C MacLellan

Effect of pulse sequence parameter selection on signal strength in positive-contrast MRI markers for MRI-based prostate postimplant assessment

PURPOSE For postimplant dosimetric assessment, computed tomography (CT) is commonly used to identify prostate brachytherapy seeds, at the expense of accurate anatomical contouring. Magnetic resonance imaging (MRI) is superior to CT for anatomical delineation, but identification of the negative-contrast seeds is challenging. Positive-contrast MRI markers were proposed to replace spacers to assist seed localization on MRI images. Visualization of these markers under varying scan parameters was investigated. METHODS To simulate a clinical scenario, a prostate phantom was implanted with 66 markers and 86 seeds, and imaged on a 3.0T MRI scanner using a 3D fast radiofrequency-spoiled gradient recalled echo acquisition with various combinations of scan parameters. Scan parameters, including flip angle, number of excitations, bandwidth, field-of-view, slice thickness, and encoding steps were systematically varied to study their effects on signal, noise, scan time, image resolution, and artifacts. RESULTS The effects of pulse sequence parameter selection on the marker signal strength and image noise were characterized. The authors also examined the tradeoff between signal-to-noise ratio, scan time, and image artifacts, such as the wraparound artifact, susceptibility artifact, chemical shift artifact, and partial volume averaging artifact. Given reasonable scan time and managable artifacts, the authors recommended scan parameter combinations that can provide robust visualization of the MRI markers. CONCLUSIONS The recommended MRI pulse sequence protocol allows for consistent visualization of the markers to assist seed localization, potentially enabling MRI-only prostate postimplant dosimetry.

Estimating nanoparticle optical absorption with magnetic resonance temperature imaging and bioheat transfer simulation

Abstract Purpose: Optically activated nanoparticle-mediated heating for thermal therapy applications is an area of intense research. The ability to characterise the spatio-temporal heating potential of these particles for use in modelling under various exposure conditions can aid in the exploration of new approaches for therapy as well as more quantitative prospective approaches to treatment planning. The purpose of this research was to investigate an inverse solution to the heat equation using magnetic resonance temperature imaging (MRTI) feedback, for providing optical characterisation of two types of nanoparticles (gold–silica nanoshells and gold nanorods). Methods: The optical absorption of homogeneous nanoparticle–agar mixtures was measured during exposure to an 808 nm laser using real-time MRTI. A coupled finite element solution of heat transfer was registered with the data and used to solve the inverse problem. The L2 norm of the difference between the temperature increase in the model and MRTI was minimised using a pattern search algorithm by varying the absorption coefficient of the mixture. Results: Absorption fractions were within 10% of literature values for similar nanoparticles. Comparison of temporal and spatial profiles demonstrated good qualitative agreement between the model and the MRTI. The weighted root mean square error was <1.5 σMRTI and the average Dice similarity coefficient for ΔT = 5 °C isotherms was >0.9 over the measured time interval. Conclusion: This research demonstrates the feasibility of using an indirect method for making minimally invasive estimates of nanoparticle absorption that might be expanded to analyse a variety of geometries and particles of interest.

A methodology for thermal dose model parameter development using perioperative MRI

Abstract Post-treatment imaging is the principal method for evaluating thermal lesions following image-guided thermal ablation procedures. While real-time temperature feedback using magnetic resonance temperature imaging (MRTI) is a complementary tool that can be used to optimise lesion size throughout the procedure, a thermal dose model is needed to convert temperature–time histories to estimates of thermal damage. However, existing models rely on empirical parameters derived from laboratory experiments that are not direct indicators of post-treatment radiologic appearance. In this work, we investigate a technique that uses perioperative MR data to find novel thermal dose model parameters that are tailored to the appearance of the thermal lesion on post-treatment contrast-enhanced imaging. Perioperative MR data were analysed for five patients receiving magnetic resonance-guided laser-induced thermal therapy (MRgLITT) for brain metastases. The characteristic enhancing ring was manually segmented on post-treatment T1-weighted imaging and registered into the MRTI geometry. Post-treatment appearance was modelled using a coupled Arrhenius-logistic model and non-linear optimisation techniques were used to find the maximum-likelihood kinetic parameters and dose thresholds that characterise the inner and outer boundary of the enhancing ring. The parameter values and thresholds were consistent with previous investigations, while the average difference between the predicted and segmented boundaries was on the order of one pixel (1 mm). The areas predicted using the optimised model parameters were also within 1 mm of those predicted by clinically utilised dose models. This technique makes clinically acquired data available for investigating new thermal dose model parameters driven by clinically relevant endpoints.

