Patient-specific Tissue Properties Research Articles

Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network

The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R2>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R2>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R2>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.

Design, Development, and Temporal Evaluation of a Magnetic Resonance Imaging-Compatible In Vitro Circulation Model Using a Compliant Abdominal Aortic Aneurysm Phantom.

Biomechanical characterization of abdominal aortic aneurysms (AAAs) has become commonplace in rupture risk assessment studies. However, its translation to the clinic has been greatly limited due to the complexity associated with its tools and their implementation. The unattainability of patient-specific tissue properties leads to the use of generalized population-averaged material models in finite element analyses, which adds a degree of uncertainty to the wall mechanics quantification. In addition, computational fluid dynamics modeling of AAA typically lacks the patient-specific inflow and outflow boundary conditions that should be obtained by nonstandard of care clinical imaging. An alternative approach for analyzing AAA flow and sac volume changes is to conduct in vitro experiments in a controlled laboratory environment. In this study, we designed, built, and characterized quantitatively a benchtop flow loop using a deformable AAA silicone phantom representative of a patient-specific geometry. The impedance modules, which are essential components of the flow loop, were fine-tuned to ensure typical intraluminal pressure conditions within the AAA sac. The phantom was imaged with a magnetic resonance imaging (MRI) scanner to acquire time-resolved images of the moving wall and the velocity field inside the sac. Temporal AAA sac volume changes lead to a corresponding variation in compliance throughout the cardiac cycle. The primary outcome of this work was the design optimization of the impedance elements, the quantitative characterization of the resistive and capacitive attributes of a compliant AAA phantom, and the exemplary use of MRI for flow visualization and quantification of the deformed AAA geometry.

Hyperthermia Treatment Planning: Clinical Application and Ongoing Developments

Hyperthermia is a proven clinical anti-cancer treatment, used in combination with radiotherapy and/or chemotherapy. During hyperthermia, tumour tissue is heated to 40-43 °C using radiofrequency or microwave antennas, which strongly enhances effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment quality depends on tumour temperatures achieved and treatment planning (i.e., simulation and optimization of absorbed power and temperature distributions) could be very useful to ensure and improve treatment quality. Hyperthermia treatment planning was mainly a research tool for decades, because of high computational costs and limited quantitative accuracy of treatment planning predictions due to a lack of patient-specific tissue properties. Thanks to developments over the past decade, treatment planning becomes increasingly important in the clinical workflow. Presently, main clinical applications of hyperthermia treatment planning are 1) applicator selection, 2) heating ability evaluation and 3) on-line treatment guidance. To improve the reliability and further increase applicability of treatment planning, ongoing developments focus on 1) dielectric imaging to derive patient-specific dielectric properties, 2) advanced thermal modelling including discrete vasculature and 3) biological modelling to predict the radiosensitizing effect of hyperthermia in terms of equivalent radiation dose. The increased clinical application and ongoing efforts will further improve treatment quality.

Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models.

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.

Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy.

Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties. In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data. This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8°C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions. These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties. The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA.

Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.

Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption. Probabilistic estimation of model parameters, however, remains an unresolved challenge. Direct Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution function (pdf) of the parameters is infeasible because it involves repeated evaluations of the computationally expensive simulation model. To accelerate this inference, one popular approach is to construct a computationally efficient surrogate and sample from this approximation. However, by sampling from an approximation, efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling of the posterior pdf into accelerating the Metropolis-Hastings (MH) sampling of the exact posterior pdf. It is achieved by two main components: (1) construction of a Gaussian process (GP) surrogate of the exact posterior pdf by actively selecting training points that allow for a good global approximation accuracy with a focus on the regions of high posterior probability; and (2) use of the GP surrogate to improve the proposal distribution in MH sampling, in order to improve the acceptance rate. The presented framework is evaluated in its estimation of the local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. In addition, the obtained posterior distributions of model parameters are interpreted in relation to the factors contributing to parameter uncertainty, including different low-dimensional representations of the parameter space, parameter non-identifiability, and parameter correlations.

Solving membrane stress on deformed configuration using inverse elastostatic and forward penalty methods

Wall stress in a curved membrane depends on the surface geometry and applied load, not the constitutive law of the wall material (static determinacy). This remarkable property suggests that the membrane stress can be determined without knowing of the material property. Being able to determine the wall stress without material knowledge is practically significant in numerous applications, particularly in in vivo biomedical stress analysis wherein patient-specific tissue properties are extremely difficult to obtain. In this article, we present two non-traditional methods that utilize the static determinacy to compute the membrane stress. A common theme is to start with a deformed configuration and use artificial material models. In one of these approaches, the equilibrium problem is formulated as an inverse elastostatic problem whereby the stress is determined by way of finding a (fictitious) reference configuration. The other approach utilizes the standard forward analysis, but in a penalty setting using artificially stiff material models. A continuum sensitivity analysis is implemented to quantitatively assess the influence of material parameters. These developments open new pathways of membrane analysis enabling stress prediction without using realistic material parameters. Numerical examples are provided to demonstrate and compare these two methods.

