HR-pQCT for the Evaluation of Muscle Quality and Intramuscular Fat Infiltration in Ageing Skeletal Muscle

An experimental study for quantitative assessment of fatty infiltration and blood flow perfusion in quadriceps muscle of rats using IDEAL-IQ and BOLD-MRI for early diagnosis of sarcopenia

BACKGROUND

Severe sarcopenia may result in severe disability. Early diagnosis is currently the key to enhancing the treatment of sarcopenia, and there is an urgent need for a highly sensitive and dependable tool to evaluate the course of early sarcopenia in clinical practice. This study aims to investigate longitudinally the early diagnosability of magnetic resonance imaging (MRI)-based fat infiltration and blood flow perfusion technology in sarcopenia progression.

METHODS

48 Sprague-Dawley rats were randomly assigned into six groups that were based on different periods of dexamethasone (DEX) injection (0, 2, 4, 6, 8, 10 days). Multimodal MRI was scanned to assess muscle mass. Grip strength and swimming exhaustion time of rats were measured to assess muscle strength and function. Immunofluorescence staining for CD31 was employed to assess skeletal muscle capillary formation, and western blot was used to detect vascular endothelial growth factor-A (VEGF-A) and muscle ring finger-1 (MuRF-1) protein expression. Subsequently, we analyzed the correlation between imaging and histopathologic parameters. A receiver operating characteristic (ROC) analysis was conducted to assess the effectiveness of quantitative MRI parameters for discriminating diagnosis in both pre- and post-modeling of DEX-induced sarcopenic rats.

RESULTS

Significant differences were found in PDFF, R2* and T2 values on day 2 of DEX-induction compared to the control group, occurring prior to the MRI-CSA values and limb grip strength on day 6 of induction and swimming exhaustion time on day 8 of induction. There is a strong correlation between MRI-CSA with HE-CSA values (r = 0.67; p < 0.001), oil red O (ORO) area with PDFF (r = 0.67; p < 0.001), microvascular density (MVD) (r = -0.79; p < 0.001) and VEGF-A (r = -0.73; p < 0.001) with R2*, MuRF-1 with MRI-CSA (r = -0.82; p < 0.001). The AUC of PDFF, R2*, and T2 values used for modeling evaluation are 0.81, 0.93, and 0.98, respectively.

CONCLUSION

Imaging parameters PDFF, R2*, and T2 can be used to sensitively evaluate early pathological changes in sarcopenia. The successful construction of a sarcopenia rat model can be assessed when PDFF exceeds 1.25, R2* exceeds 53.85, and T2 exceeds 33.88.

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

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.