Automatic assimilation of particle velocimetry data into computational blood flow models

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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.