Application of the compressed sensing technique to self-gated cardiac cine sequences in small animals

Magnetic Resonance in Medicine

Volumn 72, Issue 2, 2014, Pages 369-380

  Montesinos, Paula ^a,b   Abascal, Juan Felipe P J ^a,b   Cussõ, Lorena ^a,b,c   Vaquero, Juan José ^a,b   Desco, Manuel ^a,b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b HOSPITAL GENERAL UNIVERSITARIO GREGORIO MARAÑÓN   (Spain)

^c CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE SALUD MENTAL CIBERSAM   (Spain)

Author keywords

cardiac cine MRI; compressed sensing; self gated; Split Bregman; total variation; undersampling pattern

Indexed keywords

ANIMALS; COMPRESSED SENSING; COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; EFFICIENCY; SOLDERED JOINTS;

BREGMAN; CINE-MRI; SELF-GATED; TOTAL VARIATION; UNDER-SAMPLING;

HEART;

ALGORITHM; ANIMAL EXPERIMENT; ARTICLE; CARDIOVASCULAR MAGNETIC RESONANCE; COMPRESSED SENSING TECHNIQUE; FEMALE; HEART LEFT VENTRICLE MASS; HEART MUSCLE; NONHUMAN; QUALITY CONTROL; RAT; SELF GATED CARDIAC CINE SEQUENCE; SYSTOLE; ANATOMY AND HISTOLOGY; ANIMAL; ARTIFACT; CARDIAC GATED IMAGING; COMPUTER ASSISTED DIAGNOSIS; HEART; IMAGE ENHANCEMENT; INFORMATION PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; REPRODUCIBILITY; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; VETERINARY; WISTAR RAT;

ALGORITHMS; ANIMALS; ARTIFACTS; CARDIAC-GATED IMAGING TECHNIQUES; DATA COMPRESSION; FEMALE; HEART; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING, CINE; RATS; RATS, WISTAR; REPRODUCIBILITY OF RESULTS; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84904465647     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.24936     Document Type: Article

References (43)

