TY - JOUR
T1 - A framework for predictive modeling of anatomical deformations
AU - Davatzikos, Christos
AU - Shen, Dinggang
AU - Mohamed, Ashraf
AU - Kyriacou, Stelios K.
N1 - Funding Information:
Manuscript received March 12, 2001; revised May2, 2001. This work was supported in part by a grant form the National Science Foundation (NSF) to the Engineering Research Center for Computer Integrated Surgical Systems and Technology. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was M. W. Vannier. Astreisk indicates corresponding author. *C. Davatzikos is with the Center for Biomedical Image Computing, Department of Radiology, JHOC 3230, Johns Hopkins University School of Medicine, 601 N. Caroline Street, Baltimore, MD 21287 USA (e-mail: hristos@rad.jhu.edu).
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2001/8
Y1 - 2001/8
N2 - A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.
AB - A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.
KW - Deformable models
KW - Intraoperative deformation
KW - Soft tissue deformation
KW - Surgical planning
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U2 - 10.1109/42.938251
DO - 10.1109/42.938251
M3 - Letter
C2 - 11513034
AN - SCOPUS:0035413320
VL - 20
SP - 836
EP - 843
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 8
ER -