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

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

Estimation and Fault Detection on Hydraulic System with Adaptive-Scaling Kalman and Consensus Filtering

The area of fault detection is becoming more interesting since there have been many unique designs to detect or even compensate the faults, either from sensor or actuator. This paper applies the hydraulic system with interconnected tanks by implementing a leakage on one of the three tanks. The mathematical model along with the details of stability properties are highly discussed in this paper by imposing the Lyapunov and boundedness stability. The theory of fault detection with certain threshold after the occurrence of the fault corresponding to the state estimation error is mathematically presented ended by simulation. Moreover, the system compares the effectiveness of the proposed observer using Luenberger observer, adaptive-scaling Kalman and consensus filtering. The results for some different initial condition guarantee the detection of the fault for some time $t_d > t_f$Comment: 8 pages, 13 figure

Sonication methods and motion compensation for magnetic resonance guided high-intensity focused ultrasound

High-intensity focused ultrasound (HIFU) is an efficient noninvasive therapeutic technique for localized heating of tissues deep within the human body through intact skin. Magnetic resonance imaging (MRI) can provide excellent soft-tissue contrast and can be used for both treatment planning and post-treatment assessment of the induced tissue damage. MRI can also provide temperature sensitive in vivo images via proton resonance frequency shift thermometry. Combined, the use of MRI and HIFU (MR-HIFU) ablation make for a promising therapeutic modality for controlled and noninvasive selective tissue destruction. Sonication strategies, MR thermometry methods, feedback control, and motion compensation for MR-HIFU were developed and evaluated in this thesis. The primary aim of the thesis was to develop a safe and efficient strategy for clinical MR-HIFU ablation. An efficient volumetric method of ablation was achieved by utilizing the phased-array capabilities of the transducer and the inherent heat diffusion of already deposited heat. The induced temperature rise was monitored with rapid multiplane MR thermometry with a volumetric coverage of the heated region. Acquisition and display of temperature images during sonication improved the safety of the therapy. The therapeutic procedure was evaluated in a large animal model and proved to provide a substantial improvement in efficiency as compared to existing methods without compromising safety. The second aim was to improve the reliability of the proposed volumetric sonication strategy. This was achieved with a simple and robust binary feedback algorithm that adjusted the sonication duration of each part of the sonication trajectory based on the temperature rise as obtained by volumetric MR thermometry. The feedback algorithm was evaluated in a large animal model, and was found to reduce the variability in thermal lesion size by approximately 70%. The third aim was to develop a through-plane motion correction method for real-time MR thermometry without disturbing thermometry. This was achieved with a fat-selective navigator. This navigator outperformed the conventional navigator for direct tracking of the kidney under free breathing. The navigator also provided accurate indexing of the look-up-table used to correct the reference phase for MR thermometry of mobile organs. Finally, the combination of through-plane motion correction provided by the fat-selective navigator with existing methods of in-plane motion correction and reference phase correction, allowed for an accurate 3D motion compensation of both MR thermometry and MR-HIFU sonication

Doctor of Philosophy

dissertationThis dissertation presents original research that improves the ability of magnetic resonance imaging (MRI) to measure temperature in aqueous tissue using the proton resonance frequency (PRF) shift and T1 measurements in fat tissue in order to monitor focused ultrasound (FUS) treatments. The inherent errors involved in measuring the longitudinal relaxation time T1 using the variable flip angle method with a two-dimensional (2D) acquisition are presented. The edges of the slice profile can contribute a significant amount of signal for large flip angles at steady state, which causes significant errors in the T1 estimate. Only a narrow range of flip angle combinations provided accurate T1 estimates. Respiration motion causes phase artifacts, which lead to errors when measuring temperature changes using the PRF method. A respiration correction method for 3D imaging temperature of the breast is presented. Free induction decay (FID) navigators were used to measure and correct phase offsets induced by respiration. The precision of PRF temperature measurements within the breast was improved by an average factor of 2.1 with final temperature precision of approximately 1 °C. Locating the position of the ultrasound focus in MR coordinates of an ultrasound transducer with multiple degrees of freedom can be difficult. A rapid method for predicting the position using 3 tracker coils with a special MRI pulse iv sequence is presented. The Euclidean transformation of the coil's current positions to their calibration positions was used to predict the current focus position. The focus position was predicted to within approximately 2.1 mm in less than 1 s. MRI typically has tradeoffs between imaging field of view and spatial and temporal resolution. A method for acquiring a large field of view with high spatial and temporal resolution is presented. This method used a multiecho pseudo-golden angle stack of stars imaging sequence to acquire the large field of view with high spatial resolution and k-space weighted image contrast (KWIC) to increase the temporal resolution. The pseudo-golden angle allowed for removal of artifacts introduced by the KWIC reconstruction algorithm. The multiple echoes allowed for high readout bandwidth to reduce blurring due to off resonance and chemical shift as well as provide separate water/fat images, estimates of the initial signal magnitude M(0), T2 * time constant, and combination of echo phases. The combined echo phases provided significant improvement to the PRF temperature precision, and ranged from ~0.3-1.0 °C within human breast. M(0) and T2 * values can possibly be used as a measure of temperature in fat

Motion Tracking for Medical Applications using Hierarchical Filter Models

A medical intervention often requires relating treatment to the situation, which it was planned on. In order to circumvent undesirable effects of motion during the intervention, positional differences must be detected in real-time. To this end, in this thesis a hierarchical Particle Filter based tracking algorithm is developed in three stages. Initially, a model description of the individual nodes in the aspired hierarchical tree is presented. Using different approaches, properties of such a node are derived and approximated, leading to a parametrization scheme. Secondly, transformations and appearance of the data are described by a fixed hierarchical tree. A sparse description for typical landmarks in medical image data is presented. A static tree model with two levels is developed and investigated. Finally, the notion of 'association' between landmarks and nodes is introduced in order to allow for dynamic adaptation to the underlying structure of the data. Processes for tree maintenance using clustering and sequential reinforcement are implemented. The function of the full algorithm is demonstrated on data of abdominal breathing motion