An inverse problem approach to recovery of in vivo nanoparticle concentrations from thermal image monitoring of MR-guided laser induced thermal therapy

Nanoparticle-assisted, image-guided laser interstitial thermal therapy for cancer treatment

Laser interstitial thermal therapy (LITT) guided by magnetic resonance imaging (MRI) is a new treatment option for patients with brain and non-central nervous system (non-CNS) tumors. MRI guidance allows for precise placement of optical fiber in the tumor, while MR thermometry provides real-time monitoring and assessment of thermal doses during the procedure. Despite promising clinical results, LITT complications relating to brain tumor procedures, such as hemorrhage, edema, seizures, and thermal injury to nearby healthy tissues, remain a significant concern. To address these complications, nanoparticles offer unique prospects for precise interstitial hyperthermia applications that increase heat transport within the tumor while reducing thermal impacts on neighboring healthy tissues. Furthermore, nanoparticles permit the co-delivery of therapeutic compounds that not only synergize with LITT, but can also improve overall effectiveness and safety. In addition, efficient heat-generating nanoparticles with unique optical properties can enhance LITT treatments through improved real-time imaging and thermal sensing. This review will focus on (1) types of inorganic and organic nanoparticles for LITT; (2) in vitro, in silico, and ex vivo studies that investigate nanoparticles' effect on light-tissue interactions; and (3) the role of nanoparticle formulations in advancing clinically relevant image-guided technologies for LITT. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Imaging-based characterization of convective tissue properties

Convective transport is an important phenomenon for nanomedicine delivery. We present an imaging-based approach to recover tissue properties that are significant in the accumulation of nanoparticles delivered via systemic methods. The classical pharmacokinetic analysis develops governing equations for the particle transport from a first principle mass balance. Fundamentally, the governing equations for compartmental mass balance represent a spatially invariant mass transport between compartments and do not capture spatially variant convection phenomena. Further, the parameters recovered from this approach do not necessarily have direct meaning with respect to the governing equations for convective transport. In our approach, a framework is presented for directly measuring permeability in the sense of Darcy flow through porous tissue. Measurements from our approach are compared to an extended Tofts model as a control. We demonstrate that a pixel-wise iterative clustering algorithm may be applied to reduce the parameter space of the measurements. We show that measurements obtained from our approach are correlated with measurements obtained from the extended Tofts model control. These correlations demonstrate that the proposed approach contains similar information to an established compartmental model and may be useful in providing an alternative theoretical framework for parameterizing mathematical models for treatment planning and diagnostic studies involving nanomedicine where convection dominated effects are important.

Thermoresponsive gels containing gold nanoparticles as smart antibacterial and wound healing agents

Thermoresponsive gels containing gold nanoparticles (AuNPs) were prepared using Pluronic®127 alone (F1) and with hydroxypropyl methylcellulose (F2) at ratios of 15% w/w and 15:1% w/w, respectively. AuNPs were evaluated for particle size, zeta-potential, polydispersity index (PDI), morphology and XRD pattern. AuNP-containing thermoresponsive gels were investigated for their gelation temperature, gel strength, bio-adhesive force, viscosity, drug content, in vitro release and ex-vivo permeation, in addition to in vitro antibacterial activity against bacteria found in burn infections, Staphylococcus aureus. In vivo burn healing and antibacterial activities were also investigated and compared with those of a commercial product using burn-induced infected wounds in mice. Spherical AuNPs sized 28.9-37.65 nm displayed a surface plasmon resonance band at 522 nm, a PDI of 0.461, and a zeta potential of 34.8 mV with a negative surface charge. F1 and F2 showed gelation temperatures of 37.2 °C and 32.3 °C, bio-adhesive forces of 2.45 ± 0.52 and 4.76 ± 0.84 dyne/cm2, viscosities of 10,165 ± 1.54 and 14,213 ± 2.31 cP, and gel strengths between 7.4 and 10.3 sec, respectively. The in vitro release values of F1 and F2 were 100% and 98.03% after 6 h, with permeation flux values of (J1) 0.2974 ± 2.85 and (J2) 0.2649 ± 1.43 (µg/cm2·h), respectively. The formulations showed antibacterial activity with the highest values for wound healing properties, as shown in vivo and by histopathological studies. This study demonstrates that a smart AuNPs thermoresponsive gel was successful as an antibacterial and wound healing transdermal drug delivery system.

