Mei-Lan Chu1, Hing-Chiu Chang2, Hsiao-Wen Chung3, Mustafa R Bashir4,5, Jing Cai6,7, Lei Zhang8, Duohua Sun1, Nan-Kuei Chen1,4,8,9. 1. Department of Biomedical Engineering, University of Arizona, 1127 E. James E. Rogers Way, P.O. Box 210020, Tucson, AZ, 85721-0020, USA. 2. Department of Diagnostic Radiology, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pok Fu Lam Road, Hong Kong, China. 3. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, Taiwan, 106. 4. Department of Radiology, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA. 5. Center for Advanced Magnetic Resonance Development, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA. 6. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y934, 9/F, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China. 7. Department of Radiation Oncology, Duke University Medical Center, 2301 Erwin Road, Durham, NC, 27710, USA. 8. Medical Physics Graduate Program, Duke University, 2424 Erwin Road, Hock Plaza, Suite 101, Durham, NC, 27705, USA. 9. Brain Imaging and Analysis Center, Duke University Medical Center, 40 Duke Medicine Circle, Room 414, Durham, NC, 27710, USA.
Abstract
PURPOSE: We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate. METHODS: The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method. RESULTS: Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI. CONCLUSION: The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences.
PURPOSE: We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate. METHODS: The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method. RESULTS: Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI. CONCLUSION: The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences.
Authors: M J White; D J Hawkes; A Melbourne; D J Collins; C Coolens; M Hawkins; M O Leach; D Atkinson Journal: Magn Reson Med Date: 2009-08 Impact factor: 4.668
Authors: Joseph Y Cheng; Tao Zhang; Nichanan Ruangwattanapaisarn; Marcus T Alley; Martin Uecker; John M Pauly; Michael Lustig; Shreyas S Vasanawala Journal: J Magn Reson Imaging Date: 2014-10-20 Impact factor: 4.813