Abstract
Purpose
Patient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments.
Methods
Eight fresh cadaver specimens of the lower extremity were used in the experiments. Before dissection, CT images of all the specimens were acquired and the bone regions in CT images were extracted using an automated segmentation method to reconstruct the bone shape models. During dissection, ten different muscle attachment regions were recorded with an optical motion tracker. Then, these regions obtained from eight cadavers were integrated on an average bone surface via non-rigid registration, and muscle attachment probabilistic atlases (PAs) were constructed. An average muscle attachment region derived from the PA was non-rigidly mapped to the patients bone surface to estimate the patient-specific muscle attachment region.
Results
Average Dice similarity coefficient between the true and estimated attachment areas computed by the proposed method was more than 10% higher than the one computed by a previous method in most cases and the average boundary distance error of the proposed method was 1.1 mm smaller than the previous method on average.
Conclusion
We conducted cadaver experiments to measure the attachment regions of the hip muscles and constructed PAs of the muscle attachment regions. The muscle attachment PA clarified the variations of the location of the muscle attachments and allowed us to estimate the patient-specific attachment area more accurately based on the patient bone shape derived from CT.
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References
Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Robotics-DL tentative, International Society for Optics and Photonics, pp 586-606
Blemker SS, Delp SL (2005) Three-dimensional representation of complex muscle architectures and geometries. Ann Biomed Eng 33(5):661–673
Breteler MDK, Spoor CW, Van der Helm FC (1999) Measuring muscle and joint geometry parameters of a shoulder for modeling purposes. J Biomech 32(11):1191–1197
Carbone V, Fluit R, Pellikaan P, van der Krogt M, Janssen D, Damsgaard M, Verdonschot N (2015) TLEM 2.0? A comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity. J Biomech 48(5):734–741
Damsgaard M, Rasmussen J, Christensen ST, Surma E, De Zee M (2006) Analysis of musculoskeletal systems in the AnyBody Modeling System. Simul Model Pract Theory 14(8):1100–1111
Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Thelen DG (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. Biomed Eng IEEE Trans 54(11):1940–1950
Delp SL, Ringwelski DA, Carroll NC (1994) Transfer of the rectus femoris: effects of transfer site on moment arms about the knee and hip. J Biomech 27(10):1201–1211
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Sonka M (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30(9):1323–1341
Horsman MK, Koopman H, Van der Helm F, Prose LP, Veeger H (2007) Morphological muscle and joint parameters for musculoskeletal modelling of the lower extremity. Clin Biomech 22(2):239–247
Ito Y, Matsushita I, Watanabe H, Kimura T (2012) Anatomic mapping of short external rotators shows the limit of their preservation during total hip arthroplasty. Clin Orthop Relat Res 470(6):1690–1695
Kaptein B, Van der Helm F (2004) Estimating muscle attachment contours by transforming geometrical bone models. J Biomech 37(3):263–273
Marra MA, Vanheule V, Fluit R, Koopman BH, Rasmussen J, Verdonschot N, Andersen MS (2015) A subject-specific musculoskeletal modeling framework to predict in vivo mechanics of total knee arthroplasty. J biomech eng 137(2):020904
Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Pike B (2001) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356(1412):1293–1322
Otake Y, Yokota F, Takao M, Fukuda N, Sugano N, Sato, Y (2016) Analysis of muscle fiber structure using clinical CT: preliminary analysis using cadaveric images. Paper presented at the proceedings of the 16th annual meeting of the international society for computer assisted orthopaedic surgery, Osaka
Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492
Pellikaan P, van der Krogt M, Carbone V, Fluit R, Vigneron L, Van Deun J, Koopman H (2014) Evaluation of a morphing based method to estimate muscle attachment sites of the lower extremity. J Biomech 47(5):1144–1150
Piazza SJ, Delp SL (2001) Three-dimensional dynamic simulation of total knee replacement motion during a step-up task. J Biomech Eng 123(6):599–606
Rasmussen J, Damsgaard M, Christensen ST, Surma E (2002) Design optimization with respect to ergonomic properties. Struct Multidiscip Optim 24(2):89–97
Reinbolt JA, Fox MD, Schwartz MH, Delp SL (2009) Predicting outcomes of rectus femoris transfer surgery. Gait Posture 30(1):100–105
Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721
Styner M, Lee J, Chin B, Chin M, Commowick O, Tran H, Warfield S (2008) 3D segmentation in the clinic: a grand challenge II—MS lesion segmentation. Midas J 2008:1–6
Tokuda J, Fischer GS, Papademetris X, Yaniv Z, Ibanez L, Cheng P, Golby AJ (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot Comput Assist Surg 5(4):423–434
Ungi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181–186
Van der Helm FC, Veeger H, Pronk G, Van der Woude L, Rozendal R (1992) Geometry parameters for musculoskeletal modelling of the shoulder system. J Biomech 25(2):129–144
Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y (2013) Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. Proceeding international conference on medical image computing and computer-assisted intervention (MICCAI 2013), LNCS 8150, Springer, Berlin, Heidelberg, pp 190–197
Yokota F, Takaya M, Okada T, Takao M, Sato Y (2012) Automated muscle segmentation from 3D CT data of the hip using hierarchical multi-atlas method. Paper presented at the proceedings of the 12th annual meeting of the international society for computer assisted orthopaedic surgery, Seoul
Acknowledgements
This research was supported by MEXT/JSPS KAKENHI 26108004, JST PRESTO 20407, AMED/ETH the strategic Japanese-Swiss cooperative research program, and R21EB020113-01 from National Institutes of Health. The authors gratefully acknowledge the contributions of Drs. Ryan Murphy, Stephen Belkoff, Demetries Boston (Johns Hopkins University) to the cadaver experiments.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study has been approved by the Institutional Review Board of Osaka University Hospital (No. 15538) and Nara Institute of Science and Technology (No. 2016-I-20).
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Fukuda, N., Otake, Y., Takao, M. et al. Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers. Int J CARS 12, 733–742 (2017). https://doi.org/10.1007/s11548-016-1519-8
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DOI: https://doi.org/10.1007/s11548-016-1519-8