Blood Flow Simulation in Patient-Specific Segmented Hepatic Arterial Tree

Computational exploration of injection strategies for improving medicine distribution in the liver

BACKGROUND AND OBJECTIVES

The liver, a vital metabolic organ, is always susceptible to various diseases that ultimately lead to fibrosis, cirrhosis, acute liver failure, chronic liver failure, and even cancer. Optimal and specific medicine delivery in various diseases, hepatectomy, shunt placement, and other surgical interventions to reduce liver damage, transplantation, optimal preservation, and revival of the donated organ all rely on a complete understanding of perfusion and mass transfer in the liver. This study aims to simulate the computational fluid dynamics of perfusion and the temporal-spatial distribution of a medicine in a healthy liver to evaluate the hemodynamic characteristics of flow and medicine transport with the purpose of more effective liver treatment.

METHODS

Patient-specific geometries of parenchyma and hepatic artery, portal vein, and hepatic vein vessels of a healthy liver were segmented and reconstructed from the abdominal computed tomography scan images. Mesh was generated for the comprehensive combined model using unstructured tetrahedral elements. Transient pressure values were applied as boundary conditions at the portal vein and hepatic artery inlets, and pressure outlet boundary condition was assumed at the hepatic vein outlet. Medicine injection was done through the portal vein. The liver parenchyma was assumed to be a porous medium. Finally, computational fluid dynamics (CFD) simulation was performed to investigate blood perfusion, medicine distribution, and saturation time.

RESULTS

The velocity parameter values calculated for the hepatic artery, portal vein, and hepatic vein vessels were consistent with the physiological ranges. The mass flow rate was higher in the portal vein than in the hepatic artery, which was consistent with high perfusion through the portal vein. The portal pressure gradient was 8.53 mmHg. From a pharmacokinetic viewpoint, medicine distribution in porous tissue was a heterogeneous process. Medicine distribution was higher at end-diastolic pressure than at peak-systolic pressure which showed the influence of hepatic artery flow. The tissue was saturated faster at first 40 s and with decreasing porosity, saturation time decreased, and distribution improved.

CONCLUSION

The right lobe included a higher number of vascular terminals due to its larger volume, and the flow rate was higher in this lobe compared to the left lobe. This showed the significant effect of the right lobe on the overall function of the body. Recirculation flow zones along hepatic artery and portal vein branches emphasized the sensitivity of downstream vessels. Rotational flow and potential vortex formation at the hepatic vein outlet may indicate a risk of plaque and clot formation in this region. The heterogeneous distribution of medicine indicated the importance of injection time in treating liver diseases. The percentage of tissue porosity affected the saturation time, so adjusting the medicine dose and injection time could be challenging in treatments.

A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disease that impacts a child's way of behavior and social communication. In early childhood, children with ASD typically exhibit symptoms such as difficulty in social interaction, limited interests, and repetitive behavior. Although there are symptoms of ASD disease, most people do not understand these symptoms and therefore do not have enough knowledge to determine whether or not a child has ASD. Thus, early detection of ASD children based on accurate diagnosis model based on Artificial Intelligence (AI) techniques is a critical process to reduce the spread of the disease and control it early. Through this paper, a new Diagnostic Autism Spectrum Disorder (DASD) strategy is presented to quickly and accurately detect ASD children. DASD contains two layers called Data Filter Layer (DFL) and Diagnostic Layer (DL). Feature selection and outlier rejection processes are performed in DFL to filter the ASD dataset from less important features and incorrect data before using the diagnostic or detection method in DL to accurately diagnose the patients. In DFL, Binary Gray Wolf Optimization (BGWO) technique is used to select the most significant set of features while Binary Genetic Algorithm (BGA) technique is used to eliminate invalid training data. Then, Ensemble Diagnosis Methodology (EDM) as a new diagnostic technique is used in DL to quickly and precisely diagnose ASD children. In this paper, the main contribution is EDM that consists of several diagnostic models including Enhanced K-Nearest Neighbors (EKNN) as one of them. EKNN represents a hybrid technique consisting of three methods called K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Chimp Optimization Algorithm (COA). NB is used as a weighed method to convert data from feature space to weight space. Then, COA is used as a data generation method to reduce the size of training dataset. Finally, KNN is applied on the reduced data in weight space to quickly and accurately diagnose ASD children based on new training dataset with small size. ASD blood tests dataset is used to test the proposed DASD strategy against other recent strategies [1]. It is concluded that the DASD strategy is superior to other strategies based on many performance measures including accuracy, error, recall, precision, micro_average precision, macro_average precision, micro_average recall, macro_average recall, F1-measure, and implementation-time with values equal to 0.93, 0.07, 0.83, 0.82, 0.80, 0.83, 0.79, 0.81, 0.79, and 1.5 s respectively.

