Characterization of Susceptibility Artifacts in MR-thermometry PRFS-based during Laser Interstitial Thermal Therapy

Magnetic Resonance Thermometry (MRT) is demonstrating huge abilities to guide laser interstitial thermal therapy (LITT) in several organs, such as the brain. Among the methods to perform MRT, Proton Resonance Frequency (PRF) shift holds significant benefits, like tissue independence. Despite its potential, PRF shift-based MRT holds significant challenges affecting the accuracy of reconstructed temperature maps. In particular, susceptibility artifacts due to gas-bubble formation are an important source of error in temperature maps in MRT-guided LITT. This work presents the characterization of the susceptibility artifacts in MRT-guided LITT and the measurement of its size. LITT was performed in gelatin-based phantoms, at 5 W, 2 W, 1 W, and 0.5 W under MRI guidance with a 1.5 T clinical MRI scanner. Temperature images were obtained with a 3D EPI (Echo planar imaging) prototype sequence. Areas of temperature errors were defined as zones of negative temperature variation <-2 degrees C. Moreover, we have analyzed the artifact shape in sagittal, axial and coronal planes. The analysis demonstrates a double-lobe shape for the susceptibility artifact mainly distributed in the sagittal plane. Also, the higher laser power caused a bigger artifact area. Temperature errors of similar to 80 degrees C proved the necessity to avoid susceptibility artifact generation during MRT-guided LITT. The analysis of the influence of the laser power on the artifact has suggested that using low laser power (0.5 W) helps avoid this measurement error

Stochastic Data Assimilation Approaches for Magnetic Resonance Temperature Imaging

During magnetic resonance (MR)-guided thermal therapies, 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. Due to recent advances in computational algorithms and hardware, much more powerful statistical analysis methods are becoming realizable in the real-time processing environment. To this end, my dissertation research focused on the development, validation, and implementation of stochastic data-driven processing techniques to increase the robustness of MR temperature monitoring during thermal therapies. MR temperature imaging was demonstrated to achieve a high degree of accuracy in damage predictions during rapid ablation procedures. In the event of temperature imaging data loss, a Kalman filtered MR temperature imaging algorithm using an uncorrelated, sparse covariance matrix for a Pennes bioheat model was developed to predict temperature in regions of missing or erroneous measurement. Temperature predictions were demonstrated to be accurate, while being less computationally expensive than the dense covariance matrix used in standard Kalman filtering. A second approach developed and investigated was the use of a Gaussian process for MR temperature imaging to allow for an accurate probabilistic extrapolation of the background phase. The technique demonstrated reliable temperature estimates in the presence of unwanted background field changes. The Gaussian process algorithm was also implemented to forecast temperature using a limited number of a priori temperature images. The performance of these proposed approaches was validated in simulations, ex vivo, and in vivo. These techniques allow for a full probabilistic prediction and an estimate of the uncertainty that provide a statistical model for MR temperature imaging. In conclusion, I have developed two novel approaches to MR temperature imaging post-processing and demonstrated the feasibility of application of these stochastic, data-driven models developed to improve the robustness of MR-guidance during thermal therapies and potentially enhance the safety and efficacy of treatment

Unscented Kalman Filtering for Real Time Thermometry During Laser Ablation Interventions

International audienceWe present a data-assimilation Bayesian framework in the context of laser ablation for the treatment of cancer. For solving the nonlinear estimation of the tissue temperature evolving during the therapy, the Unscented Kalman Filter (UKF) predicts the next thermal status and controls the ablation process, based on sparse temperature information. The purpose of this paper is to study the outcome of the prediction model based on UKF and to assess the influence of different model settings on the framework performances. In particular, we analyze the effects of the time resolution of the filter and the number and the location of the observations. Clinical Relevance-The application of a data-assimilation approach based on limited temperature information allows to monitor and predict in real-time the thermal effects induced by thermal therapy for tumors

Determination of Thermal Dose Model Parameters Using Magnetic Resonance Imaging

Magnetic Resonance Temperature Imaging (MRTI) is a powerful technique for noninvasively monitoring temperature during minimally invasive thermal therapy procedures. When coupled with thermal dose models, MRTI feedback provides the clinician with a real-time estimate of tissue damage by functioning as a surrogate for post-treatment verification imaging. This aids in maximizing the safety and efficacy of treatment by facilitating adaptive control of the damaged volume during therapy. The underlying thermal dose parameters are derived from laboratory experiments that do not necessarily reflect the surrogate imaging endpoints used for treatment verification. Thus, there is interest and opportunity in deriving model parameters from clinical procedures that are tailored to radiologic endpoints. The objective of this work is to develop and investigate the feasibility of a methodology for extracting thermal dose model parameters from MR data acquired during ablation procedures. To this end, two approaches are investigated. One is to optimize model parameters using post-treatment imaging outcomes. Another is to use a multi-parametric pulse sequence designed for simultaneous monitoring of temperature and damage dependent MR parameters. These methodologies were developed and investigated in phantom and feasibility established using retrospective analysis of in vivo thermal therapy treatments. This technique represents an opportunity to exploit experimental data to obtain thermal dose parameters that are highly specific for clinically relevant endpoints

