Statistical estimation of femur micro-architecture using optimal shape and density predictors

Lekadir, Karim; Hazrati, Javad; Hoogendoorn, Corné; Taylor, Zeike A.; Rietbergen, B. van; Frangi, Alejandro F.

doi:10.1016/j.jbiomech.2015.01.002

Cited by 17 publications

(20 citation statements)

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“…Although the error on the prediction of the trabecular orientation appears to be important, the values obtained with a single template were in the same range as values reported in the studies of Hazrati and colleagues 8,19 , who relied on a large bone database.…”

Section: Discussionsupporting

confidence: 70%

“…Lekadir et al 19 used the same technique, but instead of taking the anisotropic information from a reference bone, they used a regression method to predict the anisotropic information tensor based on BV/TV and local deformations. In both studies high-resolution µCT scans were employed to obtain this information, which is not applicable in a clinical situation.…”

Section: Discussionmentioning

confidence: 99%

“…Other authors suggested using iterative FE analyses or micromechanics to predict the anisotropic information from the principal directions of the calculated stress tensor 9,27,33 . Another set of studies suggests to predict this information alternative approaches have been proposed to obtain bone anisotropy from a database of cadaveric samples 8,19 . Hazrati et al 8 chose the closest BV/TV distribution to the patient's bone in a bone database and morphed the anisotropic information from this cadaveric reference to the patient's bone.…”

Section: Introductionmentioning

confidence: 99%

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Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

et al. 2016

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Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the Bone Mineral Density (BMD) and trabecular bone architecture. However, these findings cannot directly be fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7% respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulation from clinical image datasets than isotropic model.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

et al. 2016

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“…Moreover, fabric tensors estimated through machine learning approaches will be considered (Lekadir et al 2015;Hazrati Marangalou et al 2013). In addition, we plan to apply the proposed model to trabecular bone from different skeletal sites and with different degrees of osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

Prediction of apparent trabecular bone stiffness through fourth-order fabric tensors

Moreno

Smedby

Pahr

2015

Biomech Model Mechanobiol

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The apparent stiffness tensor is an important mechanical parameter for characterizing trabecular bone. Previous studies have modeled this parameter as a function of mechanical properties of the tissue, bone density and a second-order fabric tensor, which encodes both anisotropy and orientation of trabecular bone. Although these models yield strong correlations between observed and predicted stiffness tensors, there is still space for reducing accuracy errors. In this paper we propose a model that uses fourth-order instead of second-order fabric tensors. First, the totally symmetric part of the stiffness tensor is assumed proportional to the fourth-order fabric tensor in the logarithmic scale. Second, the asymmetric part of the stiffness tensor is derived from relationships among components of the harmonic tensor decomposition of the stiffness tensor. The mean intercept length (MIL), generalized MIL (GMIL) and global structure tensor fourth-order were computed from images acquired through micro computed tomography of 264 specimens of the femur. The predicted tensors were compared to the stiffness tensors computed by using the micro finite element method (µFE), which was considered as the gold stan- dard, yielding strong correlations (R 2 above 0.962). The GMIL tensor yielded the best results among the tested fabric tensors. The Frobenius error, geodesic error and the error of the norm were reduced by applying the proposed model by 3.75%, 0.07% and 3.16%, respectively compared to the model by Zysset and Curnier (1995) with the second-order MIL tensor. From the results, fourth-order fabric tensors are a good alternative to the more expensive µFE stiffness predictions.

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“…Hu et al, 2012), use of structural models to infer tissue micro-structure (e.g. Lekadir et al, 2014Lekadir et al, , 2015Clayden et al, 2016), use of computational models to produce virtual images of unobservable features (e.g. Nørgaard et al, 2016;Lekadir et al, 2016), or computational imaging approaches that incorporate prior knowledge into image acquisition or reconstruction leading, for instance, to agile or portable imaging/sensing systems (York et al, 2011;Coskun and Ozcan, 2014).…”

Section: The Trend: From Data To Wisdom and Backmentioning

confidence: 99%

Precision Imaging: more descriptive, predictive and integrative imaging

Frangi

Taylor

Gooya

2016

Medical Image Analysis

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Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological modelbased imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.

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Statistical estimation of femur micro-architecture using optimal shape and density predictors

Cited by 17 publications

References 20 publications

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Prediction of apparent trabecular bone stiffness through fourth-order fabric tensors

Precision Imaging: more descriptive, predictive and integrative imaging

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