Image-guided patient-specific prediction of interstitial fluid flow and drug transport in solid tumors.

Tumor fluid dynamics and drug delivery simulations in solid tumors are highly relevant topics in clinical oncology. The current study introduces a novel method combining computational fluid dynamics (CFD) modeling, quantitative magnetic resonance imaging (MRI; including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted (DW) MRI), and a novel ex-vivo protocol to generate patient-specific models of solid tumors in four patients with peritoneal metastases. DCE-MRI data were analyzed using the extended Tofts model to estimate the spatial distribution of tumor capillary permeability using the Ktrans parameter. DW-MRI data analysis provided a 3D representation of drug diffusivity, and DW-MRI coupled to an ex-vivo measurement protocol informed the spatial heterogeneity of the hydraulic conductivity of tumor tissue. The patient-specific data were subsequently incorporated into a computational fluid dynamics (CFD) model to simulate individualized tumor perfusion and drug transport maps. The results on interstitial fluid flow demonstrated noticeable heterogeneity of interstitial fluid pressure and velocity within the tumor, along with heterogeneous drug penetration profiles among different tumors, even with a similar drug administration regimen.

Salavati H ，Pullens P ，Debbaut C ，Ceelen W ... -  《-》

Computational Fluid Dynamics Modeling of Material Transport Through Triply Periodic Minimal Surface Scaffolds for Bone Tissue Engineering.

Cell-laden, scaffold-based tissue engineering methods have been successfully utilized for the treatment of bone fractures and diseases, caused by factors such as trauma, tumors, congenital anomalies, and aging. In such methods, the rate of scaffold biodegradation, transport of nutrients and growth factors, as well as removal of cell metabolic wastes at the site of injury are critical fluid-dynamics factors, affecting cell proliferation and ultimately tissue regeneration. Therefore, there is a critical need to identify the underlying material transport mechanisms and factors associated with cell-seeded, scaffold-based bone tissue engineering. The overarching goal of this study is to contribute to patient-specific, clinical treatment of bone pathology. The overall objective of the work is to establish computational fluid dynamics (CFD) models: (i) to identify the consequential mechanisms behind internal and external material transport through/over porous bone scaffolds designed based on the principles of triply periodic minimal surfaces (TPMS) and (ii) to identify TPMS designs with optimal geometry and flow characteristics for the treatment of bone fractures in clinical practice. In this study, advanced CFD models were established based on ten TPMS scaffold designs for (i) single-unit internal flow analysis, (ii) single-unit external flow analysis, and (iii) cubic, full-scaffold external flow analysis, where the geometry of each design was parametrically created. The influence of several design parameters, such as surface representation iteration, wall thickness, and pore size on geometry accuracy as well as computation time, was investigated in order to obtain computationally efficient and accurate CFD models. The fluid properties (such as density and dynamic viscosity) as well as the boundary conditions (such as no-slip condition, inlet flow velocity, and pressure outlet) of the CFD models were set based on clinical/research values reported in the literature, according to the fundamentals of internal and external Newtonian flow modeling. The main fluid characteristics influential in bone regeneration, including flow velocity, flow pressure, and wall shear stress (WSS), were analyzed to observe material transport internally through and externally over the TPMS scaffold designs. Regarding the single-unit internal flow analysis, it was observed that P.W. Hybrid and Neovius designs had the highest level of not only flow pressure but also WSS. This can be attributed to their relatively flat surfaces when compared to the rest of the TPMS designs. Schwarz primitive (P) appeared to have the lowest level of flow pressure and WSS (desirable for development of bone tissues) due to its relatively open channels allowing for more effortless fluid transport. An analysis of streamline velocity exhibited an increase in velocity togther with a depiction of potential turbulent motion along the curved sections of the TPMS designs. Regarding the single-unit external flow analysis, it was observed that Neovius and Diamond yielded the highest level of flow pressure and WSS, respectively, while Schwarz primitive (P) similarly had a relatively low level of flow pressure and WSS suitable for bone regeneration. Besides, pressure buildup was observed within the inner channels of almost all the TPMS designs due to flow resistance and the intrinsic interaction between the fluid flow and the scaffold walls. Regarding the cubic (full-scaffold) external flow analysis, the Diamond and Schwarz gyroid (G) designs appeared to have a relatively high level of both flow pressure and WSS, while Schwarz primitive (P) similarly yielded a low level of flow pressure and WSS. Overall, the outcomes of this study pave the way for optimal design and fabrication of complex, bone-like tissues with desired material transport properties for cell-laden, scaffold-based treatment of bone fractures.

Coburn B ，Salary RR 《-》

Generation of personalized synthetic 3-dimensional inlet velocity profiles for computational fluid dynamics simulations of type B aortic dissection.

