Reconstruction techniques for cardiac cine MRI

1.

Mojibian H, Pouraliakbar H (2018) Cardiac magnetic resonance imaging. In: Maleki M, Alizadehasl A, Haghjoo M (ed) Practical Cardiology. Elsevier, chap. 8, p 159–166. DOI: https://doi.org/10.1016/B978-0-323-51149-0.00008-0 CrossRef

2.

Pfeiffer MP, Biederman RW (2015) Cardiac MRI: a general overview with emphasis on current use and indication. Med Clin North Am 99(4):849–861. https://doi.org/10.1016/j.mcna.2015.02.011 PubMedCrossRef

3.

Sakuma H, Takeda K, Higgins CB (1999) Fast magnetic resonance imaging of the heart. Eur J Radiol 29(2):101–113. https://doi.org/10.1016/S0720-048X(98)00173-9 PubMedCrossRef

4.

Constantine G, Shan K, Flamm SD, Sivananthan MU (2004) Role of MRI in clinical cardiology. Lancet 363(9427):2162–2171. https://doi.org/10.1016/S0140-6736(04)16509-4 PubMedCrossRef

5.

Frangi AF, Niessen WJ, Viergever MA (2001) Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Trans Med Imaging 20(1):2–5. https://doi.org/10.1109/42.906421 PubMedCrossRef

6.

Jeudy J, White CS (2008) Cardiac magnetic resonance imaging: techniques and principles. Semin Roentgenol 43(3):173–182. Cardiac imaging, Part II. https://doi.org/10.1053/j.ro.2008.02.003 PubMedCrossRef

7.

Taylor AM, Bogaert J (2012) Cardiovascular MR imaging planes and segmentation. In: Bogaert J, Dymarkowski S, Taylor AM, Muthurangu V (eds) Clinical cardiac MRI. Springer, Berlin, pp 93–107. https://doi.org/10.1007/174_2011_333 CrossRef

8.

Castellano JM, Narula J, Castillo J, Fuster V (2014) Promoting cardiovascular health worldwide: strategies, challenges, and opportunities. Rev Esp Cardiol (Engl Ed) 67(9):724–730. https://doi.org/10.1016/j.rec.2014.01.023 CrossRef

9.

Di Cesare E, Cademartiri F, Carbone I et al (2013) Clinical indications for the use of cardiac MRI. By the SIRM study group on cardiac imaging. Radiol Med 118(5):752–798. https://doi.org/10.1007/s11547-012-0899-2 PubMedCrossRef

10.

Gupta V, Kirili HA, Hendriks EA et al (2012) Cardiac MR perfusion image processing techniques: a survey. Med Image Anal 16(4):767–785. https://doi.org/10.1016/j.media.2011.12.005 PubMedCrossRef

11.

Amano Y, Yanagisawa F, Tachi M et al (2017) Three-dimensional cardiac MR imaging: related techniques and clinical applications. Magn Reson Med Sci 16(3):183–189. https://doi.org/10.2463/mrms.rev.2016-0116 PubMedPubMedCentralCrossRef

12.

Axel L, Montillo A, Kim D (2005) Tagged magnetic resonance imaging of the heart: a survey. Med Image Anal 9(4):376–393. https://doi.org/10.1016/j.media.2005.01.003 PubMedCrossRef

13.

Mavrogeni S, Apostolou D, Argyriou P et al (2017) T1 and T2 mapping in cardiology: mapping the obscure object of desire. Cardiology 138:207–217. https://doi.org/10.1159/000478901 PubMedCrossRef

14.

Scott AD, Keegan J, Firmin DN (2009) Motion in cardiovascular MR imaging. Radiology 250(2):331–351. https://doi.org/10.1148/radiol.2502071998 PubMedCrossRef

15.

Catana C (2015) Motion correction options in PET/MRI. Semin Nucl Med 45(3):212–223. https://doi.org/10.1053/j.semnuclmed.2015.01.001 PubMedPubMedCentralCrossRef

16.

