Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Patient-specific Biomechanical Model as Whole-body CT Image Registration Tool

Institution:
1Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, Perth, Australia.
2Vascular Engineering, Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Crawley, Perth, Australia.
3Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Elsevier Science
Publication Date:
May-2015
Journal:
Med Image Anal
Volume Number:
22
Issue Number:
1
Pages:
22-34
Citation:
Med Image Anal. 2015 May;22(1):22-34.
PubMed ID:
25721296
PMCID:
PMC4405489
Keywords:
Fuzzy-C Means, Patient-specific biomechanical model, Non-linear finite element analysis, Hausdorff distance, Whole-body image registration
Appears in Collections:
NAC, NA-MIC, NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
U24 CA180918/CA/NCI NIH HHS/United States
Generated Citation:
Li M., Miller K., Joldes G.R., Doyle B., Garlapati R.R., Kikinis R., Wittek A. Patient-specific Biomechanical Model as Whole-body CT Image Registration Tool. Med Image Anal. 2015 May;22(1):22-34. PMID: 25721296. PMCID: PMC4405489.
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Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images.

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