Computational fluid dynamics (CFD) simulations have shown promise in assessing type B aortic dissection (TBAD) to predict disease progression, and inlet velocity profiles (IVPs) are essential for such simulations. To truly capture patient-specific hemodynamic features, 3D IVPs extracted from 4D-flow magnetic resonance imaging (4D MRI) should be used, but 4D MRI is not commonly available. A new workflow was devised to generate personalized synthetic 3D IVPs that can replace 4D MRI-derived IVPs in CFD simulations. Based on 3D IVPs extracted from 4D MRI of 33 TBAD patients, statistical shape modelling and principal component analysis were performed to generate 270 synthetic 3D IVPs accounting for specific flow features. The synthetic 3D IVPs were then scaled and fine-tuned to match patient-specific stroke volume and systole-to-diastole ratio. The performance of personalized synthetic IVPs in CFD simulations was evaluated against patient-specific IVPs and compared with parabolic and flat IVPs. Our results showed that the synthetic 3D IVP was sufficient for faithful reproduction of hemodynamics throughout the aorta. In the ascending aorta (AAo), where non-patient-specific IVPs failed to replicate in vivo flow features in previous studies, the personalized synthetic IVP was able to match not only the flow pattern but also time-averaged wall shear stress (TAWSS), with a mean TAWSS difference of 5.9 %, which was up to 36.5 % by idealized IVPs. Additionally, the predicted retrograde flow index in both the AAo (8.36 %) and descending aorta (8.17 %) matched closely the results obtained with the 4D MRI-derived IVP (7.36 % and 6.55 %). The maximum false lumen pressure difference was reduced to 11.6 % from 68.8 % by the parabolic IVP and 72.6 % by the flat IVP. This study demonstrates the superiority of personalized synthetic 3D IVPs over commonly adopted parabolic or flat IVPs and offers a viable alternative to 4D MRI-derived IVP for CFD simulations of TBAD.

Wang K ，Armour CH ，Hanna L ，Gibbs R ，Xu XY ... -  《-》

Exploring tissue permeability of brain tumours in different grades: Insights from pore-scale fluid dynamics analysis.

Interstitial fluid (ISF) flow is identified as an essential physiological process that plays an important role in the development and progression of brain tumours. However, the relationship between the permeability of the tumour tissue, a complex porous medium, and the interstitial fluid flow around the tumour cells at the microscale is not well understood. To shed light on this issue, and in the absence of experimental techniques that can provide direct measurements, we develop a computational model to predict the tissue permeability of brain tumours in different grades by analysing the ISF flow at the pore scale. The 3-D geometrical models of tissue extracellular spaces are digitally reconstructed for each grade tumour based on their morphological properties measured from microscopic images. The predictive accuracy of the framework is validated by experimental results reported in the literature. Our results indicate that high-grade brain tumours are less permeable despite their higher porosity, whereas necrotic areas of glioblastoma are more permeable than the viable tumour areas. This implies that tissue permeability is primarily governed by both tissue porosity and the deposition of hyaluronic acid (HA), a key component of the extracellular matrix, while the HA deposition can have a greater effect than macro-level porosity. Parametric studies show that tissue permeability falls exponentially with increasing HA concentration in all grades of brain tumours, and this can be captured using an empirically derived relationship in a quantitative manner. These findings provide an improved understanding of the hydraulic properties of brain tumours and their intrinsic links to tumour microstructure. This work can be used to reveal the intratumoural physiochemical processes that rely on fluid flow and offer a powerful tool to tune textured and porous biomaterials for desired transport properties. STATEMENT OF SIGNIFICANCE: Interstitial fluid flow in the extracellular space of brain tumours plays a crucial role in their progression, development, and response to drug treatments. However, the mechanisms of interstitial fluid transport around tumour cells and the characterization of these microscale transports at the tissue scale to meet clinical requirements are largely unknown. In the absence of advanced experimental techniques to capture these pore-scale transport phenomena, we have developed and validated a computational framework to successfully reveal these phenomena across all grades of brain tumours. For the first time, we have quantitatively determined the tissue permeability of all grades of brain tumours as a function of the concentration of hyaluronic acid, a key component of the extracellular matrix. This framework will enhance our ability to capture the intratumoural physicochemical processes in brain tumours and correlate them with tumour tissue-scale behaviours.

Yang Y ，Yuan T ，Panaitescu C ，Li R ，Wu K ，Zhou Y ，Pokrajac D ，Dini D ，Zhan W ... -  《-》

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax = 45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax = 15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r = 5-10 µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

Jokivuolle M ，Mahmood F ，Madsen KH ，Harbo FSG ，Johnsen L ，Lundell H ... -  《-》