Model Predictive Filtering MR Temperature Imaging for Laser‐Induced Interstitial Thermotherapy

ABSTRACT Purpose To evaluate the use of the Model Predictive Filtering (MPF) method to improve temporal resolution of magnetic resonance temperature imaging (MRTI) for monitoring laser interstitial thermal therapy (LITT) ablations. Methods Using a Green's function method for solving differential equations, a treatment‐specific power matrix Q was derived from a LITT heating and used in the Pennes bioheat equation (PBHE) to model a subsequent higher power heating and supplement subsampled k ‐space data. This MPF method was evaluated using both 3D segmented EPI data and a tissue mimicking phantom and clinical LITT treatment data after retrospective subsampling. Reconstruction accuracy was assessed via thermal dose and analysis of the hottest voxel and region‐of‐voxels over time. Results In the phantom data, temporal resolution equivalent to a 12‐slice acquisition was produced with larger fields‐of‐view (24 and 36 slices, R = 2 and 3) with good hottest voxel‐over‐time accuracy and 240 CEM 43 volume agreement (Dice similarity coefficient, DSC 0.7). In the in vivo data, MPF reconstruction showed excellent 240 CEM 43 volume agreement for both orthogonal slices (DSC 0.9 for R = 2 and 3). The sagittal and coronal slices showed excellent hottest voxel accuracy for subsampling of R 3, with an RMSE ≤ 1°C. Hottest voxel RMSE remained within 1°C–3°C up to a subsampling factor of 5. Conclusion The MPF algorithm allowed for large field‐of‐view (FOV) volumetric temperature imaging without decreasing temporal resolution in phantom heatings. Bi‐planar clinical treatment data reconstruction showed good accuracy for the application of MPF to in vivo data.