Application of the compressed sensing technique to self-gated cardiac cine sequences in small animals
Corresponding Author
Paula Montesinos
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Correspondence to: Paula Montesinos, M.S.E., Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés (Madrid), Spain. E-mail:[email protected]Search for more papers by this authorJuan Felipe P.J. Abascal
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Search for more papers by this authorLorena Cussó
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Centro de Investigación Biomédica En Red de Salud Mental, CIBERSAM, Madrid, Spain
Search for more papers by this authorJuan José Vaquero
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Search for more papers by this authorManuel Desco
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Centro de Investigación Biomédica En Red de Salud Mental, CIBERSAM, Madrid, Spain
Search for more papers by this authorCorresponding Author
Paula Montesinos
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Correspondence to: Paula Montesinos, M.S.E., Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés (Madrid), Spain. E-mail:[email protected]Search for more papers by this authorJuan Felipe P.J. Abascal
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Search for more papers by this authorLorena Cussó
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Centro de Investigación Biomédica En Red de Salud Mental, CIBERSAM, Madrid, Spain
Search for more papers by this authorJuan José Vaquero
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Search for more papers by this authorManuel Desco
Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain
Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
Centro de Investigación Biomédica En Red de Salud Mental, CIBERSAM, Madrid, Spain
Search for more papers by this authorAbstract
Purpose
Self-gated cine sequences are a common choice for cardiac MRI in preclinical applications. The aims of our work were to apply the compressed sensing technique to IntraGateFLASH cardiac MRI studies on rats and to find the maximum acceleration factor achievable with this technique.
Theory and Methods
Our reconstruction method extended the Split Bregman formulation to minimize the total variation in both space and time. In addition, we analyzed the influence of the undersampling pattern on the acceleration factor achievable.
Results
Our results show that acceleration factors of up to 15 are achievable with our technique when appropriate undersampling patterns are used. The introduction of a time-varying random sampling clearly improved the efficiency of the undersampling schemes. In terms of computational efficiency, the proposed reconstruction method has been shown to be competitive as compared with the fastest methods found in the literature.
Conclusion
We successfully applied our compressed sensing technique to self-gated cardiac cine acquisition in small animals, obtaining an acceleration factor of up to 15 with almost unnoticeable image degradation. Magn Reson Med 72:369–380, 2014. © 2013 Wiley Periodicals, Inc.
REFERENCES
- 1Bovens 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.
- 2Madore 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.
10.1002/(SICI)1522-2594(199911)42:5<813::AID-MRM1>3.0.CO;2-S CAS PubMed Web of Science® Google Scholar
- 3Tsao 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.
- 4Aggarwal N, Bresler Y. Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI. Inverse Probl 2008; 24: 29.
- 5Lustig 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.
- 6Jung 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.
- 7Pedersen 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.
- 8Brinegar 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.
- 9Christodoulou 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.
- 10Zhao 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.
- 11Dong 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.
- 12Lingala 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.
- 13Usman 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.
- 14Usman M, Prieto C, Schaeffter T, Batchelor PG. k-t Group sparse: a method for accelerating dynamic MRI. Magn Reson Med 2011; 66: 1163–1176.
- 15Montefusco 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.
- 16Wech 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.
- 17Goud 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.
- 18Otazo 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.
- 19Moghari 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.
- 20Candes EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006; 52: 489–509.
- 21Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006; 52: 1289–1306.
- 22Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182–1195.
- 23Goldstein T, Osher S. The split Bregman method for L1-regularized problems. SIAM J Imaging Sci 2009; 2: 323–343.
- 24He 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.
10.1002/pamm.200700776 Google Scholar
- 25Cai JF, Osher S, Shen ZW. Split Bregman methods and frame based image restoration. Multiscale Model Simul 2009; 8: 337–369.
- 26Osher 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.
- 27Zhang H, Cheng L, Li J. Reweighted minimization model for MR image reconstruction with split Bregman method. Sci China Inf Sci 2012; 55: 2109–2118.
- 28Smith 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.
- 29Michailovich O, Rathi Y, Dolui S. Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans Med Imaging 2011; 30: 1100–1115.
- 30Ye 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.
- 31Wech 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.
10.1186/1532-429X-14-S1-P65 Google Scholar
- 32Wech 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.
10.1186/1532-429X-14-S1-P57 Google Scholar
- 33Motaal 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.
- 34Shi Y, Chang Q. Efficient algorithm for isotropic and anisotropic total variation deblurring and denoising. J Appl Math 2013; 2013: 14.
- 35Wang 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.
- 36van den Berg E, Friedlander MP. Probing the pareto frontier for basis pursuit solutions. SIAM J Sci Comput 2008; 31: 890–912.
- 37Becker S, Bobin J, Candes EJ. NESTA: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 2009; 4: 1–39.
- 38Abascal 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.
- 39Montesinos 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.
- 40Cai J, Osher S, Shen Z. Split Bregman methods and frame based image restoration. Multiscale Model Simul 2010; 8: 337–369.
- 41Hankiewicz 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.
- 42Starck 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.
- 43Bilen 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.
