Model Predictive Filtering MR Temperature Imaging for Laser-Induced Interstitial Thermotherapy.

Subependymal giant cell astrocytomas (SEGAs) are WHO grade 1 tumors associated with tuberous sclerosis that classically arise from the ventricular wall near the caudate groove and foramen of Monro. Laser interstitial thermal therapy (LITT) is a minimally invasive surgical technique, which works by heating a stereotactically placed laser fiber to ablative temperatures under MRI thermometry monitoring. In this paper, the authors present LITT as a surgical alternative to open resection of SEGAs. Twelve patients with SEGAs who underwent 16 procedures between 2007 and 2022 at a single institution were retrospectively reviewed. These patients underwent either open resection or LITT. Clinical data, imaging, recurrence rate, further treatments, and related complications were analyzed. Among the 16 procedures, 9 were open resection and 7 were LITT. An external ventricular drain was placed in 66% (6/9) of open procedures and 57.1% (4/7) of LITT cases. A septostomy was performed in 56% (5/9) of open procedures and 29% (2/7) of LITT cases. Complication rates were higher in open cases than in LITT procedures (44% vs 0%, p < 0.05). Complications included hydrocephalus, transient venous ischemia, wound infection, and bone flap migration. The median length of hospital stay was 4 days (IQR 3.3-5.5 days) for open cases and 4 days (IQR 3.0-7.0 days) for LITT procedures. Recurrence or progression occurred after 3 open cases and 2 LITT cases (33% vs 33%, p = 0.803). For the recurrences, 2 open cases underwent stereotactic radiosurgery, 1 open case underwent LITT, and 1 LITT case underwent repeat LITT. Among the LITT cases, only the patients with no decrease in tumor size by 6 months experienced tumor progression afterward. The 2 LITT cases with progression were the only ones with calcification present on preoperative imaging. The median follow-up times for cases assessed for progression were 8.4 years (IQR 3.8-14.4 years) for open resection and 3.9 years (IQR 3.4-5.1 years) for LITT. The small size of this case series limits generalizability or adequate comparison of safety. However, this series adds to the literature supporting LITT as a less invasive surgical alternative to open resection of SEGAs and demonstrates that LITT has similar recurrence and/or progression rates to open resection. Additional studies with more data are necessary for comprehensive comparisons between open resection and LITT for treating SEGA.

INTRODUCTION Treatment of intracranial lesions with laser interstitial thermal therapy (LITT) allows for simultaneous cytoreduction and the potential to generate an anti-tumor immune response. The safety and efficacy of combination immune checkpoint blockade (ICB) and LITT has not been determined. METHODS All patients who received LITT for neuro-oncologic indications at a single NCI-designated cancer center from 2015 – 2022 were included. Simultaneous ICB was defined as receipt of therapy within 6 weeks of LITT. Demographic, clinical, and survival data were collected. Immune-related and intracranial adverse events (iRAEs and IAEs) were graded according to Common Terminology Criteria. RESULTS Twenty-four patients received simultaneous LITT and ICB. Median age was 62 (range 40 - 78) and 12 (48%) were female. Median KPS was 80 (50 – 100). Use of ICB increased during the study period (r = 0.69). The most common pathology was non-small cell lung cancer (15 patients, 62.5%), followed by melanoma in 3 (12.5%) and 2 (8.3%) patients each with renal cell carcinoma or high-grade glioma. Twenty-three (96%) patients received single agent ICB, with pembrolizumab most common (12, 50%), followed by nivolumab (6, 25%). The median interval between LITT and ICB was 2.56 (0.85 – 4.98) weeks. The most common iRAE was hypothyroidism in 4 (16.6%) patients, with grade 3 pneumonitis in one patient. The most common IAE was seizures in 4 (16.6%) patients. Local progression was seen in 4 patients (16.6%), with a post-LITT overall survival of 20.6 (1.0 – 36.1) months for the cohort. CONCLUSIONS Combination LITT and ICB appears safe and effective, with iRAEs commensurate with trials of checkpoint inhibition. High-grade intracranial toxicity was rare. Prospective studies of combinatorial LITT + ICB are needed to establish biomarkers and optimal timing.