A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy

We evaluated a physics-based model for planning for magnetic resonance-guided laser interstitial thermal therapy for focal brain lesions. Linear superposition of analytical point source solutions to the steady-state Pennes bioheat transfer equation simulates laser-induced heating in brain tissue. The line integral of the photon attenuation from the laser source enables computation of the laser interaction with heterogeneous tissue. Magnetic resonance thermometry data sets (n = 31) were used to calibrate and retrospectively validate the model's thermal ablation prediction accuracy, which was quantified by the Dice similarity coefficient (DSC) between model-predicted and measured ablation regions (T > 57 °C). A Gaussian mixture model was used to identify independent tissue labels on pre-treatment anatomical magnetic resonance images. The tissue-dependent optical attenuation coefficients within these labels were calibrated using an interior point method that maximises DSC agreement with thermometry. The distribution of calibrated tissue properties formed a population model for our patient cohort. Model prediction accuracy was cross-validated using the population mean of the calibrated tissue properties. A homogeneous tissue model was used as a reference control. The median DSC values in cross-validation were 0.829 for the homogeneous model and 0.840 for the heterogeneous model. In cross-validation, the heterogeneous model produced a DSC higher than that produced by the homogeneous model in 23 of the 31 brain lesion ablations. Results of a paired, two-tailed Wilcoxon signed-rank test indicated that the performance improvement of the heterogeneous model over that of the homogeneous model was statistically significant (p < 0.01).

Thermal Therapy Approaches for Treatment of Brain Tumors in Animals and Humans

Primary brain tumors are often aggressive, with short survival from time of diagnosis even with standard of care therapies such as surgery, chemotherapy, and radiation therapy. Thermal therapies have been extensively investigated as both primary and adjuvant therapy. Although thermal therapies are not yet widely used clinically, there have been several promising approaches demonstrated in both animals and humans. This review presents thermal therapy approaches in animal and human studies, including both hyperthermia (temperatures ~42°C-45°C) and thermal ablation (temperatures > 50°C). Hyperthermia is primarily used as adjuvant to chemotherapy and radiotherapy, and is the most widely studied radiation sensitizer where enhanced efficacy has been shown in human patients with brain cancer. Hyperthermia has additional beneficial effects such as immunogenic effects, and opening of the bloodbrain barrier to potentially enhance drug delivery, for example in combination with nanoparticle drug delivery systems. Thermal ablation uses high temperatures for direct local tumor destruction, and it found its way into clinical use as laser interstitial thermal therapy (LITT). This review presents various hyperthermia and ablation approaches, including a review of different devices and methods that have been used for thermal therapies, such as radiofrequency/microwaves, laser, high-intensity focused ultrasound, and magnetic nanoparticles. Current research efforts include the combination of advanced thermal therapy devices, such as focused ultrasound with radiation, as well as the use of thermal therapies to enhance chemotherapy delivery across the blood-brain barrier.

Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation

Computational finite element models are commonly used for the simulation of radiofrequency ablation (RFA) treatments. However, the accuracy of these simulations is limited by the lack of precise knowledge of tissue parameters. In this technical note, an inverse solver based on the unscented Kalman filter (UKF) is proposed to optimize values for specific heat, thermal conductivity, and electrical conductivity resulting in accurately simulated temperature elevations. A total of 15 RFA treatments were performed on ex vivo bovine liver tissue. For each RFA treatment, 15 finite-element simulations were performed using a set of deterministically chosen tissue parameters to estimate the mean and variance of the resulting tissue ablation. The UKF was implemented as an inverse solver to recover the specific heat, thermal conductivity, and electrical conductivity corresponding to the measured area of the ablated tissue region, as determined from gross tissue histology. These tissue parameters were then employed in the finite element model to simulate the position- and time-dependent tissue temperature. Results show good agreement between simulated and measured temperature.

A model evaluation study for treatment planning of laser-induced thermal therapy

A cross-validation analysis evaluating computer model prediction accuracy for a priori planning magnetic resonance-guided laser-induced thermal therapy (MRgLITT) procedures in treating focal diseased brain tissue is presented. Two mathematical models are considered. (1) A spectral element discretisation of the transient Pennes bioheat transfer equation is implemented to predict the laser-induced heating in perfused tissue. (2) A closed-form algorithm for predicting the steady-state heat transfer from a linear superposition of analytic point source heating functions is also considered. Prediction accuracy is retrospectively evaluated via leave-one-out cross-validation (LOOCV). Modelling predictions are quantitatively evaluated in terms of a Dice similarity coefficient (DSC) between the simulated thermal dose and thermal dose information contained within N = 22 MR thermometry datasets. During LOOCV analysis, the transient model's DSC mean and median are 0.7323 and 0.8001 respectively, with 15 of 22 DSC values exceeding the success criterion of DSC ≥ 0.7. The steady-state model's DSC mean and median are 0.6431 and 0.6770 respectively, with 10 of 22 passing. A one-sample, one-sided Wilcoxon signed-rank test indicates that the transient finite element method model achieves the prediction success criteria, DSC ≥ 0.7, at a statistically significant level.