Bluemke DA, Boxerman JL, Atalar E, McVeigh ER (1997) Segmented K-space cine breath-hold cardiovascular MR imaging: Part 1. Principles and technique. AJR Am J Roentgenol 169(2). https://doi.org/10.2214/ajr.169.2.9242742 PubMedCrossRef

17.

Larson AC, White RD, Laub G, McVeigh ER, Li D, Simonetti OP (2004) Self-gated cardiac cine MRI. Magn Reson Med 51(1):93–102. https://doi.org/10.1002/mrm.10664 PubMedPubMedCentralCrossRef

18.

Crowe ME, Larson AC, Zhang Q et al (2004) Automated rectilinear self-gated cardiac cine imaging. Magn Reson Med 52(4):782–788. https://doi.org/10.1002/mrm.20212 PubMedCrossRef

19.

Krämer M, Herrmann KH, Biermann J, Reichenbach J (2014) Retrospective reconstruction of cardiac cine images from golden-ratio radial MRI using one-dimensional navigators. J Magn Reson Imaging 40(2):413–422. https://doi.org/10.1002/jmri.24364 PubMedCrossRef

20.

Chitiboi T, Feng L, Ramb R, Otazo R, Axel L (2018) Reconstruction of arrhythmic cardiac cycles in patients with atrial fibrillation. In: Proceedings of the International Society for Magnetic Resonance in Medicine, Paris, 2018

21.

Pang J, Sharif B, Fan Z et al (2014) ECG and navigator-free four-dimensional whole-heart coronary MRA for simultaneous visualization of cardiac anatomy and function. Magn Reson Med 72(5):1208–1217. https://doi.org/10.1002/mrm.25450 PubMedPubMedCentralCrossRef

22.

Peters DC, Nezafat R, Eggers H, Stehning C, Manning WJ (2008) 2D free-breathing dual navigator-gated cardiac function validated against the 2D breath-hold acquisition. J Magn Reson Imaging 28(3):773–777. https://doi.org/10.1002/jmri.21417 PubMedPubMedCentralCrossRef

23.

Uribe S, Muthurangu V, Boubertakh R et al (2007) Whole-heart cine MRI using real-time respiratory self-gating. Magn Reson Med 57(3):606–613. https://doi.org/10.1002/mrm.21156 PubMedCrossRef

24.

Piccini D, Littmann A, Nielles-Vallespin S, Zenge M (2012) Respiratory self-navigation for whole-heart bright-blood coronary MRI: methods for robust isolation and automatic segmentation of the blood pool. Magn Reson Med 68(2):571–579. https://doi.org/10.1002/mrm.23247 PubMedCrossRef

25.

Usman M, Vaillant G, Atkinson D, Schaeffter T, Prieto C (2014) Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magn Reson Med 72(4):1130–1140. https://doi.org/10.1002/mrm.25010 PubMedCrossRef

26.

Larson AC, Kellman P, Arai A et al (2005) Preliminary investigation of respiratory self-gating for free-breathing segmented cine MRI. Magn Reson Med 53(1):159–168. https://doi.org/10.1002/mrm.20331 CrossRef

27.

Chen X, Usman M, Baumgartner CF et al (2017) High-resolution self-gated dynamic abdominal MRI using manifold alignment. IEEE Trans Med Imaging 36(4):960–971. https://doi.org/10.1109/TMI.2016.2636449 PubMedCrossRef

28.

Liu J, Spincemaille P, Codella N, Nguyen T, Prince M, Wang Y (2010) Respiratory and cardiac self-gated free-breathing cardiac CINE imaging with multiecho 3D hybrid radial SSFP acquisition. Magn Reson Med 63(5):1230–1237. https://doi.org/10.1002/mrm.22306 PubMedPubMedCentralCrossRef

29.

