Thermal dosimetry for bladder hyperthermia treatment. An overview.

The urinary bladder is a fluid-filled organ. This makes, on the one hand, the internal surface of the bladder wall relatively easy to heat and ensures in most cases a relatively homogeneous temperature distribution; on the other hand the variable volume, organ motion, and moving fluid cause artefacts for most non-invasive thermometry methods, and require additional efforts in planning accurate thermal treatment of bladder cancer. We give an overview of the thermometry methods currently used and investigated for hyperthermia treatments of bladder cancer, and discuss their advantages and disadvantages within the context of the specific disease (muscle-invasive or non-muscle-invasive bladder cancer) and the heating technique used. The role of treatment simulation to determine the thermal dose delivered is also discussed. Generally speaking, invasive measurement methods are more accurate than non-invasive methods, but provide more limited spatial information; therefore, a combination of both is desirable, preferably supplemented by simulations. Current efforts at research and clinical centres continue to improve non-invasive thermometry methods and the reliability of treatment planning and control software. Due to the challenges in measuring temperature across the non-stationary bladder wall and surrounding tissues, more research is needed to increase our knowledge about the penetration depth and typical heating pattern of the various hyperthermia devices, in order to further improve treatments. The ability to better determine the delivered thermal dose will enable clinicians to investigate the optimal treatment parameters, and consequentially, to give better controlled, thus even more reliable and effective, thermal treatments

A Predictive-Adaptive, Multipoint Feedback Controller for Local Heat Therapy of Solid Tumors

Uniform heating of tumor tissue to therapeutic temperatures without damaging surrounding normal tissue is required for optimal local heat therapy of cancer. This paper describes an algorithm for online computer control that will allow the therapist to minimize the standard deviation of measured intratumoral temperatures. The method is applicable to systems incorporating multiple surface and/or interstitial applicators delivering microwave, radiofrequency, or ultrasonic power and operating under the control of a small computer. The essential element is a novel predictive-adaptive control algorithm that infers relevant thermal parameters from the responses of multiple temperature sensors, as each of the power applicators is briefly turned off. Applied power and effective perfusion are estimated from transient slope changes of the temperature-time curves for each sensor. By substituting these values into a system of linear equations derived from the bio-heat transfer equation, the small computer can calculate the optimal allocation of power among the various applicators (“knob settings”) to generate most uniform intratumoral temperature distribution with the desired mean, or minimum, tumor temperature

Real-time Temperature Imaging Using Ultrasonic Change in Backscattered Energy

Thermal therapy from low-temperature cryosurgery to high-temperature ablation of tumors and unwanted electrical pathways has gained increased attention. Temperature imaging (TI) from magnetic resonance studies is the de facto standard for volumetric estimation of temperature. Ultrasound has the advantages of being cheap, portable, non-invasive and non-ionizing. Our group showed in predictions for single scatterers, simulations of scatterer populations and measurements in 1D, 2D and 3D, that CBE changed monotonically with temperature with 1oC accuracy. An obstacle to clinical application of CBE TI is estimation of temperature in real time, which is limited by time for motion compensation (MC). To achieve real-time TI, we implemented a two-computer architecture. Our Terason 3000 ultrasonic imaging system collected and sent raw images over a jtcp connection to a TI computer with a GeForce GTX 770 GPU card. The TI computer performed motion compensation and extracted temperature images. Turkey specimens were imaged during heating with hot water (75oC) in 1 cm tube. Total heating time was 1200 sec, with a 30 sec interval between image acquisitions; tissue temperature was monitored with thermocouples. Over six experiments at 3 thermocouple sites, the accuracy of CBE TI was 0.8±0.7oC. Using its CPU, the TI computer updated temperature images using rigid MC in 4 sec, and using more nearly accurate nonrigid MC in 7 sec. Nonrigid MC time was reduced to 0.2 sec using the GPU processor along with optimization of the MC algorithm. Calculation of CBE in MC images and conversion of CBE to TI takes less than an additional 0.1 sec. With TI time reduced to \u3c 0.3 sec, the limit to real-time CBE TI now lies with the Terason 3000 system. It takes about 5 sec to transform an ultrasonic image in its native format to Matlab before sending it to the TI computer. Therefore, we believe CBE TI can be done at a 1 Hz frame rate with \u3c 1oC error if conversion to Matlab in the Terason 3000 can be reduced to less than 0.7 sec

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

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

Possible Patient Early Diagnosis by Ultrasonic Noninvasive Estimation of Thermal Gradients into Tissues Based on Spectral Changes Modeling

To achieve a precise noninvasive temperature estimation, inside patient tissues, would open promising research fields, because its clinic results would provide early-diagnosis tools. In fact, detecting changes of thermal origin in ultrasonic echo spectra could be useful as an early complementary indicator of infections, inflammations, or cancer. But the effective clinic applications to diagnosis of thermometry ultrasonic techniques, proposed previously, require additional research. Before their implementations with ultrasonic probes and real-time electronic and processing systems, rigorous analyses must be still made over transient echotraces acquired from well-controlled biological and computational phantoms, to improve resolutions and evaluate clinic limitations. It must be based on computing improved signal-processing algorithms emulating tissues responses. Some related parameters in echo-traces reflected by semiregular scattering tissues must be carefully quantified to get a precise processing protocols definition. In this paper, approaches for non-invasive spectral ultrasonic detection are analyzed. Extensions of author's innovations for ultrasonic thermometry are shown and applied to computationally modeled echotraces from scattered biological phantoms, attaining high resolution (better than 0.1°C). Computer methods are provided for viability evaluation of thermal estimation from echoes with distinct noise levels, difficult to be interpreted, and its effectiveness is evaluated as possible diagnosis tool in scattered tissues like liver

