important role in measuring their diameters. The technique relies on evolving interfaces modelled using level sets. Results have been presented on 20 patient MRA datasets.
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The leading cause of death worldwide are cardiovascular diseases. In addition, the
number of patients suffering from heart failure is rising. The underlying cause of heart failure is often a myocardial infarction. For diagnosis in clinical routine, cardiac magnetic resonance imaging is used, as it provides information about morphology, blood flow, perfusion, and tissue characterization. In more detail, the analysis of the
tissue viability is very important for diagnosis, procedure planning, and guidance, i.e., for implantation of a bi-ventricular pacemaker. The clinical gold standard for
the viability assessment is 2-D late gadolinium enhanced magnetic resonance imaging
(LGE-MRI). In the last years, the imaging quality continuously improved and LGE-MRI was extended to a 3-D whole heart scan. This scan guarantees an accurate
quantification of the myocardium to the extent of myocardial scarring.
The main challenge arises in the accurate segmentation and analysis of such images. In this work, novel methods for the segmentation of the LGE-MRI data sets, both 2-D and 3-D, are proposed. One important goal is the direct segmentation of the LGE-MRI and the independence of an anatomical scan to avoid errors from the anatomical scan contour propagation. For the 2-D LGE-MRI segmentation, the short
axis stack of the left ventricle (LV) is used. First, the blood pool is detected and a rough outline is maintained by a morphological active contours without edges approach. Afterwards, the endocardial and epicardial boundary is estimated by either a filter or learning based method in combination with a minimal cost path search in polar space. For the endocardial contour refinement, an additional scar exclusion step is added. For the 3-D LGE-MRI, the LV is detected within the whole heart scan. In the next step, the short axis view is estimated using principal component analysis. For the endocardial and epicardial boundary estimation also a filter based or learning
based approach can be applied in combination with dynamic programming in polar
space. Furthermore, because of the high resolution also the papillary muscles are
segmented.
In addition to the fully automatic LV segmentation approaches, a generic semi-
automatic method based on Hermite radial basis function interpolation is introduced in combination with a smart brush. Effective interactions with less number of equations accelerate the performance and therefore, a real-time and an intuitive, interactive segmentation of 3-D objects is supported effectively.
After the segmentation of the left ventricle’s myocardium, the scar tissue is quantified. In this thesis, three approaches are investigated. The full-width-at-half-max algorithm and the x-standard deviation methods are implemented in a fully automatic manner. Furthermore, a texture based scar classification algorithm is introduced.
Subsequently, the scar tissue can be visualized, either in 3-D as a surface mesh or in 2-D projected onto the 16 segment bull’s eye plot of the American Heart Association.
However, for precise procedure planning and guidance, the information about the scar transmurality is very important. Hence, a novel scar layer visualization is introduced.
Therefore, the scar tissue is divided into three layers depending on the location of
the scar within the myocardium. With this novel visualization, an easy distinction between endocardial, mid-myocardial, or epicardial scar is possible. The scar layers
can also be visualized in 3-D as surface meshes or in 2-D projected onto the 16
segment bull’s eye plot.
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