Kim Pauly, Postdoctoral Research Mentor
Volumetric thermometry with fine spatiotemporal resolution is desirable to monitor MR-guided focused ultrasound (MRgFUS) procedures in the brain, but requires some form of accelerated imaging. Accelerated MR temperature imaging methods have been developed that undersample k-space and leverage signal correlations over time to suppress the resulting undersampling artifacts. However, in transcranial MRgFUS treatments, the water bath surrounding the skull creates signal variations that do not follow those correlations, leading to temperature errors in the brain due to signal aliasing.To eliminate temperature errors due to the water bath, a spatially-segmented iterative reconstruction method was developed. The method fits a k-space hybrid signal model to reconstruct temperature changes in the brain, and a conventional MR signal model in the water bath. It was evaluated using single-channel 2DFT Cartesian, golden angle radial, and spiral data from gel phantom heating, and in vivo 8-channel 2DFT data from a FUS thalamotomy. Water bath signal intensity in phantom heating images was scaled between 0-100% to investigate its effect on temperature error. Temperature reconstructions of retrospectively undersampled data were performed using the spatially-segmented method, and compared to conventional whole-image k-space hybrid (phantom) and SENSE (in vivo) reconstructions.At 100% water bath signal intensity, 3 ×-undersampled spatially-segmented temperature reconstruction error was nearly 5-fold lower than the whole-image k-space hybrid method. Temperature root-mean square error in the hot spot was reduced on average by 27 × (2DFT), 5 × (radial), and 12 × (spiral) using the proposed method. It reduced in vivo error 2 × in the brain for all acceleration factors, and between 2 × and 3 × in the temperature hot spot for 2-4 × undersampling compared to SENSE.Separate reconstruction of brain and water bath signals enables accelerated MR temperature imaging during MRgFUS procedures with low errors due to undersampling using Cartesian and non-Cartesian trajectories. The spatially-segmented method benefits from multiple coils, and reconstructs temperature with lower error compared to measurements from SENSE-reconstructed images. The acceleration can be applied to increase volumetric coverage and spatiotemporal resolution.
View details for DOI 10.1186/s40349-017-0092-0
View details for PubMedID 28560040
To reconstruct proton resonance frequency-shift temperature maps free of chemical shift distortions.Tissue heating created by thermal therapies such as focused ultrasound surgery results in a change in proton resonance frequency that causes geometric distortions in the image and calculated temperature maps, in the same manner as other chemical shift and off-resonance distortions if left uncorrected. We propose an online-compatible algorithm to correct these distortions in 2DFT and echo-planar imaging acquisitions, which is based on a k-space signal model that accounts for proton resonance frequency change-induced phase shifts both up to and during the readout. The method was evaluated with simulations, gel phantoms, and in vivo temperature maps from brain, soft tissue tumor, and uterine fibroid focused ultrasound surgery treatments.Without chemical shift correction, peak temperature and thermal dose measurements were spatially offset by approximately 1 mm in vivo. Spatial shifts increased as readout bandwidth decreased, as shown by up to 4-fold greater temperature hot spot asymmetry in uncorrected temperature maps. In most cases, the computation times to correct maps at peak heat were less than 10 ms, without parallelization.Heat-induced proton resonance frequency changes create chemical shift distortions in temperature maps resulting from MR-guided focused ultrasound surgery ablations, but the distortions can be corrected using an online-compatible algorithm. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.
View details for DOI 10.1002/mrm.25899
View details for PubMedID 26301458
View details for PubMedCentralID PMC4766074
Acceleration of magnetic resonance (MR) thermometry is desirable for several applications of MR-guided focused ultrasound, such as those requiring greater volume coverage, higher spatial resolution, or higher frame rates.We propose and validate a constrained reconstruction method that estimates focal temperature changes directly from k-space without spatial or temporal regularization. A model comprising fully-sampled baseline images is fit to undersampled k-space data, which removes aliased temperature maps from the solution space. Reconstructed temperature maps are compared to maps reconstructed using parallel imaging (iterative self-consistent parallel imaging reconstruction [SPIRiT]) and conventional hybrid thermometry, and temporally constrained reconstruction thermometry.Temporal step response simulations demonstrate finer temporal resolution and lower error in 4×-undersampled radial k-space reconstructions compared to temporally constrained reconstruction. Simulations show that the k-space method can achieve higher accelerations with multiple receive coils. Phantom heating experiments further demonstrate the algorithm's advantage over reconstructions relying on parallel imaging alone to overcome undersampling artifacts. In vivo model error comparisons show the algorithm achieves low temperature error at higher acceleration factors (up to 32× with a radial trajectory) than compared reconstructions.High acceleration factors can be achieved using the proposed temperature reconstruction algorithm, without sacrificing temporal resolution or accuracy.
View details for DOI 10.1002/mrm.25327
View details for Web of Science ID 000353240600023
View details for PubMedID 24935053