STTR Phase I: A Non-invasive Image­‐based Skeletal Muscle Analytics Tool

这项小型企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力是提供一种揭示有关骨骼肌力量和健康的重要信息的新工具。肌肉无力是我们社会普遍存在的问题，包括关节疾病患者、老年人、肥胖者和神经肌肉疾病患者。所有这些问题都会以不同的和非的方式影响全身的肌肉？直观的方式;然而，到目前为止，市场上还没有一种技术可以通过肌肉来定量测量肌肉大小。肌肉的基础。最初的目标客户是运动员组织，他们的目标是利用该技术为运动员的训练提供信息，以提高成绩，同时为预测受伤易感性提供更多的定量指标，并使回报-? ？]运动决策。然而，随着进入运动员领域，最终目标是将这项技术推进到可以更广泛地用作临床诊断工具的程度，用于预防、诊断和治疗与肌肉骨骼疾病和活动相关的健康状况，这将产生广泛的整体社会影响。拟议的项目将提供一个重大的进步形象-？基于建模工具，以允许高-？运动员整个下肢肌肉的通量成像和分割。目前，物理治疗师、运动教练和力量训练教练只有非常钝的工具来评估每个人。fs的力量。因此，培训和康复方法是通过经验和试错来发展的。同样，由于工具非常钝，目前还不知道运动员的最佳肌肉特征是什么。这里提出的技术通过利用图像来解决这些问题。模型管道来量化整个下肢的肌肉大小。初步证据表明，这些新的测量方法为表现型运动员提供了全新的指标，确定了哪些肌肉特征导致了最大的表现（例如，速度，跳跃高度，敏捷性）。目前在学术环境中开发的技术在市场翻译方面的潜力有限，因为：(i)它需要非常专业的磁共振成像协议，（ii）分割时间太长，无法实现商业可行性。拟议的活动在开发解决这两个问题的创新方法方面具有很高的智力价值，最终将其从研究工具转变为商业上可行的服务。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to provide a new tool revealing important information regarding skeletal muscle strength and health. Muscle weakness is a pervasive problem across our society, including people with joint disease, aging people, obese people, and people with neuromuscular disorders. All these problems affect muscles across the body in different and non--?]intuitive ways; however, to date there has not been marketed technology that allows for quantitative measurement of muscle size on a muscle--?]by--?]muscle basis. The initial targeted customers are athlete organizations, with the goal of using the technology to both inform training of athletes to improve performance as well as to provide more quantitative metrics for predicting injury susceptibility and make return--?]to--?]sport decisions. However, following penetration into the athlete sector, the ultimate goal is to advance the technology to the point where it can more broadly to used clinically as a diagnostic tool to be used in the prevention, diagnosis, and treatment of health conditions related to musculoskeletal disease and mobility, which will have wide spanning overall societal impact.The proposed project will provide a major advancement in image--?]based modeling tools in order to allow high--?]throughput imaging and segmentation of muscles of the entire lower limb of athletes. Currently, physical therapists, athletic trainers and strength and conditioning couches only have very blunt tools to assess each individual?fs strength. Therefore, training and rehabilitative approaches are developed via experience and trial and error. Similarly, because the tools have been very blunt, it is currently unknown what the optimal muscle characteristics of an athlete would be. The technology proposed here solves these problems by making using of an image--?]to--?]model pipeline to quantify muscle size across in the entire lower extremity. Preliminary evidence demonstrates that these new measurements provide entirely new metrics for phenotyping athletes, identifying which muscle profiles result in maximal performance (e.g., speed, jump height, agility). The current technology developed in the academic environment is limited in its potential to market translation because: (i) it requires a very specialized magnetic resonance imaging protocol, and (ii) segmentation time is prohibitive for commercial viability. The proposed activities have high intellectual merit in developing innovative approaches to solve both problems, ultimately transforming this from a research tool to a commercially viable service.