Chuan Huang1, Yoann Petibon2, Jinsong Ouyang3, Timothy G Reese4, Mark A Ahlman5, David A Bluemke5, Georges El Fakhri3. 1. Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114; Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115; and Departments of Radiology, Psychiatry, Stony Brook Medicine, Stony Brook, New York 11794. 2. Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114. 3. Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115. 4. Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115 and Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129. 5. Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland 20892.
Abstract
PURPOSE: Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. METHODS: Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. RESULTS: Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. CONCLUSIONS: Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.
PURPOSE: Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. METHODS: Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. RESULTS: Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. CONCLUSIONS: Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.
Authors: Nikolaos Dikaios; David Izquierdo-Garcia; Martin J Graves; Venkatesh Mani; Zahi A Fayad; Tim D Fryer Journal: Eur Radiol Date: 2011-09-22 Impact factor: 5.315
Authors: Mark A Griswold; Peter M Jakob; Robin M Heidemann; Mathias Nittka; Vladimir Jellus; Jianmin Wang; Berthold Kiefer; Axel Haase Journal: Magn Reson Med Date: 2002-06 Impact factor: 4.668
Authors: Piotr J Slomka; Hidetaka Nishina; Daniel S Berman; Xingping Kang; Cigdem Akincioglu; John D Friedman; Sean W Hayes; Usaf E Aladl; Guido Germano Journal: J Nucl Med Date: 2004-07 Impact factor: 10.057
Authors: Fabian Gigengack; Lars Ruthotto; Martin Burger; Carsten H Wolters; Xiaoyi Jiang; Klaus P Schäfers Journal: IEEE Trans Med Imaging Date: 2011-11-09 Impact factor: 10.048
Authors: Christoph Kolbitsch; Mark A Ahlman; Cynthia Davies-Venn; Robert Evers; Michael Hansen; Devis Peressutti; Paul Marsden; Peter Kellman; David A Bluemke; Tobias Schaeffter Journal: J Nucl Med Date: 2017-02-09 Impact factor: 10.057
Authors: Jian Wu; Thomas R Mazur; Su Ruan; Chunfeng Lian; Nalini Daniel; Hilary Lashmett; Laura Ochoa; Imran Zoberi; Mark A Anastasio; H Michael Gach; Sasa Mutic; Maria Thomas; Hua Li Journal: Med Image Anal Date: 2018-04-06 Impact factor: 8.545
Authors: Yoann Petibon; Nicolas J Guehl; Timothy G Reese; Behzad Ebrahimi; Marc D Normandin; Timothy M Shoup; Nathaniel M Alpert; Georges El Fakhri; Jinsong Ouyang Journal: Phys Med Biol Date: 2016-12-20 Impact factor: 3.609