Imaging of Sarcopenia

A Novel MRI-Based Paravertebral Muscle Quality (PVMQ) Score for Evaluating Muscle Quality and Bone Quality: A Comparative Study with the VBQ Score

Purpose

This study aims to develop a novel MRI-based paravertebral muscle quality (PVMQ) score for assessing muscle quality and to investigate its correlation with the degree of fat infiltration (DFF) and the vertebral bone quality (VBQ) score of paravertebral muscles. Additionally, the study compares the effectiveness of the PVMQ score and the VBQ score in assessing muscle quality and bone quality.

Methods

PVMQ scores were derived from the ratio of paravertebral muscle signal intensity (SI) to L3 cerebrospinal fluid SI on T2-weighted MRI. Image J software assessed paravertebral muscle cross-sectional area (CSA) and DFF. Spearman rank correlation analyses explored associations between PVMQ, VBQ scores, DFF, and T-scores in both genders. Receiver operating characteristic (ROC) curves compared PVMQ and VBQ scores' effectiveness in distinguishing osteopenia/osteoporosis and high paraspinal muscle DFF.

Results

In this study of 144 patients (94 females), PVMQ scores were significantly higher in osteoporosis and osteopenia groups compared to normals, with variations observed between genders (P < 0.05). PVMQ showed stronger positive correlation with VBQ scores and DFF in females than males (0.584 vs 0.445, 0.579 vs 0.528; P < 0.01). ROC analysis favored PVMQ over VBQ for low muscle mass in both genders (AUC = 0.767 vs 0.718, 0.793 vs 0.718). VBQ was better for bone mass in males (0.737/0.865 vs 0.691/0.858), whereas PVMQ excelled for females (0.808/0.764 vs 0.721/0.718).

Conclusion

The novel PVMQ score provides a reliable assessment of paravertebral muscle quality and shows a strong correlation with VBQ scores and DFF, particularly in females. It outperforms VBQ scores in evaluating muscle mass and offers valuable insights for assessing bone mass in females. These findings underscore the potential of the PVMQ score as a dual-purpose tool for evaluating both muscle and bone health, informing future research and clinical practice.

Biomarkers of Body Composition

The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.

Ultrasonography for Assessment of Sarcopenia: A Primer

The human skeletal muscle has a pivotal role in preserving health by maintaining mobility, balance, and metabolic homeostasis. Significant muscle loss as a part of aging and accelerated by disease leads to sarcopenia which becomes an important predictor of quality of life in older persons. Therefore, clinical screening for sarcopenia and validation by precise qualitative and quantitative measurement of skeletal muscle mass (MM) and function is at the center-stage of translational research. Many imaging modalities are available, each having their strengths and limitations, either in interpretation, technical processes, time constraints, or expense. B-mode ultrasonography (US) is a relatively novel approach to evaluating muscle. It can measure several parameters such as MM and architecture simultaneously including muscle thickness, cross-sectional area, echogenicity, pennate angle, and fascicle length. It can also evaluate dynamic parameters like muscle contraction force and muscle microcirculation. US has not gained global attention due to a lack of consensus on standardization and diagnostic threshold values to diagnose sarcopenia. However, it is an inexpensive and widely available technique with clinical applicability. The ultrasound-derived parameters correlate well with strength and functional capacity and provide potential prognostic information. Our aim is to present an update on the evidence-based role of this promising technique in sarcopenia, its advantages over the existing modalities, and its limitations in actual practice with the hope that it may emerge as the "stethoscope" for community diagnosis of sarcopenia.