Computational modeling of thrombosis in cerebral aneurysms

Thrombus plays an important role in the progression, rupture and healing of cerebral aneurysms. Nowadays, cerebral aneurysms are usually treated with endovascular techniques including coiling and flow-diverting, which are targeted to cause flow stasis inside aneurysm sac and induce intra-aneurysmal thrombosis followed by shrinkage of the sac as thrombus develops. Computational fluid dynamics has been used to investigate hemodynamics inside aneurysms before and after treatment. However, most studies in literature focus only on hemodynamic parameters, neglecting the biochemistry behind thrombus formation. In this study, we proposed a new computational model coupling both hemodynamics and biochemical reactions to explain stasis-induced thrombosis in aneurysms. The model predicted a threshold response of thrombosis to shear rate, of which the critical value to trigger coagulation was found to be close to values reported in published literature. A threshold value for fibrin concentration was also identified by simulated prothrombin test in the study. The model has been verified by animal experiments. The fibrin distributions in a ligated right common carotid artery predicted by the model were in good agreement with experimental results obtained from rats. Spontaneous thrombosis in aneurysms has also been studied with the model. Larger size, narrower neck width, larger dome height and straight parent artery have been identified to favor thrombosis in aneurysm. A height-to-neck aspect ratio of 1.6 has been found to be a critical value to discriminate aneurysms with or without thrombosis. The model has also demonstrated its capability of predicting treatment outcomes of flow diversion in cerebral aneurysms, consistent with angiographic observations from clinical follow-up. This novel computational model can serve as a tool for clinicians to evaluate treatment outcome pre-operatively, facilitating the process of medical decision making and surgical planning.</p

A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms

Flow diverters, the specially designed low porosity stents, have been used to redirect blood flow from entering aneurysm, which induces flow stasis in aneurysm and promote thrombosis for repairing aneurysm. However, it is not clear how thrombus develops following flow-diversion treatment. Our objective was to develop a computation model for the prediction of stasis-induced thrombosis following flow-diversion treatment in cerebral aneurysms. We proposed a hypothesis to initiate coagulation following flow-diversion treatment. An experimental model was used by ligating rat’s right common carotid artery (RCCA) to create flow-stasis environment. Thrombus formed in RCCA as a result of flow stasis. The fibrin distributions in different sections along the axial length of RCCA were measured. The fibrin distribution predicted by our computational model displayed a trend of increase from the proximal neck to the distal tip, consistent with the experimental results on rats. The model was applied on a saccular aneurysm treated with flow diverter to investigate thrombus development following flow diversion. Thrombus was predicted to form inside the sac, and the aneurysm was occluded with only a small remnant neck remained. Our model can serve as a tool to evaluate flow-diversion treatment outcome and optimize the design of flow diverters. © 2016, International Federation for Medical and Biological Engineering

Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images

Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examination. Since collecting OCT images is relatively more expensive than fundus photographs, existing methods use multi-modal learning to complement limited OCT data with additional context from fundus images. However, the multi-modal framework requires eye-paired datasets of both modalities, which is impractical for clinical use. To address this problem, we propose a novel fundus-enhanced disease-aware distillation model (FDDM), for retinal disease classification from OCT images. Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use. Specifically, we propose a novel class prototype matching to distill disease-related information from the fundus model to the OCT model and a novel class similarity alignment to enforce consistency between disease distribution of both modalities. Experimental results show that our proposed approach outperforms single-modal, multi-modal, and state-of-the-art distillation methods for retinal disease classification. Code is available at https://github.com/xmed-lab/FDDM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

GUIDANCE ON AREA OF CREATED TEARS FOR AORTIC FENESTRATION TREATMENT BASED ON COMPUTATIONAL FLUID DYNAMICS

Aortic fenestration (AF) uses puncture and a dilation balloon to create a tear in the intimal flap, which can directly relieve ischemia syndrome and reduce hypertension in the false lumen. The selection of a dilation balloon as well as the area of the created tear applied in reality depend on clinical experience, so we aim to provide a quantitative guidance and reference for doctors to better plan the treatment of aortic fenestration. In this study, the area of the created tear was virtually enlarged to at least 10 different values for four cases including one ideal case, and a computational fluid dynamic approach was applied to simulate blood flows in the aorta. The area ratio (AR) between the created tear and entry tear was introduced to express the enlargement of the created tear. The quantitative hemodynamic results indicate that the AR should be controlled to be larger than 7.0, but not too big to obtain the best treatment for acute aortic dissection (AD) case. Additionally, we assessed that AR might also be a risk factor for the prediction of dissection propagation. </jats:p

Coronary vessel segmentation using multiresolution and multiscale deep learning

We present a coronary vessel segmentation method for X-Ray coronary angiography images using multiresolution and multiscale deep learning. Our segmentation method constructs a set of multiresolution images from an input image via bilinear interpolation, which can handle coronary vessels with uneven distribution of contrast. We incorporate Multiresolution and Multiscale Convolution Filtering into an U-Net Network, which can help to improve accuracy of segmentation results by dealing with various thickness of coronary vessels in different positions. We investigate two types of experiments of multiresolution strategy with U-Net and multiscale strategy with U-Net, respectively. Our method has been evaluated and compared both qualitatively with networks such as single U-Net, Attention U-Net, R2U-Net and R2AttU-Net, and quantitatively with 20 state-of-the-art visual segmentation methods using a benchmark X-Ray coronary angiography database. The experiments demonstrate that our segmentation method outperforms methods using each of these networks alone and these 20 methods significantly in terms of Dice Coefficient metric, which is considered as a major evaluation criteria of segmentation results

Hemodynamic modeling of leukocyte and erythrocyte transport and interactions in intracranial aneurysms by a multiphase approach

Hemodynamics has been recognized as an important factor in the development, growth, and rupture of cerebral aneurysms, and investigated by computational fluid dynamics techniques using a single phase approach. However, flow-dependent cell transport and interactions are usually ignored in single phase models, in which blood is usually treated as a single phase Newtonian fluid. For getting better insight into the underlying pathology of intracranial aneurysm, cell transport and interactions should be covered in hemodynamic studies. In the present study, a multiphase hemodynamic model incorporating cell transport and interactions was developed, in which blood was modeled as multiphase fluid having a continuous phase (plasma) and two particulate phases (erythrocytes and leukocytes). The model showed good agreement with experimental data and observations in the literature, and was applied to four patient-specific aneurysms in a pulsatile manner. Leukocyte accumulations were predicted at locations with flow disturbance and low wall shear stress. The concentrations of leukocyte at accumulation sites were found to exceed 200 to 500% of normal physiological level on three unstable aneurysms, including two ruptured aneurysms and a growing aneurysm where accumulation was observed near a daughter sac and a secondary aneurysm. This suggested that aneurysms with complex secondary flow patterns could be prone to leukocyte accumulation on the wall. As this is the first study to characterize cell transport and interactions in aneurysm hemodynamics, our model can serve as a foundation for future intracranial aneurysm models.9 page(s