1. Bovens SM, Boekhorst B, den Ouden K, van de Kolk KWA, Nauerth A, Nederhoff MGJ, Pasterkamp G, ten Hove M, van Echteld CJA,. Evaluation of infarcted murine heart function: comparison of prospectively triggered with self-gated MRI. NMR Biomed 2011; 24: 307-315.
2. Madore B, Glover GH, Pelc NJ,. Unaliasing by fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI. Magn Reson Med 1999; 42: 813-828.
3. Tsao J, Boesiger P, Pruessmann KP,. k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 2003; 50: 1031-1042.
4. Aggarwal N, Bresler Y,. Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI. Inverse Probl 2008; 24: 29.
5. Lustig M, Santos JM, Donoho D, Pauly JM,. k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In Proceedings of the 14th Annual Meeting of ISMRM, Seattle, Washington, USA, 2006. Abstract 2420.
6. 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.
7. Pedersen H, Kozerke S, Ringgaard S, Nehrke K, Kim WY,. k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis. Magn Reson Med 2009; 62: 706-716.
8. Brinegar C, Haosen Z, Wu YJL, Foley LM, Hitchens TK, Qing Y, Pocci D, Fan L, Chien H, Zhi-Pei L,. Real-time cardiac MRI using prior spatial-spectral information. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 4383-4386.
9. Christodoulou AG, Brinegar C, Haldar JP, Zhang H, Wu YJL, Foley LM, Hitchens TK, Ye Q, Ho C, Liang ZP,. High-resolution cardiac MRI using partially separable functions and weighted spatial smoothness regularization. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 871-874.
10. 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.
11. Dong L, DiBella EVR, Rong-Rong C, Ying L,. K-T ISD: compressed sensing with iterative support detection for dynamic MRI. In Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro, Chicago, Illinois, USA 2011.
12. 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.
13. Usman M, Prieto C, Odille F, Atkinson D, Schaeffter T, Batchelor PG,. A computationally efficient OMP-based compressed sensing reconstruction for dynamic MRI. Phys Med Biol 2011; 56: N99-N114.
14. Usman M, Prieto C, Schaeffter T, Batchelor PG,. k-t Group sparse: a method for accelerating dynamic MRI. Magn Reson Med 2011; 66: 1163-1176.
15. Montefusco LB, Lazzaro D, Papi S, Guerrini C,. A fast compressed sensing approach to 3D MR image reconstruction. IEEE Trans Med Imaging 2011; 30: 1064-1075.
16. Wech T, Lemke A, Medway D, Stork LA, Lygate CA, Neubauer S, Kostler H, Schneider JE,. Accelerating cine-MR imaging in mouse hearts using compressed sensing. J Magn Reson Imaging 2011; 34: 1072-1079.
17. Goud S, Hu Y, Jacob M,. Real-time cardiac MRI using low-rank and sparsity penalties. In Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, Rotterdam, the Netherlands, 2010. p 988-991.
18. 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.
19. Moghari MH, Akcakaya M, O'Connor A, et al. Compressed-Sensing Motion Compensation (CosMo): a joint prospective-retrospective respiratory navigator for coronary MRI. Magn Reson Med 2011; 66: 1674-1681.
20. Candes EJ, Romberg J, Tao T,. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006; 52: 489-509.
21. Donoho DL,. Compressed sensing. IEEE Trans Inf Theory 2006; 52: 1289-1306.
22. Lustig M, Donoho D, Pauly JM,. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182-1195.
23. Goldstein T, Osher S,. The split Bregman method for L1-regularized problems. SIAM J Imaging Sci 2009; 2: 323-343.
24. He L, Chang T-C, Osher S, Fang T, Speier P,. MR image reconstruction from undersampled data by using the iterative refinement procedure. PAMM 2007; 7: 1011207-1011208.
25. Cai JF, Osher S, Shen ZW,. Split Bregman methods and frame based image restoration. Multiscale Model Simul 2009; 8: 337-369.
26. Osher S, Burger M, Goldfarb D, Xu JJ, Yin WT,. An iterative regularization method for total variation-based image restoration. Multiscale Model Simul 2005; 4: 460-489.
27. Zhang H, Cheng L, Li J,. Reweighted minimization model for MR image reconstruction with split Bregman method. Sci China Inf Sci 2012; 55: 2109-2118.
28. Smith DS, Welch EB,. Non-sparse phantom for compressed sensing MRI reconstruction. In Proceedings of the 19th Annual Meeting of ISMRM, Montreal, Quebec, Canada, 2011. Abstract 2845.
29. Michailovich O, Rathi Y, Dolui S,. Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans Med Imaging 2011; 30: 1100-1115.
30. Ye X, Chen Y, Lin W, Huang F,. Fast MR image reconstruction for partially parallel imaging with arbitrary k-space trajectories. IEEE Trans Med Imaging 2011; 30: 575-585.
31. Wech T, Lygate C, Neubauer S, Kostler H, Schneider J,. Highly accelerated cardiac functional MRI in rodent hearts using compressed sensing and parallel imaging at 9.4T. J Cardiovasc Magn Reson 2012; 14 (Suppl 1): P65.
32. Wech T, Medway D, Lygate C, Neubauer S, Kostler H, Schneider J,. Accurate infarct-size measurements from accelerated, compressed sensing reconstructed cine-MRI images in mouse hearts. J Cardiovasc Magn Reson 2012; 14 (Suppl 1): P57.
33. Motaal AG, Coolen BF, Abdurrachim D, Castro RM, Prompers JJ, Florack LMJ, Nicolay K, Strijkers GJ,. Accelerated high-frame-rate mouse heart cine-MRI using compressed sensing reconstruction. NMR Biomed 2013; 26: 451-457.
34. Shi Y, Chang Q,. Efficient algorithm for isotropic and anisotropic total variation deblurring and denoising. J Appl Math 2013; 2013: 14.
35. Wang YL, Yang JF, Yin WT, Zhang Y,. A new alternating minimization algorithm for total variation image reconstruction. SIAM J Imaging Sci 2008; 1: 248-272.
36. van den Berg E, Friedlander MP,. Probing the pareto frontier for basis pursuit solutions. SIAM J Sci Comput 2008; 31: 890-912.
37. Becker S, Bobin J, Candes EJ,. NESTA: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 2009; 4: 1-39.
38. Abascal J, Chamorro-Servent J, Aguirre J, Arridge S, Correia T, Ripoll J, Vaquero JJ, Desco M,. Fluorescence diffuse optical tomography using the split Bregman method. Med Phys 2011; 38: 6275-6284.
39. Montesinos P, Abascal JFPJ, Chamorro J, Chavarrias C, Benito M, Vaquero JJ, Desco M,. High-resolution dynamic cardiac MRI on small animals using reconstruction based on split Bregman methodology. In Proceedings of the Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Valencia, California, USA, 2011. p 3462-3464.
40. Cai J, Osher S, Shen Z,. Split Bregman methods and frame based image restoration. Multiscale Model Simul 2010; 8: 337-369.
41. Hankiewicz JH, Goldspink PH, Buttrick PM, Lewandowski ED,. Principal strain changes precede ventricular wall thinning during transition to heart failure in a mouse model of dilated cardiomyopathy. Am J Physiol Heart Circ Physiol 2008; 294: H330-H336.
42. Starck JL, Elad M, Donoho DL,. Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans Image Process 2005; 14: 1570-1582.
43. Bilen C, Selesnick IW, Wang Y, Otazo R, Kim D, Axel L, Sodickson DK,. On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization. 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2010: 630-633.