Referenceless magnetic resonance temperature imaging using Gaussian process modeling

Purpose During magnetic resonance (MR)‐guided thermal therapies, water proton resonance frequency shift (PRFS)‐based MR temperature imaging can quantitatively monitor tissue temperature changes. It is widely known that the PRFS technique is easily perturbed by tissue motion, tissue susceptibility changes, magnetic field drift, and modality‐dependent applicator‐induced artifacts. Here, a referenceless Gaussian process modeling (GPM)‐based estimation of the PRFS is investigated as a methodology to mitigate unwanted background field changes. The GPM offers a complementary trade‐off between data fitting and smoothing and allows prior information to be used. The end result being the GPM provides a full probabilistic prediction and an estimate of the uncertainty. Methods GPM was employed to estimate the covariance between the spatial position and MR phase measurements. The mean and variance provided by the statistical model extrapolated background phase values from nonheated neighboring voxels used to train the model. MR phase predictions in the heating ROI are computed using the spatial coordinates as the test input. The method is demonstrated in ex vivo rabbit liver tissue during focused ultrasound heating with manually introduced perturbations (n = 6) and in vivo during laser‐induced interstitial thermal therapy to treat the human brain (n = 1) and liver (n = 1). Results Temperature maps estimated using the GPM referenceless method demonstrated a RMS error of <0.8°C with artifact‐induced reference‐based MR thermometry during ex vivo heating using focused ultrasound. Nonheated surrounding areas were <0.5°C from the artifact‐free MR measurements. The GPM referenceless MR temperature values and thermally damaged regions were within the 95% confidence interval during in vivo laser ablations. Conclusions A new approach to estimation for referenceless PRFS temperature imaging is introduced that allows for an accurate probabilistic extrapolation of the background phase. The technique demonstrated reliable temperature estimates in the presence of the background phase changes and was demonstrated useful in the in vivo brain and liver ablation scenarios presented.

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

Abstract 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).

Accelerated magnetic resonance thermometry in the presence of uncertainties

A model-based information theoretic approach is presented to perform the task of magnetic resonance (MR) thermal image reconstruction from a limited number of observed samples on k-space. The key idea of the proposed approach is to optimally detect samples of k-space that are information-rich with respect to a model of the thermal data acquisition. These highly informative k-space samples can then be used to refine the mathematical model and efficiently reconstruct the image. The information theoretic reconstruction was demonstrated retrospectively in data acquired during MR-guided laser induced thermal therapy (MRgLITT) procedures. The approach demonstrates that locations with high-information content with respect to a model-based reconstruction of MR thermometry may be quantitatively identified. These information-rich k-space locations are demonstrated to be useful as a guide for k-space undersampling techniques. The effect of interactively increasing the predicted number of data points used in the subsampled model-based reconstruction was quantified using the L2-norm of the distance between the subsampled and fully sampled reconstruction. Performance of the proposed approach was also compared with uniform rectilinear subsampling and variable-density Poisson disk subsampling techniques. The proposed subsampling scheme resulted in accurate reconstructions using a small fraction of k-space points, suggesting that the reconstruction technique may be useful in improving the efficiency of thermometry data temporal resolution.

WE-AB-BRA-04: Investigation of MRI Derived Thermal Dose Models

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 & Stoll (WS), Diller & 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 >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.