Accurate 3D temperature dosimetry during hyperthermia therapy by combining invasive measurements and patient-specific simulations

Purpose: Dosimetry during deep local hyperthermia treatments in the head and neck currently relies on a limited number of invasively placed temperature sensors. The purpose of this study was to assess the feasibility of 3D dosimetry based on patient-specific temperature simulations and sensory feedback. Materials and methods: The study includes 10 patients with invasive thermometry applied in at least two treatments. Based on their invasive thermometry, we optimised patient-group thermal conductivity and perfusion values for muscle, fat and tumour using a ‘leave-one-out’ approach. Next, we compared the accuracy of the predicted temperature (ΔT) and the hyperthermia treatment quality (ΔT50) of the optimisations based on the patient-group properties to those based on patient-specific properties, which were optimised using previous treatment measurements. As a robustness check, and to enable comparisons with previous studies, we optimised the parameters not only for an applicator efficiency factor of 40%, but also for 100% efficiency. Results: The accuracy of the predicted temperature (ΔT) improved significantly using patient-specific tissue properties, i.e. 1.0 °C (inter-quartile range (IQR) 0.8 °C) compared to 1.3 °C (IQR 0.7 °C) for patient-group averaged tissue properties for 100% applicator efficiency. A similar accuracy was found for optimisations using an applicator efficiency factor of 40%, indicating the robustness of the optimisation method. Moreover, in eight patients with repeated measurements in the target region, ΔT50 significantly improved, i.e. ΔT50 reduced from 0.9 °C (IQR 0.8 °C) to 0.4 °C (IQR 0.5 °C) using an applicator efficiency factor of 40%. Conclusion: This study shows that patient-specific temperature simulations combined with tissue property reconstruction from sensory data provides accurate minimally invasive 3D dosimetry during hyperthermia treatments: T50 in sessions without invasive measurements can be predicted with a median accuracy of 0.4 °C.

Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility. A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes' bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images. The total time to initialize and simulate an MRgLITT procedure using the GUI was [Formula: see text]5 min. Each independent simulation took [Formula: see text]30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour [Formula: see text] was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm [Formula: see text], whereas the mean Dice similarity coefficient was 0.93 [Formula: see text]. We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.

Graphic Processing Units (GPUs)-Based Haptic Simulator for Dental Implant Surgery

This paper presents a haptics-based training simulator for dental implant surgery. Most of the previously developed dental simulators are targeted for exploring and drilling purpose only. The penalty-based contact force models with spherical-shaped dental tools are often adopted for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure, with no limitations on drill shape. In addition, a real-time torque model is proposed to simulate the torque resistance in the implant insertion procedure, based on patient-specific tissue properties and implant geometry. To achieve better anatomical accuracy, our oral model is reconstructed from cone beam computed tomography (CBCT) images with a voxel-based method. To enhance the real-time response, the parallel computing power of GPUs is exploited through extra efforts in data structure design, algorithms parallelization, and graphic memory utilization. Results show that the developed system can produce appropriate force feedback at different tissue layers during pilot drilling and can create proper resistance torque responses during implant insertion.

Towards determining soft tissue properties for modelling spine surgery: current progress and challenges

Current complication rates for adolescent scoliosis surgery necessitate the development of better surgical planning tools to improve outcomes. Here we present our approach to developing finite element models of the thoracolumbar spine for deformity surgery simulation, with patient-specific model anatomy based on low-dose pre-operative computed tomography scans. In a first step towards defining patient-specific tissue properties, an initial 'benchmark' set of properties were used to simulate a clinically performed pre-operative spinal flexibility assessment, the fulcrum bending radiograph. Clinical data for ten patients were compared with the simulated results for this assessment and in cases where these data differed by more than 10%, soft tissue properties for the costo-vertebral joint (CVJt) were altered to achieve better agreement. Results from these analyses showed that changing the CVJt stiffness resulted in acceptable agreement between clinical and simulated flexibility in two of the six cases. In light of these results and those of our previous studies in this area, it is suggested that spinal flexibility in the fulcrum bending test is not governed by any single soft tissue structure acting in isolation. More detailed biomechanical characterisation of the fulcrum bending test is required to provide better data for determination of patient-specific soft tissue properties.