Spatially-segmented undersampled MRI temperature reconstruction for transcranial MR-guided focused ultrasound

Spatially-segmented undersampled MRI temperature reconstruction for transcranial MR-guided focused ultrasound

Journal of Therapeutic Ultrasound, May 2017

28560040

Pooja Gaur, Beat Werner, Xue Feng, Samuel W. Fielden, Craig H. Meyer, William A. Grissom

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.