Model predictive filtering MR thermometry: Effects of model inaccuracies, k-space reduction factor, and temperature increase rate

Assessment of radio-frequency heating of a parallel transmit coil in a phantom using multi-echo proton resonance frequency shift thermometry

We propose a workflow for validating parallel transmission (pTx) radio-frequency (RF) magnetic field heating patterns using Proton-Resonance Frequency shift (PRF)-based MR thermometry. Electromagnetic (EM) and thermal simulations of a 7 T 8-channel dipole coil were done using commercially available software (Sim4Life) to assess RF heating. The fabrication method for a phantom with electrical properties matched to human tissue is also described, along with methods for its electrical and thermal characterisation. Energy was deposited to specific transmit channels, whilst acquiring 3D PRF data using a pair of interleaved RF shim transmit modes. A multi-echo readout and pre-scan stabilisation protocol were used for increased sensitivity and to correct for measurement-to-measurement instabilities. The electrical properties of the phantom were found to be within 10% of the intended values. Adoption of a 14-min stabilisation scan gave sufficient suppression of any evolving background spatial variation in the B₀ field to achieve <0.001 °C/mm thermometry drift over 10 min of subsequent scanning. Using two RF shim transmit modes enabled full phantom coverage and combining multiple echo times enabled a 13-54% improvement in the RMSE sensitivity to temperature changes. Combining multiple echoes reduced the peak RMSE by 45% and visually reduced measurement-to-measurement instabilities. A reference fibre optic probe showed temperature deviations from the PRF-estimated temperature to be smaller than 0.5 °C. Given the importance of RF safety in pTx applications, this workflow enables accurate validation of RF heating simulations with minimal additional hardware requirements.

MR thermometry for focused ultrasound monitoring utilizing model predictive filtering and ultrasound beam modeling

Background

A major challenge in using magnetic resonance temperature imaging (MRTI) to monitor focused ultrasound (FUS) applications is achieving high spatio-temporal resolution over a large field of view (FOV). This is important to accurately monitor all ultrasound (US) power depositions. Magnetic resonance (MR) subsampling in conjunction with thermal model-based reconstruction of the MRTI utilizing Pennes bioheat transfer equation (PBTE) is one promising approach. The thermal properties used in the thermal model are often estimated from a pre-treatment, low-power sonication.

Methods

In this proof-of-concept study we investigate the use of US simulations computed using the hybrid angular spectrum (HAS) method to estimate the US power deposition density Q, thereby avoiding the pre-treatment sonication and any potential tissue damage. MRTI reconstructions are performed using a thermal model-based reconstruction method called model predictive filtering (MPF). Experiments are performed in a homogeneous gelatin phantom and in a gelatin phantom with embedded plastic skull. MPF reconstructions are compared to separate sonications imaged with fully sampled data over a smaller FOV. Temperature root-mean-square errors (RMSE) and focal spot positions and shapes are evaluated.

Results

HAS simulations accurately predict the location of the focal spot (to within 1 mm) in both phantoms. Accurate temperature maps (RMSE below 1 °C), where the location of the focal spot agrees well with fully sampled “truth” (to within 1 mm), are also achieved in both phantoms.

Conclusions

HAS simulations can be used to accurately predict the focal spot location in homogeneous media and when focusing through an aberrating plastic skull. The HAS simulated power deposition (Q) patterns can be used in the MPF thermal model-based reconstruction to obtain accurate temperature maps with high spatio-temporal resolution over large FOVs.

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.