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• W2772316482 abstract "Abstract Inspired by the self‐optimizing capabilities of bone, a new concept of bone microstructure reconstruction has been recently introduced by using 2D synthetic skeletal images. As a preliminary clinical study, this paper proposes a topology optimization‐based method that can estimate 3D trabecular bone microstructure for the volume of interest (VOI) from 3D computed tomography (CT) scan data with enhanced computational efficiency and phenomenological accuracy. For this purpose, a localized finite element (FE) model is constructed by segmenting a target bone from CT scan data and determining the physiological local loads for the VOI. Then, topology optimization is conducted with multiresolution bone mineral density (BMD) deviation constraints to preserve the patient‐specific spatial bone distribution obtained from the CT scan data. For the first time, to our knowledge, this study has demonstrated that 60‐μm resolution trabecular bone images can be reconstructed from 600‐μm resolution CT scan data (a 62‐year‐old woman with no metabolic bone disorder) for the 4 VOIs in the proximal femur. The reconstructed trabecular bone includes the characteristic trabecular patterns and has morphometric indices that are in good agreement with the anatomical data in the literature. As for computational efficiency, the localization for the VOI reduces the number of FEs by 99%, compared with that of the full FE model. Compared with the previous single‐resolution BMD deviation constraint, the proposed multiresolution BMD deviation constraints enable at least 65% and 47% reductions in the number of iterations and computing time, respectively. These results demonstrate the clinical feasibility and potential of the proposed method." @default.
• W2772316482 created "2017-12-22" @default.
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• W2772316482 date "2018-01-11" @default.
• W2772316482 modified "2023-10-16" @default.
• W2772316482 title "Computational study of estimating 3D trabecular bone microstructure for the volume of interest from CT scan data" @default.
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• W2772316482 doi "https://doi.org/10.1002/cnm.2950" @default.
• W2772316482 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29218827" @default.
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