血管弯曲度评估算法研究进展

Progress in vessel tortuosity evaluation algorithm research

血管弯曲度,即血管在空间中的曲折程度,与多种疾病,如阿尔茨海默病、动脉粥样硬化等,有密切关系,是疾病诊断的一项重要指标。因此,准确评估血管弯曲度对于临床诊断、治疗和预后具有重要价值。随着医学影像技术的发展,血管弯曲度评估的研究取得了显著进展。本文综述了血管弯曲度评估的主要方法和技术,并对它们的优缺点进行了比较和评价。文中涉及基于距离、角度、曲率、深度学习等技术的血管弯曲度评估算法。基于距离的血管弯曲度评估算法具有计算简单、直观的特点。基于角度和曲率的血管弯曲度评估算法则通过分析血管中心线上的角度和曲率变化来评估弯曲度,具有局部特征捕捉的优势。此外,近年来深度学习算法应用于血管弯曲度评估中,这种算法具有能够自动学习和识别复杂的血管结构的优点。这些算法可以较为准确、稳定地评估血管弯曲度,为临床实践提供了有效的工具。然而,目前的研究仍然存在一些局限性,如算法的通用性、个体差异、实时性等问题尚待解决。继续研究血管弯曲度评估技术,进一步完善血管弯曲度评估算法,以更好地满足临床需求,改善疾病诊断的精确性和效率,对于推动相关领域的发展具有重要意义。本文旨在为未来的研究提供方向,为血管弯曲度评估研究的进一步发展提供有价值的参考和启示。

Vessel tortuosity,referring to the degree of twisting and turning of vessels in space,is closely associated with various diseases,including Alzheimer disease,atherosclerosis,and others,serving as a crucial indicator for disease diagnosis.Consequently,the accurate evaluation of vessel tortuosity is of significant value for clinical diagnosis,treatment,and prognosis.With the advancement of medical imaging technology,research on the evaluation of vessel tortuosity has made remarkable progress.This article provides a comprehensive review of the primary methods and technologies for evaluating vessel tortuosity and evaluates their respective strengths and weaknesses.The discussion encompasses algorithms based on distance,angle,curvature,and deep learning for evaluating vessel tortuosity.Distance⁃based algorithms are noted for their simplicity and intuitiveness.In contrast,angle and curvature⁃based algorithms,which evaluate tortuosity by analyzing changes in angle and curvature along the vessel’s centerline,excel in capturing local features.Moreover,the emergence of deep learning algorithms in recent years,characterized by their ability to automatically learn and recognize complex vessel structures,has enriched the field.These algorithms contribute to the accurate and stable evaluation of vessel tortuosity,offering effective tools for clinical practice.However,current research is not without its limitations,with issues related to the universality of algorithms,individual variations,and real⁃time processing yet to be addressed.The ongoing exploration and refinement of vessel tortuosity evaluation techniques and algorithms to better meet clinical needs and enhance the precision and efficiency of disease diagnosis are essential for advancing the field.This article aims to guide future research,offering valuable insights and references for the continued development of vessel tortuosity evaluation studies.