AI Article Synopsis
- - A hybrid motion compensation algorithm called PCA-PDF for MR thermometry was optimized using an atlas image enhancement technique and was validated through experiments.
- - Research involved conducting tests on a 3T Philips MRI and a high-intensity focused ultrasound system, where motion was artificially induced in gelatin phantoms while monitoring temperature during hyperthermia.
- - The enhanced PCA-PDF algorithm significantly improved temperature accuracy during both periodic and sporadic motion, achieving a standard deviation of less than 1°C, which is a notable advancement over the original method.
Article Abstract
Purpose: A hybrid principal component analysis and projection onto dipole fields (PCA-PDF) MR thermometry motion compensation algorithm was optimized with atlas image augmentation and validated.
Methods: Experiments were conducted on a 3T Philips MRI and Profound V1 Sonalleve high intensity focused ultrasound (high intensity focused ultrasound system. An MR-compatible robot was configured to induce motion on custom gelatin phantoms. Trials with periodic and sporadic motion were introduced on phantoms while hyperthermia was administered. The PCA-PDF algorithm was augmented with a predictive atlas to better compensate for larger sporadic motion.
Results: During periodic motion, the temperature SD in the thermometry was improved from to C with both the original and augmented PCA-PDF application. For large sporadic motion, the augmented atlas improved the motion compensation from the original PCA-PDF correction from to C.
Conclusion: The PCA-PDF algorithm improved temperature accuracy to <1°C during periodic motion, but was not able to adequately address sporadic motion. By augmenting the PCA-PDF algorithm, temperature SD during large sporadic motion was also reduced to <1°C, greatly improving the original PCA-PDF algorithm.
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| http://dx.doi.org/10.1002/mrm.29988 | DOI Listing |
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