Purpose:Nanoparticle Mediated Laser Interstitial Thermal Therapy (npLITT) is a technique that utilizes tumor localized optically activated nanoparticles to increase the conformality of laser ablation procedures. Temperatures in these procedures are dependent on the particle concentration which generally cannot be measured noninvasively prior to therapy. In this work we attempt to quantify particle concentration in vivo by estimating the increase in R2* relaxation induced by bifunctional magnetic resonance (MR)‐visible gold‐based nanoparticles (SPIO@Au) and relate it to the temperature increase observed during real time MR temperature imaging (MRTI) of laser ablation.Methods:SPIO@Au nanoparticles (90nm) were synthesized containing a silica‐iron core (for MR visibility via R2*) and gold shell (for near‐infrared absorption). High resolution R2* maps were acquired before and after injecting four different particle concentrations (saline,1e10, 5e10, and 10e10 particles/mL) into HN5 flank xenografts. Tumors were monitored using MRTI during treatment with an interstitial fiber. (1 watt, 808 nm, 3 minutes)Results:The maximum temperature within the tumors increased linearly with concentration of injected particles, reaching 34.0, 37.6, 45.8, and 55.4 ⁰C for saline, 1e10, 5e10 and 10e10 particles/mL injections, respectively (R2=.994). The highest temperatures occur at the injection site rather than the fiber, confirming that SPIO@Au nanoparticles are the primary absorber. The differences between the median R2* measured at the injection site and the rest of the tumor were −6, 134, 111, 156 s‐1 for the saline,1e10,5e10 and 10e10 particles/mL injections, respectively. This R2* change is consistent with the measured relaxivity for the 1e10 particles/mL injection but does not maintain linearity at higher concentrations.Conclusion:Bifunctional SPIO@Au nanoparticles are a promising technology for providing noninvasive estimates of particle concentration via MRI and temperature increase in npLITT procedures. Future experiments will focus on lower, physiologically relevant particle concentrations and spin echo R2 mapping to better quantify the particle concentration.This research was supported by the National Institutes of Health and National Cancer Institute under Award Numbers TL1TR000369 and P30CA016672 and was conducted at the MD Anderson Center for Advanced Biomedical Imaging in‐part with equipment support from General Electric Healthcare.

To evaluate the accuracy and precision of manual and automatic blood vessel diameter measurements, a quantitative comparison was conducted, using both phantom and clinical 3D magnetic resonance angiography (MRA) data. Since diameters are often manually measured, which likely is influenced by operator dependency, automatic lumen delineation, based on the full-width at half-maximum (FWHM), could improve these measurements. Manual and automatic diameter assessments were compared, using MRA data from a vascular phantom (geometry obtained with μCT) and clinical MRA data. The diameters were manually assessed by 15 MRA experts, using both caliper and contour tools. To translate the experimental results to clinical practice, the precision obtained using phantom data was compared to the precision obtained with clinical data. A diameter error <10% was obtained with resolutions above 2, 3, and 5 pixels/diameter for the automatic FWHM, contour, and caliper methods, respectively. Using phantom data, precision of the manual methods was low (error >20%), even at high resolutions, while precision for the automatic method was high (error <3%) when using more than 2 pixels/diameter. A similar trend was found with clinical data. The results obtained clearly demonstrate improvement in the accuracy and precision of vessel diameter measurements with use of the automatic FWHM-based method.

PurposeQ.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT.MethodsThe NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region.ResultsBased on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast.ConclusionThe BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

The green's function method in quantum statistics

Towards accurate dose accumulation for Step-&-Shoot IMRT: Impact of weighting schemes and temporal image resolution on the estimation of dosimetric motion effects

