Multiscale Monte Carlo methods for multiparametric biomedical image processing and analysis

多参数成像的最新进展对改进的疾病诊断方案的发展做出了重大贡献。然而，考虑到这种多参数成像数据的数量和复杂性，以及与保持合理的采集时间相关的图像质量权衡，研究科学家和临床医生通常很难以有意义和有效的方式解释和分析采集的数据。提出的研究计划的主要目标是开发用于多参数生物医学图像处理和分析的多尺度蒙特卡罗方法，并将这些方法整合到临床可行的平台中，供放射科医生、临床医生和临床研究人员使用。将研究三个重要问题：1)图像建模，2)图像处理，3)图像分析。这些问题中的每一个都将在一个有效的多尺度马尔可夫链蒙特卡罗(MCMC)框架内进行研究。 所提出的研究对多参数生物医学图像处理和分析领域具有重要影响。正在开发的图像处理算法有可能显著提高成像数据的分辨率、对比度和信噪比。这种方法将对临床成像设备产生重大影响，因为它允许在保持图像质量的同时总体上更快地采集时间，以及减少放射成像技术中的辐射剂量，以提高患者的安全性。此外，正在研究的图像分析方法有可能显著提高多参数成像数据的定量分析的速度和精度，这将极大地帮助临床医生诊断疾病和研究科学家更好地了解疾病的原因和后果。开发的技术将通过与桑尼布鲁克健康科学中心等健康机构以及爱克发医疗保健和旋风医疗系统公司等公司的积极合作，直接转移到医疗行业。

Recent advancements in multi-parametric imaging have contributed significantly to the development of improved disease diagnosis protocols. However, given the quantity and complexities of such multiparametric imaging data, along with image quality tradeoffs associated with maintaining reasonable acquisition times, it is often difficult for research scientists and clinicians to interpret and analyze the acquired data in a meaningful and efficient fashion. The main objectives of the proposed research program is to develop multiscale Monte Carlo methods for multiparametric biomedical image processing and analysis, and to incorporate such methods into a clinically-viable platform for use by radiologists, clinicians, and clinical researchers. Three important issues will be investigated: 1) Image modeling, 2) Image processing, and 3) Image analysis. Each of these issues will be investigated within an efficient multiscale Markov-chain Monte Carlo (MCMC) framework. The proposed research can have a significant impact in the area of multiparametric biomedical image processing and analysis. The image processing algorithms being developed have the potential to significantly improve resolution, contrast, and signal-to-noise ratios in imaging data. Such methods would have significant effect on clinical imaging devices, as it allows for overall faster acquisition times while maintaining image quality, as well as reduced radiation dosage in radiographic imaging technologies to improve patient safety. Furthermore, the image analysis methods being researched have the potential to significantly improve both the speed and accuracy of quantitative analysis of multiparametric imaging data, which can greatly aid clinicians in disease diagnosis and research scientists in better understanding the causes and effects of disease. The developed technologies will be transferred directly into the healthcare industry through active collaborations with health institutions such as Sunnybrook Health Sciences Centre, and companies such as Agfa Healthcare and Tornado Medical Systems.