Automatic assimilation of particle velocimetry data into computational blood flow models

随着世界范围内糖尿病患病率的不断增加，糖尿病视网膜病变（DR）仍然是糖尿病患者最常见的血管并发症。尽管在治疗方面取得了进展，但DR仍然是全世界工作年龄人群视力丧失的主要原因。为了更好地了解如何预防糖尿病性眼部并发症导致的视力丧失，需要新的方法。在这种情况下，早期DR检测是一种有希望避免视网膜损伤和视力丧失的方法。以往的研究发现，糖尿病眼的血流变化发生在血管病变出现之前，这是目前诊断的主要临床体征。因此，我们假设可以通过监测早期血流变化来实现DR的早期检测。在最近的一项研究中，我们提出了有史以来第一个非侵入性的方法来评估视网膜中央凹旁区域的血流（对中央锐利视力至关重要）。我们通过比较计算血流模型预测的血流速度与通过跟踪视网膜扫描中可见的血细胞聚集（BCA）获得的体内血流速度测量结果，验证了我们的方法。尽管模型估计的准确性，但在DR组与对照组之间的比较中，我们无法达到统计学意义。我们假设这主要是由我们的流动模型定义中的一个重要限制引起的：使用非患者特定的流动边界条件，我们预计这在两组中有很大的不同。在当前的建议中，我们的目标是通过BCA速度同化估计模型边界条件来解决这种模型限制。这种方法将允许我们基于一小组粒子跟踪实验读数来定义特定于患者的模型。提出的方法是基于构造一个约束优化问题，通过最小化模型输出速度和细胞跟踪数据之间的不匹配来诊断模型解域（在这种情况下为流体压力和速度）。作为项目的一部分，我们将开发一个软件工具，将之前的数值程序应用于由眼睛中央凹旁区域的高分辨率视网膜图像和BCA跟踪获得的体内血液速度测量重建的微血管网络流动模型。前者将依赖于作者先前发表的方法。视网膜图像和细胞跟踪数据已经存在于项目合作伙伴的实验室中，并成功地应用于先前的出版物中。

As the worldwide prevalence of diabetes mellitus continues to increase, diabetic retinopathy (DR) remains the most common vascular complication in diabetic patients. Despite advances in treatment, DR remains a leading cause of visual loss in working-aged people worldwide. New approaches are necessary in order to better understand how to prevent vision loss from diabetic eye complications.In this context, early DR detection is a promising approach to avoiding retinal damage and vision loss. Previous studies have found changes in blood flow in the diabetic eyes preceding the appearance of vascular lesions, which are currently the main clinical signs for diagnosis. Therefore, we hypothesise that DR early detection can be achieved via monitoring of early blood flow changes.In a recent study, we proposed the first-ever non-invasive method for the assessment of blood flow in the parafoveal region of the retina (of paramount importance for central sharp vision). We validated our approach by comparing the blood velocity predicted by our computational flow models with in vivo blood velocity measurements obtained by tracking blood cell aggregations (BCA) visible in the retinal scans. Despite the accuracy in the model estimates, we could not achieve statistical significance in the comparison between the DR and control groups. We hypothesise that this is primarily caused by an important limitation in the definition of our flow models: the use of non-patient-specific flow boundary conditions, which we anticipate differing substantially in both groups.In the current proposal we aim to address this model limitation by estimating the model boundary conditions via BCA velocity assimilation. This approach will allow us to define patient-specific models based on a small set of particle tracking experimental readouts. The proposed approach is based on constructing a constrained optimisation problem, whereby the model solution fields (fluid pressure and velocity in this case) are diagnosed by minimising a measure of the mismatch between the model output velocity and the cell tracking data.As part of the project, we will develop a software tool that applies the previous numerical procedure to microvascular network flow models reconstructed from high resolution retinal images of the parafoveal region of the eye and in vivo blood velocity measurements obtained by BCA tracking. The former will rely on methodology previously published by the authors. Both retinal images and cell tracking data already exist at the laboratory of a project partner and were successfully employed in a previous publication.