Purpose:To develop and investigate a novel thermal dose model methodology using MR temperature imaging (MRTI) and post‐treatment MRI changes as a surrogate of tissue damage in clinical laser ablation of the brain.Methods:Post‐treatment contrast enhanced T1‐weighted images were registered to MRTI acquired during two MR‐guided thermal ablation procedures. The non‐enhancing central region and hyperintense ring of vascular disruption characteristic of the thermal lesion were segmented manually and resampled into the 2D geometry of the MRTI. Regions immediately inside and outside the ring were classified as nonviable and viable tissue, respectively (N=1860). Logistic regression was performed on these classifications using thermal dose (Ω) calculated via four Arrhenius dose models from the literature (Henriques (H), Weaver &amp; Stoll (WS), Diller &amp; Klutke (DK), and Brown (B)). The optimal (O) Arrhenius dose model was found by iteratively changing the parameters of the Arrhenius model, Ea and A, until logistic model deviance was minimized.Results:Deviance of the Henriques model was closest to the optimized model (360 (H), 630 (WS), 438 (DK), 803(B), 330(O)). The thermal dose required to achieve a 90% probability of nonviability was 1.4 (H), 2.65 (WS), 1.8 (D), 4.5 (B), 1.1 (O) for each model. Agreement between the area of &gt;90% nonviability between each model and the optimal model was 0.97 (H), 0.89(WS), 0.94(DK), and 0.88(B) using the Dice Similarity Coefficient.Conclusion:A methodology for deriving new thermal dose models from clinically used surrogates of tissue damage has been developed and tested. The logistic model suggests that the common dose threshold of Ω=1 may overestimate tissue damage. Good agreement is observed between the nonviable regions predicted by the optimized model and literature models. However, agreement between models is expected to decrease for longer ablation procedures designed to create larger thermal lesions and is an opportunity for further study.

MR-guided laser interstitial thermal therapy (MRgLITT) is associated with lower seizure-free outcome but better safety profile compared to open surgery. However, the predictors of seizure freedom following MRgLITT remain uncertain. This study aimed to use machine learning to predict seizure-free outcome following MRgLITT and to identify important predictors of seizure freedom in children with drug-resistant epilepsy. This multicenter study included children treated with MRgLITT for drug-resistant epilepsy at 13 epilepsy centers. The authors used clinical data, diagnostic investigations, and ablation features to predict seizure-free outcome at 1 year post-MRgLITT. Patients from 12 centers formed the training cohort, and patients in the remaining center formed the testing cohort. Five machine learning algorithms were developed on the training data by using 10-fold cross-validation, and model performance was measured on the testing cohort. The models were developed and tested on the complete feature set. Subsequently, 3 feature selection methods were used to identify important predictors. The authors then assessed performance of the parsimonious models based on these important variables. This study included 268 patients who underwent MRgLITT, of whom 44.4% had achieved seizure freedom at 1 year post-MRgLITT. A gradient-boosting machine algorithm using the complete feature set yielded the highest area under the curve (AUC) on the testing set (AUC 0.67 [95% CI 0.50-0.82], sensitivity 0.71 [95% CI 0.47-0.88], and specificity 0.66 [95% CI 0.50-0.81]). Logistic regression, random forest, support vector machine, and neural network yielded lower AUCs (0.58-0.63) compared to the gradient-boosting machine but the findings were not statistically significant (all p > 0.05). The 3 feature selection methods identified video-EEG concordance, lesion size, preoperative seizure frequency, and number of antiseizure medications as good prognostic features for predicting seizure freedom. The parsimonious models based on important features identified by univariate feature selection slightly improved model performance compared to the complete feature set. Understanding the predictors of seizure freedom after MRgLITT will assist with prognostication.

Simulations that are meant to determine the steady-state distribution of a diffusible solute such as oxygen in tissues have typically used finite difference methods to solve the diffusion equation. Finite difference methods require a tissue mesh with enough points to resolve oxygen gradients near and between discrete blood vessels. The large number of points that are typically required can make these calculations very slow. In this paper, we investigate a numerical method known as the Green's function method which is not bound by the same constraint. The Green's function method is expected to yield an accurate oxygen distribution more quickly by requiring fewer mesh points. Both methods were applied to calculate the steady state oxygen distribution in a model simulation region. When the Green's function calculation used meshes with 1/2, 1/4 and, 1/8 of the resolution required for the finite-difference mesh, there was good agreement with the finite difference calculation in all cases. When the volume of the domain was increased 8-fold the Green's function method was able to calculate the O2 field in 22 minutes, whereas the finite difference calculation is expected to take approximately 1 week. The number of steps required for the Green's function calculation increases quadratically with the number of points in the tissue mesh. As a result, small meshes are calculated very quickly using Green's functions, while for larger mesh sizes this method experiences a significant decrease in efficiency.