Mechanisms of the ascites volume differences between patients receiving a left or right hemi-liver graft liver transplantation: From biofluidic analysis

BACKGROUND AND OBJECTIVE

Post-transplant refractory ascites (RA) is common in patients receiving living donor liver transplantation (LDLT) using a left hemi-liver graft than in those using a right hemi-liver graft. However, there is currently no clear mechanism explaining the effect of grafts on ascites drainage. The purpose of this study is to analyze the values of blood flow parameters in the portal vein under different grafts using computational fluid dynamics (CFD) to interpret the relationship between portal pressure values with ascites drainage.

METHODS

In this work, ascites drainage was counted in 30 patients who underwent left-sided liver transplantation and 26 patients who underwent right-sided liver transplantation. The portal vein flow models of the transplanted liver under different flow rates were established based on computed tomography (CT) images and finite element theory. Ascites drainage and blood flow parameters were qualitatively compared.

RESULTS

The results show that the ascites drained from patients who received LDLT with a left hemi-liver is three times as that with a right hemi-liver. The simulation results show that the coefficient of the pressure-velocity curve of the left-liver is 1.7 times of the right-liver under the same hydrodynamic conditions, which qualitatively agrees with the clinical data. Moreover, the streamline of the transplanted left liver shows more vortexes compared with the right liver, which is a major reason for the left liver's higher pressure value.

CONCLUSION

This clinical phenomenon is reproduced and comprehensively explained by the hemodynamic parameters of the portal vein. This work establishes the relationship between portal pressure values and floating water drainage, and offers a new way for physicians to predict postoperative risks intuitively.

Computational Fluid Dynamics Modeling of Liver Radioembolization: A Review

Yttrium-90 radioembolization (RE) is a widely used transcatheter intraarterial therapy for patients with unresectable liver cancer. In the last decade, computer simulations of hepatic artery hemodynamics during RE have been performed with the aim of better understanding and improving the therapy. In this review, we introduce the concept of computational fluid dynamics (CFD) modeling with a clinical perspective and we review the CFD models used to study RE from the fluid mechanics point of view. Finally, we show what CFD simulations have taught us about the hemodynamics during RE, the current capabilities of CFD simulations of RE, and we suggest some future perspectives.

Multiscale Computational Fluid Dynamics Modeling for Personalized Liver Cancer Radioembolization Dosimetry

Yttrium-90 (90Y) radioembolization is a minimally invasive procedure increasingly used for advanced liver cancer treatment. In this method, radioactive microspheres are injected into the hepatic arterial bloodstream to target, irradiate, and kill cancer cells. Accurate and precise treatment planning can lead to more efficient and safer treatment by delivering a higher radiation dose to the tumor while minimizing the exposure of the surrounding liver parenchyma. Treatment planning primarily relies on the estimated radiation dose delivered to tissue. However, current methods used to estimate the dose are based on simplified assumptions that make the dosimetry results unreliable. In this work, we present a computational model to predict the radiation dose from the 90Y activity in different liver segments to provide a more realistic and personalized dosimetry. Computational fluid dynamics (CFD) simulations were performed in a 3D hepatic arterial tree model segmented from cone-beam CT angiographic data obtained from a patient with hepatocellular carcinoma (HCC). The microsphere trajectories were predicted from the velocity field. 90Y dose distribution was then calculated from the volumetric distribution of the microspheres. Two injection locations were considered for the microsphere administration, a lobar and a selective injection. Results showed that 22% and 82% of the microspheres were delivered to the tumor, after each injection, respectively, and the combination of both injections ultimately delivered 49% of the total administered 90Y microspheres to the tumor. Results also illustrated the nonhomogeneous distribution of microspheres between liver segments, indicating the importance of developing patient-specific dosimetry methods for effective radioembolization treatment.