Characterization of spine and torso stiffness via differentiable biomechanics

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Christos Koutras , Hamed Shayestehpour , Jesús Pérez , Christian Wong , John Rasmussen , Miguel A. Otaduy
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引用次数: 0

Abstract

We present a methodology to personalize the stiffness response of a biomechanical model of the torso and the spine. In high contrast to previous work, the proposed methodology uses controlled force–deformation data that mimic the conditions of spinal bracing for scoliosis, which leads to personalized biomechanical models that are suitable for computational brace design. The novel methodology relies on several technical contributions. First, a prototype system that includes controlled force measurement and low-dose radiographs, with low-encumbrance for its implementation in the clinical protocol. Second, a model of differentiable biomechanics of the torso and the spine, which becomes the key building block for robust parameter estimation. And third, an optimization procedure for parameter estimation from force–deformation data, which relies on differentiability of the biomechanics and the image generation process. We demonstrate the application of the methodology to a cohort of 7 subjects who underwent scoliosis check-ups, and we show quantitative validation of the estimated personalized parameters and the improvement over default parameters from the bibliography.

Abstract Image

通过可微分生物力学表征脊柱和躯干刚度
我们提出了一种方法,以个性化的刚度响应的生物力学模型的躯干和脊柱。与之前的工作形成鲜明对比的是,所提出的方法使用受控的力变形数据来模拟脊柱侧凸支撑的条件,从而产生适合计算支撑设计的个性化生物力学模型。这种新颖的方法依赖于几项技术贡献。首先,一个原型系统,包括控制力测量和低剂量的x光片,其在临床方案中的实施具有低障碍。其次,建立了躯干和脊柱的可微生物力学模型,该模型成为鲁棒参数估计的关键组成部分。第三,基于生物力学的可微性和图像生成过程,提出了基于力-变形数据的参数估计优化过程。我们展示了该方法在7名接受脊柱侧凸检查的受试者队列中的应用,并展示了估计的个性化参数的定量验证以及对参考文献中默认参数的改进。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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