A Framework for Temperature Imaging using the Change in Backscattered Ultrasonic Signals

Hyperthermia is a cancer treatment that elevates tissue temperature to 40 to 43oC. It would benefit from a non-invasive, safe, inexpensive and convenient thermometry to monitor heating patterns. Ultrasound is a modality that meets these requirements. In our initial work, using both prediction and experimental data, we showed that the change in the backscattered energy: CBE) is a potential parameter for TI. CBE, however, was computed in a straightforward yet ad hoc manner. In this work, we developed and exploited a mathematical representation for our approach to TI to optimize temperature accuracy. Non-thermal effects of noise and motion confound the use of CBE. Assuming additive white Gaussian noise, we applied signal averaging and thresholding to reduce noise effects. Our motion compensation algorithms were also applied to images with known motion to evaluate factors affecting the compensation performance. In the framework development, temperature imaging was modeled as a problem of estimating temperature from the random processes resulting from thermal changes in signals. CBE computation was formalized as a ratio between two random variables. Mutual information: MI) was studied as an example of possible parameters for temperature imaging based on the joint distributions. Furthermore, a maximum likelihood estimator: MLE) was developed. Both simulations and experimental results showed that noise effects were reduced by signal averaging. The motion compensation algorithms proved to be able to compensate for motion in images and were improved by choosing appropriate interpolation methods and sample rates. For images of uniformly distributed scatterers, CBE and MI can be computed independent of SNR to improve the temperature accuracy. The application of the MLE also showed improvements in temperature accuracy compared to the energy ratio from the signal mean in simulations. The application of the framework to experimental data requires more work to implement noise reduction approaches in 3D heating experiments. The framework identified ways in which we were able to reduce the effects of both noise and motion. The framework formalized our approaches to temperature imaging, improved temperature accuracy in simulations, and can be applied to experimental data if the noise reduction approaches can be implemented for 3D experiments

Doctor of Philosophy

dissertationMagnetic Resonance guided High Intensity Focused Ultrasound (MRgHIFU) treatments are a promising modality for cancer treatments in which a focused beam of ultrasound energy is used to kill tumor tissue. However, obstacles still exist to its widespread clinical implementation, including long treatment times. This research demonstrates reductions in treatment times through intelligent selection of the usercontrollable parameters, including: the focal zone treatment path, focal zone size, focal zone spacing, and whether to treat one or several focal zone locations at any given time. Several treatments using various combinations of these parameters were simulated using a finite difference method to solve the Pennes bio-heat transfer equation for an ultrasonically heated tissue region with a wide range of acoustic, thermal, geometric, and tumor properties. The total treatment time was iteratively optimized using either a heuristic method or routines included in the Matlab software package, with constraints imposed for patient safety and treatment efficacy. The results demonstrate that large reductions in treatment time are possible through the intelligent selection of user-controllable treatment parameters. For the treatment path, treatment times are reduced by as much as an order of magnitude if the focal zones are arranged into stacks along the axial direction and a middle-front-back ordering is followed. For situations where normal tissue heating constraints are less stringent, these focal zones should have high levels of adjacency to further decrease treatment times; however, adjacency should be reduced in some cases where normal tissue constraints are more stringent. Also, the use of smaller, more concentrated focal zones produces shorter treatment times than larger, more diluted focal zones, a result verified in an agar phantom model. Further, focal zones should be packed using only a small amount of overlap in the axial direction and with a small gap in the transverse direction. These studies suggest that all treatment time reductions occur due to selection of parameters that advantageously use mechanisms of decreasing the focal zone size to concentrate the power density, increasing thermal superposition in the tumor, decreasing thermal superposition in the normal tissue, and advantageously using nonlinear rates of thermal dose deposition with increasing temperature

Numerical Modelling of Magnetic Nanoparticle Behavior in an Alternating Magnetic Field Based on Multiphysics Coupling

In magnetic nanoparticle hyperthermia, the magnetic nanoparticles (MNPs) start oscilla- tions when they are exposed to an alternating magnetic field, which may generate ultra- sound waves. These resulting oscillations of nanoparticles can lead to the movement of drug carrier liposomes. In this study, a multiphysics coupling model of magnetic nanoparticle behavior in an alternating magnetic field was developed, implementing solid mechanics compliance parameters and piezomagnetic coupling matrices. A detailed sensitivity study was conducted to to examine the effects of size and elastic modulus of MNPs, distribution and distance between two MNPs, elasticity and viscosity of the glycerol medium and mesh element sizes on the output displacement signals of MNPs. The results indicated that mag- netic nanoparticles undergo some displacements when they are exposed to an alternating magnetic field. These oscillations may generate ultrasound waves, though the amount of displacement for each nanoparticle is negligibly small. It is expected that aggregated nanoparticles result in much higher oscillations.Ministry of Science and Innovation, Spain grant numbers PID2019-106947RA-C2FEDER EQC2018-004508-