Motion-compensated compressed sensing for dynamic contrast-enhanced MRI using regional spatiotemporal sparsity and region tracking: Block low-rank sparsity with motion-guidance (BLOSM)

Magnetic Resonance in Medicine

Volumn 72, Issue 4, 2014, Pages 1028-1038

  Chen, Xiao ^a   Salerno, Michael ^b   Yang, Yang ^a   Epstein, Frederick H ^a  

^a University of Virginia   (United States)

^b UNIVERSITY OF VIRGINIA   (United States)

Author keywords

Cardiac MRI; Compressed sensing; Dynamic contrast enhanced MRI; Motion compensation; Regional sparsity; Respiratory artifact

Indexed keywords

COMPRESSED SENSING; HEART; IMAGE ENHANCEMENT; MAGNETIC RESONANCE IMAGING; MEAN SQUARE ERROR; MOTION COMPENSATION; MOTION ESTIMATION; RESPIRATORY MECHANICS;

CARDIAC MRI; COMPRESSIVE SENSING; DYNAMIC CONTRAST ENHANCED MRI; MOTION ESTIMATION AND COMPENSATIONS; REGIONAL SPARSITY; RESPIRATORY ARTIFACT; ROOT MEAN SQUARED ERRORS; STRUCTURAL SIMILARITY INDICES;

MOTION TRACKING;

ARTICLE; BLOCK LOW RANK SPARSITY WITH MOTION GUIDANCE; BREATHING; CARDIOVASCULAR MAGNETIC RESONANCE; COMPRESSED SENSING; COMPUTER SIMULATION; CONTRAST ENHANCEMENT; CONTROLLED STUDY; EVALUATION STUDY; HEART PERFUSION; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; IMAGE RECONSTRUCTION; K T SPARSITY AND LOW RANK METHOD; MOTION; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; SIGNAL PROCESSING; ALGORITHM; ARTIFACT; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC USE; HEART DISEASE; IMAGE ENHANCEMENT; INFORMATION PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPATIOTEMPORAL ANALYSIS;

CONTRAST MEDIUM;

ALGORITHMS; ARTIFACTS; CONTRAST MEDIA; DATA COMPRESSION; HEART DISEASES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE IMAGING, CINE; MOTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPATIO-TEMPORAL ANALYSIS;

EID: 84920184781     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.25018     Document Type: Article

References (47)