Tsao J (2010) Ultrafast imaging: principles, pitfalls, solutions, and applications. J Magn Reson Imaging 32(2):252–266. https://doi.org/10.1002/jmri.22239 PubMedCrossRef

30.

Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42(5):952–962. https://doi.org/10.1002/(SICI)1522-2594(199911)42:5h952::AID-MRM16i3.0.CO;2-S PubMedCrossRef

31.

Griswold MA, Jakob PM, Heidemann RM et al (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47(6):1202–1210. https://doi.org/10.1002/mrm.10171 PubMedCrossRef

32.

Stehling M, Turner R, Mansfield P (1991) Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science 254(5028):43–50. https://doi.org/10.1126/science.1925560 PubMedCrossRef

33.

Winkelmann S, Schaeffter T, Koehler T, Eggers H, Doessel O (2007) An optimal radial profile order based on the Golden Ratio for time-resolved MRI. IEEE Trans Med Imaging 26(1):68–76. https://doi.org/10.1109/TMI.2006.885337 PubMedCrossRef

34.

Block K, Chandarana H, Milla S et al (2014) Towards routine clinical use of radial stack-of-stars 3D gradient-echo sequences for reducing motion sensitivity. J Korean Soc Magn Reson Med 18(2):87–106. https://doi.org/10.13104/jksmrm.2014.18.2.87 CrossRef

35.

Piccini D, Littmann A, Nielles-Vallespin S, Zenge MO (2011) Spiral phyllotaxis: the natural way to construct a 3D radial trajectory in MRI. Magn Reson Med 66(4):1049–1056. https://doi.org/10.1002/mrm.22898 PubMedCrossRef

36.

Baron CA, Dwork N, Pauly JM, Nishimura DG (2018) Rapid compressed sensing reconstruction of 3D non-Cartesian MRI. Magn Reson Med 79(5):2685–2692. https://doi.org/10.1002/mrm.26928 PubMedCrossRef

37.

Cheng JY, Zhang T, Ruangwattanapaisarn N et al (2015) Free-breathing pediatric MRI with nonrigid motion correction and acceleration. J Magn Reson Imaging 42(2):407–420. https://doi.org/10.1002/jmri.24785 PubMedCrossRef

38.

Prieto C, Doneva M, Usman M et al (2015) Highly efficient respiratory motion compensated free-breathing coronary mra using golden-step Cartesian acquisition. J Magn Reson Imaging 41(3):738–746. https://doi.org/10.1002/jmri.24602 PubMedCrossRef

39.

Usman M, Ruijsink B, Nazir M, Cruz G, Prieto C (2017) Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory. Magn Reson Imaging 38:129–137. https://doi.org/10.1016/j.mri.2016.12.021 PubMedPubMedCentralCrossRef

40.

Han F, Zhou Z, Cao M, Yang Y, Sheng K, Hu P (2017) Respiratory motion‐resolved, self-gated 4D‐MRI using rotating cartesian k‐space (ROCK). Med Phys 44(4):1359–1368. https://doi.org/10.1002/mp.12139 PubMedCrossRef

41.

Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306. https://doi.org/10.1109/TIT.2006.871582 CrossRef

42.

Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195. https://doi.org/10.1002/mrm.21391 PubMedCrossRef

43.

Adcock B, Hansen A, Roman B, Teschke G (2014) Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. In: Hawkes PW (ed) Advances in imaging and electron physics, vol. 182. Elsevier, pp 187–279. https://doi.org/10.1016/B978-0-12-800146-2.00004-7

44.

Otazo R, Cands E, Sodickson DK (2015) Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 73(3):1125–1136. https://doi.org/10.1002/mrm.25240 PubMedCrossRef

45.

Campbell-Washburn AE, Tavallaei MA, Pop M et al (2017) Real-time MRI guidance of cardiac interventions. J Magn Reson Imaging 46(4):935–950. https://doi.org/10.1002/jmri.25749 PubMedCrossRef

46.

