Bone-Muscle-Fat Interactions and Perfusive Evidence of Inflammation in Non-Overweight Postmenopausal Women With Early Knee Oa

Liu, S.; Anwari, Vahid; Yazdankhah, N.; Tam, Kwok‐Kwan ‐K; Whyte, R.; Ha, E.; Johnston, James D.; Naraghi, Ali; Mohankumar, Rakesh; Veit-Haibach, Patrick; Sussman, Marshall S.; Probyn, L.; Wong, Andy Kin On

doi:10.1016/j.ostima.2022.100030

Cited by 2 publications

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“…In the past 2 decades, peripheral quantitative computed tomography (pQCT) and high resolution peripheral quantitative computed tomography (HR-pQCT) have emerged as essential technologies for segmentation and quantification of bone, muscle and adipose tissue properties at the diaphyseal regions of the limbs. Segmentation of hard and soft tissues in pQCT and HR-pQCT imaging has been used to assess the effects of type-2 diabetes mellitus (T2DM) (Starr et al, 2018), osteoporosis (Simon et al, 2022) and osteoarthritis (Chen et al, 2018), to establish measures for characterizing sex-, ethnic-, site-, and age-related outcomes (Gabel et al, 2018), to study the effect of exercise on the muscle and fat cross-sectional areas (Rowe et al, 2019), and in studies of aging and age-related diseases (Chow et al, 2022;Liu et al, 2022). A challenge in pQCT-based segmentation is subject movement and the associated motion artifacts.…”

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confidence: 99%

Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography

et al. 2022

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Accurate and reproducible tissue identification is essential for understanding structural and functional changes that may occur naturally with aging, or because of a chronic disease, or in response to intervention therapies. Peripheral quantitative computed tomography (pQCT) is regularly employed for body composition studies, especially for the structural and material properties of the bone. Furthermore, pQCT acquisition requires low radiation dose and the scanner is compact and portable. However, pQCT scans have limited spatial resolution and moderate SNR. pQCT image quality is frequently degraded by involuntary subject movement during image acquisition. These limitations may often compromise the accuracy of tissue quantification, and emphasize the need for automated and robust quantification methods. We propose a tissue identification and quantification methodology that addresses image quality limitations and artifacts, with increased interest in subject movement. We introduce a multi-atlas image segmentation (MAIS) framework for semantic segmentation of hard and soft tissues in pQCT scans at multiple levels of the lower leg. We describe the stages of statistical atlas generation, deformable registration and multi-tissue classifier fusion. We evaluated the performance of our methodology using multiple deformable registration approaches against reference tissue masks. We also evaluated the performance of conventional model-based segmentation against the same reference data to facilitate comparisons. We studied the effect of subject movement on tissue segmentation quality. We also applied the top performing method to a larger out-of-sample dataset and report the quantification results. The results show that multi-atlas image segmentation with diffeomorphic deformation and probabilistic label fusion produces very good quality over all tissues, even for scans with significant quality degradation. The application of our technique to the larger dataset reveals trends of age-related body composition changes that are consistent with the literature. Because of its robustness to subject motion artifacts, our MAIS methodology enables analysis of larger number of scans than conventional state-of-the-art methods. Automated analysis of both soft and hard tissues in pQCT is another contribution of this work.

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