New imaging technologies and assessment of regeneration patterns for the optimization of computer-assisted planning and risk analysis for living donor liver transplantation
Zusammenfassung der Projektergebnisse
Goal of the project was the optimization of preoperative planning for living donor liver transplantations and the exploration of regeneration patterns after transplantation. The first work package pursued three approaches to improve preoperative imaging: a) Improvement of routine 1.5 T MRI by use of new MRI sequences and a hepatocyte-specific contrast medium (Primovist™, Bayer-Schering). Comparison of suchlike improved “all-in-one” hepatic MRI with state-of-the-art “all-in-one” hepatic MDCT suggests that MDCT still represents the favorable choice for donor evaluation; b) Evaluation of Dual Energy Multidetector CT was discontinued in an early phase due to limited scan field-of-view and thus incomplete coverage of the liver in many patients; c) Development of 7T liver MRI: our interdisciplinary studies have now demonstrated the technical feasibility of visualizing the human liver in vivo. The second work package focused on the improvement of basic segmentation steps and the development of a model for advanced risk analysis. The following algorithms were newly developed or optimized: a) the vascular extraction by refinement of the segmentation method, b) the matching of tree structures for image registration, and c) the liver segmentation by development of a statistical liver model. In the third work package postoperative liver regeneration was assessed regarding morphology, angiogenesis, and development of intrahepatic venous collaterals and growth of liver and territorial volume. Twenty-two right-lobe donors and fifteen right-lobe recipients were examined at four postoperative time points and compared with the preoperative status. A newly developed software assistant enabled an elastic matching and quantification of extracted vascular structures, territories, and CT data at all time points. Our studies have shown: ▪ at 12 m postop. remnant volume reaches ~85% of the preoperative total liver volume ▪ at 10 d postop. volume growth in recipient grafts is increased compared to donor remnants ▪ impairment of regeneration of segment 4 is independent of the MHV status ▪ outflow obstruction and local growth of (sub)segments are correlated inversely ▪ relevant collaterals develop over a period of at least one week.
Projektbezogene Publikationen (Auswahl)
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"All-in-one" imaging protocols for the evaluation of potential living liver donors: Comparison of magnetic resonance imaging and multidetector computed tomography. Liver Transpl 2005, 11: 776-787
Schroeder T, Malagó M, Debatin JF, Goyen M, Nadalin S, Ruehm SG
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Evaluation of living liver donors with an allinclusive 3D multi-detector row CT protocol. Radiology 2006, 238: 900-910
Schroeder T, Radtke A, Kuehl H, Debatin JF, Malagó M, Ruehm SG
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Preoperative volume prediction in adult living donor liver transplantation: how much can we rely on it? Am J Transplantation 2007, 7: 672-679
Radtke A, Sotiropoulos GC, Nadalin S, Molmenti EP, Schroeder T, Lang H, Saner F, Valentin-Gamazo C, Frilling A, Schenk A, Broelsch CE, Malagó M
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Territorial belonging of the middle hepatic vein in living liver donor candidates evaluated by three-dimensional computed tomographic reconstruction and virtual liver resection. Br J Surg 2009, 96: 206-213
Radtke A, Sgourakis G, Sotiropoulos GC, Molmenti EP, Saner FH, Timm S, Malagó M, Lang H
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Computer-assisted surgery planning for complex liver resections: when is it helpful? A single-center experience over an 8-year period. Ann Surg 2010, 252: 876-883
Radtke A, Sotiropoulos GC, Molmenti EP, Schroeder T, Peitgen HO, Frilling A, Broering DC, Broelsch CE, Malagó M
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Donor/recipient algorithm for management of the middle hepatic vein in right graft live donor liver transplantation. Am J Surg 2010, 199: 708-715
Radtke A, Sgourakis G, Sotiropoulos GC, Beckebaum S, Molmenti EP, Saner FH, Schroeder T, Nadalin S, Schenk A, Lang H, Malagó M, Broelsch CE
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Hepatic venous drainage: how much can we learn from imaging studies? Anatomicfunctional classification derived from three-dimensional computed tomography reconstructions. Transplantation 2010, 89: 1518-1525
Radtke A, Sotiropoulos GC, Sgourakis G, Molmenti EP, Schroeder T, Saner FH, Beckebaum S, Broelsch CE, Broering DC, Malagó M
