Stress field within biological tissues from multiparametric Magnetic Resonance Imaging

生物组织的重塑在很大程度上是由于细胞所承受的机械应力。在许多疾病中，组织机械损伤与扰乱的机械应力分布有关，这种分布触发了生物变化。这些生物变化产生了结构变化，进而改变了机械性能和应力分布，导致了一个“恶性循环”。我相信，评估生物组织内的机械应力是理解人类基本过程的基石，并将使我们能够制定新的战略来打破这种“恶性循环”。我的研究计划的长期目标是提供新的诊断成像和建模技术，以便对肌肉骨骼或心脏组织内的机械应力进行稳健的在体评估，从而进一步预测人体组织的降解和再生。 短期目标是：1)开发根据相位MRI图像中编码的速度和波在心脏和椎间盘中的传播测量组织应变图的算法；2)从MRI扩散成像中对心力衰竭和椎间盘退变过程中的各向异性演化进行全面量化；3)开发新的算法，从逆向材料参数识别策略中推断机械应力。 稳健的机械应力估计越来越受到以下方面的关注：1)由于缺乏活体机械应力数据，生物医学研究人员开发了纯几何验证的数学模型；2)机械研究人员开发了从超弹性或粘弹性材料的位移识别机械性能的反问题的解决方案。利用所提出的诊断成像和建模技术，早期检测生物组织内细微的机械变化，越来越受到临床医生的关注，以预测病理进展并提供早期损伤生物标志物。这种向儿科骨科医生、心脏病专家和肿瘤学家的知识转移应该为评估许多疾病状况的纵向研究铺平道路。通过生成适用于医学成像设备的自动诊断算法，未来有可能进行商业应用，这将启动一项重要的工业技术转移。

Biological tissues remodeling is largely due to the mechanical stresses withstood by the cells. In many diseases, tissue mechanical damage is associated to a perturbed mechanical stress distribution that triggers biological changes. These biological changes produce structural changes that in turn modify the mechanical properties and stress distribution, resulting in a “vicious cycle”. I believe that the evaluation of the mechanical stresses within biological tissues is a cornerstone in understanding of fundamental processes in humans and would enable defining new strategies to break this “vicious cycle”. The long-term objective of my research program is to provide new diagnostic imaging and modeling technologies for the robust in vivo mechanical stresses evaluation within musculoskeletal or cardiac tissues for further prediction of human tissues degradation and regeneration. The short-term objectives are 1) the development of algorithms to measure tissue strain maps from velocity encoded in phase MRI images and wave propagation in the heart and intervertebral discs; 2) the comprehensive anisotropy evolution quantification during heart failure and intervertebral disc degeneration from MRI diffusion imaging; 3) the development of new algorithms to infer mechanical stresses from inverse materials parameters identification strategies. The robust mechanical stress estimation is of growing interest for 1) biomedical researchers developing mathematical models for which validation is purely geometrical, due to lack of in vivo mechanical stresses data; and 2) mechanical researchers developing solutions to the inverse problem of mechanical properties identification from displacements for hyperelastic or viscoelastic materials. The early detection of subtle mechanical changes within biological tissues with the proposed diagnostic imaging and modeling technologies is of growing interest for the clinicians to anticipate pathology progression and provide early damage biomarkers. This knowledge transfer to pediatric orthopaedists, cardiologists and oncologists should pave the way for longitudinal studies assessing many disease conditions. There is a potential for future commercial application by generating automated diagnostic algorithms applicable in medical imaging equipment that would initiate an important technology transfer to industry.