We have calculated the Debye-Waller factor (DWF) of Cu from a model that was used successfully in earlier calculations of anharmonicity by Cowley and Shukla. The present calculation has been carried out using quasiharmonic theory, the lowest-order (λ2) anharmonic perturbation theory, and a Green's function (GF) method which sums an infinite series of the λ2−type anharmonic terms. The static approximation ω → 0 in the cubic contribution to the self-energy of the GF, introduced in the earlier work on the DWF by Shukla and Hubschle is further justified by showing that in the high-temperature limit the exact results for the λ2 anharmonic contributions (cubic and quartic) to the Helmholtz free energy are given in this approximation. Results for the DWF are also obtained for a modified version of the Morse potential with λ2 perturbation theory (PT) and the GF method. The GF results are in excellent agreement with the experimental Mossbauer and X-ray data in the entire temperature range, 300 K  T  120...

The elastic response of a vacancy in a semi-infinite fcc copper lattice containing a free surface is calculated by using a new multiscale Green's function method. The method treats the lattice distortion near the vacancy at the atomistic level and the free surface at the macroscopic continuum level in the same formalism. The lattice is modeled using the lattice statics Green's function that fully accounts for the discrete atomistic structure of the lattice and can model a large crystallite containing a million atoms without excessive CPU effort. The method is especially useful for modeling the elastic response of nanocrystals containing point defects in which surfaces and interfaces play important roles. The method bridges the length scales seamlessly by relating the microscopic lattice distortion near a point defect to measurable macroscopic parameters of the solid such as the strain and the displacement field at a free surface. Using the interatomic potential derived by Cleri and Rosato, the lattice distortion, relaxation energy, and relaxation volume due to a vacancy are calculated in an otherwise perfect copper lattice for a million-atom model containing a free (100) surface. The calculated value of the relaxation volume is in excellent agreement with the observed value. Numerical results are also presented for the strain and the displacement fields at the free surface due to a vacancy and the interaction energy between a vacancy and the free surface in anisotropic semi-infinite copper.

There is a worldwide increasing interest in the use of non- and minimally invasive thermo therapies like radio frequency (RF), laser or focused ultrasound ablation for tumor treatment. Magnetic resonance (MR-)based on-line monitoring of such thermal ablation procedures enables therapists to readjust treatment parameters instantly to optimize therapy outcome. We designed a universal software tool called TAM (Thermal Ablation Monitoring) for MR monitoring of several thermal ablation methods, like RF ablation, laser interstitial induced thermal therapy (LITT) or focused ultrasound surgery (FUS). Based on the proton resonance frequency (PRF) shift method temperature maps, thermal dose and induced tissue damage are calculated and displayed. Moreover, TAM offers simultaneous on-line and off-line evaluation.Accuracy and usability of TAM were evaluated in vitro and in vivo. LITT and FUS experiments were carried out in tissue phantoms and in animals. On-line guidance and monitoring of FUS and LITT were tested. The contours of displayed lethal thermal dose compared to contrast enhanced T1-weighted MR images and macroscopic visible in vivo lesion size was over all smaller than 2 mm. Hence, the accurate monitoring functionality of TAM was demonstrated.KeywordsMR-guided thermotherapyMR thermometryThermal dosimetryInterventional MR

An analytic and numerical solution with spectral Green's function method for transport equation in spherical geometry

A new coarse-mesh computational method for the numerical solution of heat conduction and fluid flow problems is formally developed and applied to sample problems. The method is based upon formal use of Green's functions, which are defined locally over subdomains of the original system under consideration. The formal development of the local Green's function method for the solution of heat conduction problems is presented and discussed. Numerical solutions of sample problems for one-dimensional heat conduction with constant thermal conductivity, one-dimensional heat conduction with temperature-dependent thermal conductivity, and two-dimensional heat conduction with constant thermal conductivity are given, and these results are compared with results obtained using the finite difference and finite element methods. The formal development of the local Green's function method for the solution of fluid flow problems is then also presented and discussed; the numerical solution of a sample problem for simple one-dimensional incompressible fluid flow with viscous heating is also given.