Abdominal Imaging. Computation and Clinical Applications: by Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W.

By Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos (auth.), Hiroyuki Yoshida, Simon Warfield, Michael W. Vannier (eds.)

This booklet constitutes the refereed court cases of the fifth overseas Workshop CCAA 2013, held at the side of MICCAI 2013, in Nagoya, Japan, in September 2013. The booklet comprises 32 papers that have been rigorously reviewed and chosen from 38 submissions. the subjects lined are belly atlases, form research and morphology in belly buildings and organs, detection of anatomical and sensible landmarks, dynamic, practical, physiologic, and anatomical belly imaging, registration equipment for belly intra- and inter-patient variability, augmented fact recommendations for intervention, scientific purposes in radio-frequency ablation, open surgical procedure, and minimally invasive surgery.

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Additional info for Abdominal Imaging. Computation and Clinical Applications: 5th International Workshop, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings

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One case (D) did not improve but the initial error was already relatively small. For comparison, E shows an well-prepared case (which was not included in the evaluation) in which the registration error was low to start with due to the inital good similarity in distension [9]. 0 mm. This is because the similarity measure will already be high initially and cause the proposed method to terminate at an early iteration. R. Roth et al. Fig. 7. Evolution of registration error in mm over several iterations of the proposed optimization method 4 Discussion This paper introduced a prone and supine registration method that aims to overcome the difficulties posed by marked differences in distension between datasets.

The endoluminal surface is iteratively deformed using thin plate spline interpolation in order to increase similarity between prone and supine surfaces. Iterative deformation allows the re-computation of surface curvatures and, therefore, surface features to resemble one another more closely. Therefore, the similarity between surfaces increases with each optimization step when running a subsequent intensity-based registration in cylindrical space. Improved spatial alignment of endoluminal surfaces and better registration accuracies are shown in a limited number of challenging cases.

Alternative methods that aim to register the full endoluminal surfaces have been proposed [10,11,4,16]. However, studies routinely use optimally cleansed and distended CTC datasets; yet in reality, approximately 50% of cases are “poorly prepared” [3]. Poor preparation may be due to incomplete cleansing or inadequate distension but a further, less quantifiable problem encountered in daily practice is that of markedly discrepant distension, ofen due to the redistributiuon of gas during patient repositioning.

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