Investigation of Heat Therapies using Multi-Scale Models and Statistical Methods

Ph.DDOCTOR OF PHILOSOPH

Doctor of Philosophy

dissertationFor magnetic resonance-guided focused ultrasound (MRgFUS) treatments to be broadly accepted, progress must be made in treatment planning, monitoring, and control. A key component to this goal is accurate modeling of the bioheat transfer equation (BHTE). This dissertation develops new methods for identifying the significant parameters of the BHTE: the ultrasonic specific absorption rate (SAR), the tissue thermal diffusivity, and perfusion-related energy losses. SAR is determined by fitting an analytical solution one-dimensional radial Gaussian heating) to MRgFUS temperature data in simulations and a tissue-mimicking phantom. This new method is compared with linear and exponential methods for different fitting times, beam sizes, perfusion, and thermal diffusivity values. The analytical method is consistently most reliable and is accurate to within 10% for all cases, except high perfusion. An extension to the analytical solution improves SAR estimates for high perfusion cases. MRgFUS sampling characteristics (spatial averaging, temporal sampling, and noise) for SAR and thermal diffusivity estimation are parametrically evaluated against several focused ultrasound beam sizes. For single point heatings, a maximum voxel size of 1x1x3 mm is recommended for temperature and estimate errors to remain less than 10%. Two MRgFUS thermal diffusivity estimation methods are evaluated against a standard technique in ex vivo porcine and in vivo rabbit back muscle. Both methods accurately estimate thermal diffusivity using cooling data (overall ex vivo error < 6%, in vivo < 12%). Including heating data in the Gaussian SAR method further reduces errors (ex vivo error < 2%, in vivo < 3%). The Gaussian SAR method has better precision than the Gaussian temperature method. Two methods for quantifying perfusion-related energy losses using MRgFUS cooling temperatures are developed (experimental + modeled data vs. experimental data). The methods are verified via simulations and experiments in ex vivo perfused porcine kidney at different flow rates. The difference techniques employed make these methods susceptible to noise errors, but this feasibility study demonstrates promise for their use in future work. In conclusion, these methods can be used to validate biothermal models, and associated improvements in thermal modeling have the potential to increase the efficacy and safety of MRgFUS therapies

Comparison between A-mode and B-mode ultrasound in local hyperthermia monitoring

Hyperthermia therapy is one of the therapy methods used for cancer treatment. It has shown to be an effective way of treating the cancerous tissue when compared to surgery, chemotherapy and radiation. However, real time monitoring method is capable in delivering a consistent heat and preventing any damages to the nearby tissue. Ultrasound is among the widely used technique in clinical setting. A-Mode ultrasound involves one-dimensional (1D) signal processing which enables a quantitative measurement on different types of breast tissues to be conducted faster as it has relatively simple signal processing requirement. On the other hand, B-Mode ultrasound offers good spatial resolution for thermal monitoring. Therefore, the aim of this study is to investigate and to compare the most optimum A-Mode and B-Mode ultrasound parameters to monitor hyperthermia in normal and pathological breast tissue. A series of experiment was conducted on 40 female Sprague Dawley rats. The pathological and normal rats were dissected and exposed to hyperthermia at variation temperature of 37oC (body temperature) and 40oC, 45oC, 50oC and 55oC for hyperthermia temperatures. A-Mode and B-Mode of 7.5 Mhz and 6Mhz was used simultaneously during the experiment for collecting acoustic information and scanning purposes before and after the hyperthermia exposure. Result obtained shows that, for normal tissue condition of both A-Mode and B-Mode, the attenuation calculation to mean of pixel intensity found to be (3.59±0.04)dB and 187.68 at temperature value of 50 oC. Meanwhile, in pathological tissue condition, the attenuation value with respect to pixel intensity was obtained by (3.36±0.26)dB at temperature value of 45oC and 199.26 was achieved at temperature value of 40oC. For backscatter coefficient to variance analysis, the result found that, in both A-Mode and B-Mode normal tissue condition, at temperature value of 40oC, (1.81±0.25) of backscatter coefficient was obtained while at 45oC, the variance value of 3298.94 was achieved. In pathological tissue, the temperature value of 40oC and 55oC was the most pronounce temperature dependent of (1.45±0.28) for backscatter coefficient with respect to 3275.35 of variance analysis. The result obtained from artificial neural network have shown that, 91.67% to 87.5% of testing to validation percentage accuracy of A-Mode was achieved, while in B-Mode, 88.89% and 81.25% of testing and validation data was obtained. Therefore, it is shown that, the use of A-Mode with comparison to B-Mode ultrasound can be used as another potential approach since its attenuation to pixel intensity and backscatter coefficient with respect to variance of A-Mode and B-Mode is very sensitive to the tissue structure in monitoring hyperthermia therapy with respect to the changes of temperature

Anniversary Paper: Evolution of ultrasound physics and the role of medical physicists and the AAPM and its journal in that evolution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134810/1/mp2048.pd