TU‐H‐CAMPUS‐IeP3‐03: Validation of Image Registration Methods for Brain Magnetic Resonance Imaging

PURPOSE To assess the performance of both commercial and open-source co-registration solutions as applied to intra-subject, multi-sequence, magnetic resonance (MR) images of the brain. METHODS Twenty (20) patients were imaged on clinical 3.0T MR scanners to obtain T2-weighted (T2W), fluid attenuation inversion recovery (FLAIR), susceptibility weighted angiography (SWAN), and T1 post-contrast (T1C) image sequences. Fiducial landmark sites (n=15 per sequence, 4 sequences per patient, 1200 total planned for 4 sequences and 20 patients, 1175 total realized) were specified throughout these image volumes to define identical locations across sequences. Both commercial (General Electric [GE] VolumeViewer) and open-source software (Advanced Normalization Tools [ANTs]) registration solutions were applied using the T2W sequence as the fixed reference. Landmark and image similarity (cross-correlation [CC], mutual information [MI]) based registrations were performed for all image pairs. Rigid (6DOF) and affine (12DOF) transformations were considered. The Euclidean Target-to-Registration Error (TRE) was calculated at all landmarks of each image pair. RESULTS Prior to registration, TRE values for FLAIR, SWAN, and T1C were 2.07 ± 0.55 mm, 2.63 ± 0.62 mm and 3.65 ± 2.00 mm, respectively. Post-registration, the best (smallest) average TRE values for FLAIR, SWAN, and T1C were 1.55 ± 0.46 mm (rigid MI), 1.34 ± 0.23 mm (affine MI) and 1.06 ± 0.16 mm (GE), respectively. CONCLUSION This study presents a methodology to quantify the registration accuracy of commercial algorithms (whose figures of merit are often not publically available, despite clinical use), and compares that accuracy to open-source alternatives. All sequences, on average, were improved by spatial registrations that corrected for patient motion, and such motion itself was found to increase with time spent in the MR scanner. The neuroanatomical information encoded in these landmarks, as placed on images with different contrast mechanisms, collectively represents a comprehensive dataset for quantitative evaluation of clinically-used registration software. A.C. is an employee of GE Healthcare. This research was supported in part by the National Cancer Institute (Cancer Center Support Grant CA016672 and Training Grant 5T32CA119930). J.S.L. acknowledges support from the Baylor College of Medicine Medical Scientist Training Program and the Cullen Trust for Higher Education Physician/Scientist Fellowship Program.

SU-F-J-03: Treatment Planning for Laser Ablation Therapy in Presence of Heterogeneous Tissue: A Retrospective Study

PURPOSE MR guided Laser Induced Thermal Therapy (MRgLITT) is an effective technique for cancerous tissue destruction in brain. Precise modeling of the thermal ablation procedure is important for treatment planning for MRgLITT. Physical properties of target tissue such as optical attenuation (µ) highly affect the outcome of thermal ablation modeling. Hence to ensure accuracy of modeling scheme, it is crucial to precisely estimate patient specific heterogeneity in these parameters. METHODS A steady-state Bioheat model is used for simulation of temperature field over the heterogeneous target tissue. Physical properties are assessed from training based on previously recorded MRTI datasets. In detail, a cost function is defined based on the difference between the Bioheat model temperature predictions and MRTI measurements. Minimizing this cost function results in optimal values for physical properties of each tissue type. Optical attenuation coefficients (µ) are the dominant sensitivity and are optimized in this work. Nominal values of other parameters are used. Population averages of the optimal values of µ are then used in Bioheat model for temperature assessment in future patients. Performance of these predictions is then verified using Dice similarity index. RESULTS Results provide a comprehensive analysis of the effect of using heterogeneous tissue segmentations on performance of LITT mathematical simulation. Optimal values of µ coefficients range between 100 m-1 and 200 m-1 . This corresponds with obtained values in literature. The mean and median of Dice similarity index while using optimal values of optical attenuation coefficients is 0.8022 and 0.8225, respectively. CONCLUSION Calibration of mathematical modeling outcomes with clinical MRTI data provides a better understanding about the parameters of the Bioheat equation and consequently provides a more confident thermal dose assessment than either approach alone. Results demonstrate that modeling predictions are accurate in a-priori prediction of thermal dose and may be useful in planning the procedure.

MO-FG-BRA-09: Quantification of Nanoparticle Heating and Concentration for MR-Guided Laser Interstitial Thermal Therapy

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.