1. LustigM, Donoho D, Pauly JM. SparseMRI: the application of compressed sensing for rapidMR imaging.Magn Reson Med 2007;58:1182-1195.
2. Gamper U, Boesiger P, Kozerke S. Compressed sensing in dynamic MRI. Magn Reson Med 2008;59:365-373.
3. Otazo R, Kim D, Axel L, Sodickson DK. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 2010;64:767-776.
4. Jung H, Sung K, Nayak KS, Kim EY, Ye JC. k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 2009;61:103-116.
5. Adluru G, Awate SP, Tasdizen T, Whitaker RT, Dibella EV. Temporally constrained reconstruction of dynamic cardiac perfusion MRI. Magn Reson Med 2007;57:1027-1036.
6. Lingala SG, Hu Y, DiBella E, Jacob M. Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 2011;30:1042-1054.
7. Usman M, Atkinson D, Odille F, Kolbitsch C, Vaillant G, Schaeffter T, Batchelor PG, Prieto C. Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn Reson Med 2013;70:504-516.
8. Lingala S, DiBella E, Chefd'hotel C, Nadar M, Jacob M. Motion compensated reconstruction for myocardial perfusion MRI. J Cardiovasc Magn Reson 2012;14(Suppl 1):M11.
9. Adluru G, DiBella E. A comparison of L1 and L2 norms as temporal constraints for reconstruction of undersampled dynamic contrast enhanced cardiac scans with respiratory motion. In Proceedings of the 17th Annual Meeting of ISMRM, Toronto, Canada, 2008. Abstract 340.
10. Lingala SG, Nadar M, Chefd'hotel C, Zhang L, Jacob M. Unified reconstruction and motion estimation in cardiac perfusion MRI. Proc IEEE Int Symp Biomed Imaging 2011:65-68.
11. Chen X, Salerno M, Antkowiak PF, Epstein FH. Motion-guided temporally-constrained compressed sensing for dynamic MRI. In Proceedings of 20th Annual Meeting of ISMRM, Melbourne, Australia, 2012. Abstract 1230.
12. Lustig M, Santos JM, Donoho D, Pauly JM. k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In Proceedings of 15th Annual Meeting of ISMRM, Seattle, Washington,USA, 2006.Abstract 2420.
13. Zhao B, Haldar JP, Liang ZP. PSF model-based reconstruction with sparsity constraint: algorithm and application to real-time cardiac MRI. Conf Proc IEEE Eng Med Biol Soc 2010;2010:3390-3393.
14. Hansen MS, Sorensen TS, Arai AE, Kellman P. Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction. Magn Reson Med 2012;68:741-750.
15. Batchelor PG, Atkinson D, Irarrazaval P, Hill DLG, Hajnal J, Larkman D. Matrix description of general motion correction applied to multishot images. Magn Reson Med 2005;54:1273-1280.
16. Asif MS, Hamilton L, Brummer M, Romberg J. Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI. Magn Reson Med 2013;70:800-812.
17. Chen X, Salerno M, Meyer CH, Epstein FH. Block LOw-Rank Sparsity with Motion guidance (BLOSM) for accelerated dynamic MRI. In Proceedings of 22nd Annual Meeting of ISMRM, Salt Lake City, Utah, USA, 2013. Abstract 4555.
18. Bergen JR, Anandan P, Hanna KJ, Hingorani R. Hierarchical modelbased motion estimation. Lect Notes Comput Sci 1992;588:237-252.
19. Makela T, Clarysse P, Sipila O, Pauna N, Pham QC, Katila T, Magnin IE. A review of cardiac image registration methods. IEEE Trans Med Imaging 2002;21:1011-1021.
20. Dufaux F, Moscheni F. Motion estimation techniques for digital TV-a review and a new contribution. Proc IEEE 1995;83:858-876.
21. Montoliu R, Pla F. Robust techniques in least squares-based motion estimation problems. Progr Pattern Recognit Speech Image Anal 2003;2905:62-70.
22. Zitova B, Flusser J. Image registration methods: a survey. Image Vision Comput 2003;21:977-1000.
23. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 2011;54:2033-2044.
24. Nicholas J. Tustison BBA. Diffeomorphic directly manipulated freeform deformation image registration via vector fields flows. In Proceedings of the 5th Workshop on Biomedical Image Registration, Nashville, Tennessee, USA. 2012;7359:31-39.
25. Thevenaz P, Blu T, Unser M. Interpolation revisited. IEEE Trans Med Imaging 2000;19:739-758.
26. Zhao B, Haldar JP, Brinegar C, Liang ZP. Lowrankmatrix recovery for realtime cardiacMRI. Proc IEEE Int Symp Biomed Imaging 2010:996-999.
27. Liang ZP. Spatiotemporal imaging with partially separable functions. Proc IEEE Int Symp Biomed Imaging 2007:988-991.
28. Zhao B, Haldar JP, Christodoulou AG, Liang ZP. Image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. IEEE Trans Med Imaging 2012;31:1809-1820.
29. Akcakaya M, Basha TA, Goddu B, Goepfert LA, Kissinger KV, Tarokh V, Manning WJ, Nezafat R. Low-dimensional-structure self-learning and thresholding: regularization beyond compressed sensing for MRI reconstruction. Magn Reson Med 2011;66:756-767.
30. Akcakaya M, Basha TA, Chan RH, Rayatzadeh H, Kissinger KV, Goddu B, Goepfert LA, Manning WJ, Nezafat R. Accelerated contrastenhanced whole-heart coronary MRI using low-dimensional-structure self-learning and thresholding. Magn Reson Med 2012;67:1434-1443.
31. Vitanis V, Manka R, Giese D, Pedersen H, Plein S, Boesiger P, Kozerke S. High resolution three-dimensional cardiac perfusion imaging using compartment-based k-t principal component analysis. Magn Reson Med 2011;65:575-587.
32. Dillencourt MB, Samet H, Tamminen M. A general-approach to connected-component labeling for arbitrary image representations. JACM 1992;39:253-280.
33. Majumdar A, Ward RK. An algorithm for sparse MRI reconstruction by Schatten p-norm minimization. Magn Reson Imaging 2011;29:408-417.
34. Combettes PL, Wajs VR. Signal recovery by proximal forwardbackward splitting. Multiscale Model Sim 2005;4:1168-1200.
35. Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 2007;16:2080-2095.
36. Jung H. k-t FOCUSS [Internet]. Available from http://bisp.kaist.ac.kr/ktFOCUSS.htm. Accessed May 1, 2013.
37. Adluru G, McGann C, Speier P, Kholmovski EG, Shaaban A, Dibella EV. Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging. Magn Reson Imaging 2009;29:466-473.
38. Petzschner FH, Ponce IP, Blaimer M, Jakob PM, Breuer FA. Fast MR parameter mapping using k-t principal component analysis. Magn Reson Med 2011;66:706-716.
39. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13:600-612.
40. Brinegar C, Zhang H, Wu YJ, Foley LM, Hitchens T, Ye Q, Ho C, Liang ZP. First-pass perfusion cardiac MRI using the Partially Separable Functions model with generalized support. Conf Proc IEEE Eng Med Biol Soc 2010;2010:2833-2836.
41. Feng X, Salerno M, Kramer CM, Meyer CH. Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging. Magn Reson Med 2012;69:1346-1356.
42. Yang JF, Zhang Y. Alternating direction algorithms for L(1)-problems in compressive sensing. Siam J Sci Comput 2011;33:250-278.
43. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952-962.
44. Lustig M, Pauly JM. SPIRiT: iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magnetic resonance in medicine 2010;64:457-471.
45. Liang D, Liu B, Wang JJ, Ying L. Accelerating SENSE Using Compressed Sensing. Magn Reson Med 2009;62:1574-1584.
46. Kim D, Dyvorne HA, Otazo R, Feng L, Sodickson DK, Lee VS. Accelerated phase-contrast cine MRI using k-t SPARSE-SENSE. Magn Reson Med 2012;67:1054-1064.
47. Murphy M, Alley M, Demmel J, Keutzer K, Vasanawala S, Lustig M. Fast l(1)-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime. IEEE Trans Med Imaging 2012;31:1250-1262.