PURPOSE: Develop and evaluate the effectiveness of a T1-based correction method for errors in proton resonant frequency shift thermometry due to non-local field effects caused by heating in fatty breast tissues. METHODS: Computational models of human breast tissue were created by segmenting MRI data from a healthy human volunteer. MR-guided focused ultrasound (MRgFUS) heating and MR thermometry measurements were simulated in several locations in the heterogeneous segmented breast models. A T1-based correction method for PRF thermometry errors was applied and the maximum positive and negative errors and the root mean squared error (RMSE) in a region around each heating location was evaluated with and without correction. The method uses T1 measurements to estimate the temperature change in fatty tissues and correct for their influence. Experimental data from a heating study in cadaver breast tissue were analyzed, and the expected PRFS error computed. RESULTS: The simulated MR thermometry had maximum single voxel errors ranging between 10% and 18% when no correction was applied. Applying the correction led to a considerable improvement, lowering the maximum error range to 2%-5%. The 5th to 95th percentile interval of the temperature error distribution was also lowered with correction, from approximately 3.5 to 1°C. This correction worked even when T1 times were uniformly raised or lowered by 5%-10%. The experimental data showed predicted errors of 15%. CONCLUSIONS: This simulation study demonstrates that the T1-based correction method reduces MR thermometry errors due to non-local effects from heating in fatty tissues, potentially improving the accuracy of thermometry measurements during MRgFUS treatments. The presented correction method is reliant on having a patient-specific 3D model of the breast, and may be limited by the accuracy of the fat temperatures which in turn may be limited by noise or bias present in the T1 measurements.
PURPOSE: Develop and evaluate the effectiveness of a T1-based correction method for errors in proton resonant frequency shift thermometry due to non-local field effects caused by heating in fatty breast tissues. METHODS: Computational models of human breast tissue were created by segmenting MRI data from a healthy human volunteer. MR-guided focused ultrasound (MRgFUS) heating and MR thermometry measurements were simulated in several locations in the heterogeneous segmented breast models. A T1-based correction method for PRF thermometry errors was applied and the maximum positive and negative errors and the root mean squared error (RMSE) in a region around each heating location was evaluated with and without correction. The method uses T1 measurements to estimate the temperature change in fatty tissues and correct for their influence. Experimental data from a heating study in cadaver breast tissue were analyzed, and the expected PRFS error computed. RESULTS: The simulated MR thermometry had maximum single voxel errors ranging between 10% and 18% when no correction was applied. Applying the correction led to a considerable improvement, lowering the maximum error range to 2%-5%. The 5th to 95th percentile interval of the temperature error distribution was also lowered with correction, from approximately 3.5 to 1°C. This correction worked even when T1 times were uniformly raised or lowered by 5%-10%. The experimental data showed predicted errors of 15%. CONCLUSIONS: This simulation study demonstrates that the T1-based correction method reduces MR thermometry errors due to non-local effects from heating in fatty tissues, potentially improving the accuracy of thermometry measurements during MRgFUS treatments. The presented correction method is reliant on having a patient-specific 3D model of the breast, and may be limited by the accuracy of the fat temperatures which in turn may be limited by noise or bias present in the T1 measurements.
Authors: Paul Baron; Roel Deckers; Martijn de Greef; Laura G Merckel; Chris J G Bakker; Job G Bouwman; Ronald L A W Bleys; Maurice A A J van den Bosch; Lambertus W Bartels Journal: Magn Reson Med Date: 2013-12-17 Impact factor: 4.668
Authors: Caroline D Jordan; Bruce L Daniel; Kevin M Koch; Huanzhou Yu; Steve Conolly; Brian A Hargreaves Journal: J Magn Reson Imaging Date: 2012-08-03 Impact factor: 4.813
Authors: Allison Payne; Robb Merrill; Emilee Minalga; J Rock Hadley; Henrik Odeen; Lorne W Hofstetter; Sara Johnson; Christine Tunon de Lara; Sophie Auriol; Stephanie Recco; Erik Dumont; Dennis L Parker; Jean Palussiere Journal: IEEE Trans Biomed Eng Date: 2021-02-19 Impact factor: 4.538