Sensitivity of tissue properties derived from MRgFUS temperature data to input errors and data inclusion criteria: ex vivo study in porcine muscle.

This study evaluates the sensitivity of two magnetic resonance-guided focused ultrasound (MRgFUS) thermal property estimation methods to errors in required inputs and different data inclusion criteria. Using ex vivo pork muscle MRgFUS data, sensitivities to required inputs are determined by introducing errors to ultrasound beam locations (r error = -2 to 2 mm) and time vectors (t error = -2.2 to 2.2 s). In addition, the sensitivity to user-defined data inclusion criteria is evaluated by choosing different spatial (r fit = 1-10 mm) and temporal (t fit = 8.8-61.6 s) regions for fitting. Beam location errors resulted in up to 50% change in property estimates with local minima occurring at r error = 0 and estimate errors less than 10% when r error < 0.5 mm. Errors in the time vector led to property estimate errors up to 40% and without local minimum, indicating the need to trigger ultrasound sonications with the MR image acquisition. Regarding the selection of data inclusion criteria, property estimates reached stable values (less than 5% change) when r fit > 2.5 × FWHM, and were most accurate with the least variability for longer t fit. Guidelines provided by this study highlight the importance of identifying required inputs and choosing appropriate data inclusion criteria for robust and accurate thermal property estimation. Applying these guidelines will prevent the introduction of biases and avoidable errors when utilizing these property estimation techniques for MRgFUS thermal modeling applications.

Analytical estimation of ultrasound properties, thermal diffusivity, and perfusion using magnetic resonance-guided focused ultrasound temperature data.

For thermal modeling to play a significant role in treatment planning, monitoring, and control of magnetic resonance-guided focused ultrasound (MRgFUS) thermal therapies, accurate knowledge of ultrasound and thermal properties is essential. This study develops a new analytical solution for the temperature change observed in MRgFUS which can be used with experimental MR temperature data to provide estimates of the ultrasound initial heating rate, Gaussian beam variance, tissue thermal diffusivity, and Pennes perfusion parameter. Simulations demonstrate that this technique provides accurate and robust property estimates that are independent of the beam size, thermal diffusivity, and perfusion levels in the presence of realistic MR noise. The technique is also demonstrated in vivo using MRgFUS heating data in rabbit back muscle. Errors in property estimates are kept less than 5% by applying a third order Taylor series approximation of the perfusion term and ensuring the ratio of the fitting time (the duration of experimental data utilized for optimization) to the perfusion time constant remains less than one.

Effects of MRTI sampling characteristics on estimation of HIFU SAR and tissue thermal diffusivity.

While the non-invasive and three-dimensional nature of magnetic-resonance temperature imaging (MRTI) makes it a valuable tool for high-intensity focused ultrasound (HIFU) treatments, random and systematic errors in MRTI measurements may propagate into temperature-based parameter estimates used for pretreatment planning. This study assesses the MRTI effects of zero-mean Gaussian noise (SD = 0.0-2.0 °C), temporal sampling (tacq = 1.0-8.0 s), and spatial averaging (Res = 0.5-2.0 mm isotropic) on HIFU temperature measurements and temperature-based estimates of the amplitude and full width half maximum (FWHM) of the HIFU specific absorption rate and of tissue thermal diffusivity. The ultrasound beam used in simulations and ex vivo pork loin experiments has lateral and axial FWHM dimensions of 1.4 mm and 7.9 mm respectively. For spatial averaging simulations, beams with lateral FWHM varying from 1.2-2.2 mm are also assessed. Under noisy conditions, parameter estimates are improved by fitting to data from larger voxel regions. Varying the temporal sampling results in minimal changes in measured temperatures (<2% change) and parameter estimates (<5% change). For the HIFU beams studied, a spatial resolution of 1 × 1 × 3 mm(3) or smaller is required to keep errors in temperature and all estimated parameters less than 10%. By quantifying the errors associated with these sampling characteristics, this work provides researchers with appropriate MRTI conditions for obtaining estimates of parameters essential to pretreatment modeling of HIFU thermal therapies.

An analytical solution for improved HIFU SAR estimation.

Accurate determination of the specific absorption rates (SARs) present during high intensity focused ultrasound (HIFU) experiments and treatments provides a solid physical basis for scientific comparison of results among HIFU studies and is necessary to validate and improve SAR predictive software, which will improve patient treatment planning, control and evaluation. This study develops and tests an analytical solution that significantly improves the accuracy of SAR values obtained from HIFU temperature data. SAR estimates are obtained by fitting the analytical temperature solution for a one-dimensional radial Gaussian heating pattern to the temperature versus time data following a step in applied power and evaluating the initial slope of the analytical solution. The analytical method is evaluated in multiple parametric simulations for which it consistently (except at high perfusions) yields maximum errors of less than 10% at the center of the focal zone compared with errors up to 90% and 55% for the commonly used linear method and an exponential method, respectively. For high perfusion, an extension of the analytical method estimates SAR with less than 10% error. The analytical method is validated experimentally by showing that the temperature elevations predicted using the analytical method's SAR values determined for the entire 3D focal region agree well with the experimental temperature elevations in a HIFU-heated tissue-mimicking phantom.