Simulation of phase contrast angiography for renal arterial models

High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations

BACKGROUND AND OBJECTIVE

High-quality vascular modeling is crucial for blood flow simulations, i.e., computational fluid dynamics (CFD). As without an accurate geometric representation of the smooth vascular surface, it is impossible to make meaningful blood flow simulations. The purpose of this work is to develop high-quality vascular modeling and modification method for blood flow computations.

METHODS

We develop a new technique for the accurate geometric modeling and modification of vasculatures using implicit extrusion surfaces (IES). In the proposed method, the skeleton of the vascular structure is subdivided into short curve segments, each of which is then represented implicitly locally as the intersection of two mutually orthogonal implicit surfaces defined by distance functions. A set of contour points is extracted and fitted with an implicit curve for accurately specifying the vessel cross-section profile, which is then extruded locally along the skeleton to fill the gaps between two vascular tube cross sections. We also present a new implicit geometric editing technique to modify the constructed vascular model with pathology for virtual stenting.

RESULTS

Experimental results and validations show that accurate vascular models with highly smooth surfaces can be generated by the proposed method. In addition, we conduct some blood flow simulations to indicate the effectiveness of proposed method for hemodynamic simulations.

CONCLUSIONS

The proposed technique can achieve precise geometric models of vasculatures with any required degree of smoothness for reliable blood flow simulations.

A Multi-Layer Perceptron Network for Perfusion Parameter Estimation in DCE-MRI Studies of the Healthy Kidney

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging technique which helps in visualizing and quantifying perfusion—one of the most important indicators of an organ’s state. This paper focuses on perfusion and filtration in the kidney, whose performance directly influences versatile functions of the body. In clinical practice, kidney function is assessed by measuring glomerular filtration rate (GFR). Estimating GFR based on DCE-MRI data requires the application of an organ-specific pharmacokinetic (PK) model. However, determination of the model parameters, and thus the characterization of GFR, is sensitive to determination of the arterial input function (AIF) and the initial choice of parameter values. Methods: This paper proposes a multi-layer perceptron network for PK model parameter determination, in order to overcome the limitations of the traditional model’s optimization techniques based on non-linear least-squares curve-fitting. As a reference method, we applied the trust-region reflective algorithm to numerically optimize the model. The effectiveness of the proposed approach was tested for 20 data sets, collected for 10 healthy volunteers whose image-derived GFR scores were compared with ground-truth blood test values. Results: The achieved mean difference between the image-derived and ground-truth GFR values was 2.35 mL/min/1.73 m2, which is comparable to the result obtained for the reference estimation method (−5.80 mL/min/1.73 m2). Conclusions: Neural networks are a feasible alternative to the least-squares curve-fitting algorithm, ensuring agreement with ground-truth measurements at a comparable level. The advantages of using a neural network are twofold. Firstly, it can estimate a GFR value without the need to determine the AIF for each individual patient. Secondly, a reliable estimate can be obtained, without the need to manually set up either the initial parameter values or the constraints thereof.

A novel vision-based system for quantitative analysis of abdominal aortic aneurysm deformation

BACKGROUND

In clinical diagnostics, combination of different imaging techniques is applied to assess spatial configuration of the abdominal aortic aneurysm (AAA) and deformation of its wall. As deformation of aneurysm wall is crucial parameter in assessing wall rupture, we aimed to develop and validate a Non-Invasive Vision-Based System (NIVBS) for the analysis of 3D elastic artificial abdominal aortic models. 3D-printed elastic AAA models from four patients were applied for the reconstruction of real hemodynamic. During experiments, the inlet boundary conditions included the injection volume and frequency of pulsation averaged from electrocardiography traces. NIVBS system was equipped with nine cameras placed at a constant distance to record wall movement from 360^o angle and a dedicated set of artificial lights providing coherent illumination. Additionally, self-prepared algorithms for image acquisition, processing, segmentation, and contour detection were used to analyze wall deformation. Finally, the shape deformation factor was applied to evaluate aorta's deformation. Experimental results were confronted with medical data from AngioCT and 2D speckle-tracking echocardiography (2DSTE).

RESULTS

Image square analyses indicated that the optimal distance between the camera's lens and the investigated object was in the range of 0.30-0.35 m. There was approximately 1.44% difference observed in aneurysm diameters between NIVBS (86.57 ± 5.86 mm) and AngioCT (87.82 ± 6.04 mm) (p = 0.7764). The accuracy of developed algorithm for the reconstruction of the AAA deformation was equal to 98.56%. Bland-Altman analysis showed that the difference between clinical data (2DSTE) and predicted wall deformation (NIVBS) for all patients was 0.00 mm (confidence interval equal to 0.12 mm) for aneurysm size, 0.01 mm (confidence interval equal to 0.13 mm) and 0.00 mm (confidence interval equal to 0.09 mm) for the anterior and posterior side, as well as 0.01 mm (confidence interval equal to 0.18 mm) and 0.01 mm (confidence interval equal to 0.11 mm) for the left and right side. The optimal range of camera's lens did not affect acquired values.

CONCLUSIONS

The NIVBS with proposed algorithm that reconstructs the pressure from surrounding organs is appropriate to analyze the AAAs in water environment. Moreover, NIVBS allowed detailed quantitative analysis of aneurysm sac wall deformation.