Zhipeng Cao1,2,3, John C Gore1,2,3, William A Grissom1,2,3. 1. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. 2. Vanderbilt University Institute of Imaging Science, Nashville, Tennessee. 3. Department of Radiology, Vanderbilt University, Nashville, Tennessee.
Abstract
PURPOSE: To improve multichannel compressed sensing (CS) reconstruction for MR proton resonance frequency (PRF) shift thermography, with application to MRI-induced RF heating evaluation and MR guided high intensity focused ultrasound (MRgFUS) temperature monitoring. METHODS: A new compressed sensing reconstruction is proposed that enforces joint low rank and sparsity of complex difference domain PRF data between post heating and baseline images. Validations were performed on 4 retrospectively undersampled dynamic data sets in PRF applications, by comparing the proposed method to a previously described L1 and total variation- (TV-) based CS approach that also operates on complex difference domain data, and to a conventional low rank plus sparse (L+S) separation-based CS reconstruction applied to the original domain data. RESULTS: In all 4 retrospective validations, the proposed reconstruction method outperformed the conventional L+S and L1 +TV CS reconstruction methods with a 3.6× acceleration ratio in terms of temperature accuracy with respect to fully sampled data. For RF heating evaluation, the proposed method achieved RMS error of 12%, compared to 19% for the L+S method and 17% for the L1 +TV method. For in vivo MRgFUS thalamotomy, the peak temperature reconstruction errors were 19%, 31%, and 35%, respectively. CONCLUSION: The complex difference-based low rank and sparse model enhances compressibility for dynamic PRF temperature imaging applications. The proposed multichannel CS reconstruction method enables high acceleration factors for PRF applications including RF heating evaluation and MRgFUS sonication.
PURPOSE: To improve multichannel compressed sensing (CS) reconstruction for MR proton resonance frequency (PRF) shift thermography, with application to MRI-induced RF heating evaluation and MR guided high intensity focused ultrasound (MRgFUS) temperature monitoring. METHODS: A new compressed sensing reconstruction is proposed that enforces joint low rank and sparsity of complex difference domain PRF data between post heating and baseline images. Validations were performed on 4 retrospectively undersampled dynamic data sets in PRF applications, by comparing the proposed method to a previously described L1 and total variation- (TV-) based CS approach that also operates on complex difference domain data, and to a conventional low rank plus sparse (L+S) separation-based CS reconstruction applied to the original domain data. RESULTS: In all 4 retrospective validations, the proposed reconstruction method outperformed the conventional L+S and L1 +TV CS reconstruction methods with a 3.6× acceleration ratio in terms of temperature accuracy with respect to fully sampled data. For RF heating evaluation, the proposed method achieved RMS error of 12%, compared to 19% for the L+S method and 17% for the L1 +TV method. For in vivo MRgFUS thalamotomy, the peak temperature reconstruction errors were 19%, 31%, and 35%, respectively. CONCLUSION: The complex difference-based low rank and sparse model enhances compressibility for dynamic PRF temperature imaging applications. The proposed multichannel CS reconstruction method enables high acceleration factors for PRF applications including RF heating evaluation and MRgFUS sonication.
Authors: Li Feng; Robert Grimm; Kai Tobias Block; Hersh Chandarana; Sungheon Kim; Jian Xu; Leon Axel; Daniel K Sodickson; Ricardo Otazo Journal: Magn Reson Med Date: 2013-10-18 Impact factor: 4.668