Walsh DO, Gmitro AF, Marcellin MW (2000) Adaptive reconstruction of phased array MR imagery. Magn Reson Med 43(5):682–690. https://doi.org/10.1002/(SICI)1522-2594(200005)43:5h682::AID-MRM10i3.0.CO;2-G PubMedCrossRef

47.

Buehrer M, Pruessmann KP, Boesiger P, Kozerke S (2007) Array compression for MRI with large coil arrays. Magn Reson Med 57(6):1131–1139. https://doi.org/10.1002/mrm.21237 PubMedCrossRef

48.

Zhang T, Pauly JM, Vasanawala SS, Lustig M (2013) Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med 69(2):571–582. https://doi.org/10.1002/mrm.24267 PubMedCrossRef

49.

Feng Z, Guo H, Wang Y et al (2014) GPU accelerated high-dimensional compressed sensing MRI. In: 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, pp 648–651. https://doi.org/10.1109/ICARCV.2014.7064380

50.

Xu R, Wright GA (2016) GPU accelerated dynamic respiratory motion model correction for MRI-guided cardiac interventions. Comput Methods Programs Biomed 136:31–43. https://doi.org/10.1016/j.cmpb.2016.08.003 PubMedCrossRef

51.

Hansen MS, Sørensen TS (2013) Gadgetron: an open source framework for medical image reconstruction. Magn Reson Med 69(6):1768–1776. https://doi.org/10.1002/mrm.24389 PubMedCrossRef

52.

Uecker M, Ong F, Tamir JI et al (2015) Berkeley advanced reconstruction toolbox. In: Proceedings - Annual Meeting of the International Society for Magnetic Resonance in Medicine, vol 23, p 2486

53.

Simmross-Wattenberg F, Rodríguez-Cayetano M, del Val JR et al (2018) OpenCLIPER: an OpenCL-based C++ Framework for overhead-reduced medical image processing and reconstruction on heterogeneous devices. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2018.2869421 PubMedCrossRef

54.

Wang S, Su Z, Ying L et al (2016) Accelerating magnetic resonance imaging via deep learning. IEEE 13th International Symposium on Biomedical Imaging (ISBI):514–517. https://doi.org/10.1109/ISBI.2016.7493320

55.

Yang Y, Sun J, Li H, Xu Z (2016) Deep ADMM-Net for compressive sensing MRI. In: Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R (eds) Advances in neural information processing systems 29. NIPS 2016. Curran Associates, Inc., New York, pp 10–18

56.

Hammernik K, Klatzer T, Kobler E et al (2018) Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 79(6):3055–3071. https://doi.org/10.1002/mrm.26977 PubMedPubMedCentralCrossRef

57.

Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D (2018) A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 37(2):491–503. https://doi.org/10.1109/TMI.2017.2760978 PubMedCrossRef

58.

Lee D, Yoo J, Ye JC (2017) Deep residual learning for compressed sensing MRI. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI), 2017

59.

Tsao J, Boesiger P, Pruessmann KP (2003) k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 50(5):1031–1042. https://doi.org/10.1002/mrm.10611 PubMedCrossRef

60.

Huang F, Akao J, Vijayakumar S, Duensing GR, Limkeman M (2005) k-t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor. Magn Reson Med 54(5):1172–1184. https://doi.org/10.1002/mrm.20641 PubMedCrossRef

61.

Lustig M, Santos JM, Donoho DL, Pauly JM (2006) k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In: Proceedings of the 13th International Society for Magnetic Resonance in Medicine, 2006

62.

Jung H, Sung K, Nayak KS, Kim EY, Ye JC (2009) k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 61(1):103–116. https://doi.org/10.1002/mrm.21757 PubMedCrossRef

63.

Pedersen H, Kozerke S, Ringgaard S, Nehrke K, Kim WY (2009) k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis. Magn Reson Med 62(3):706–716. https://doi.org/10.1002/mrm.22052 PubMedCrossRef

64.

Christodoulou AG, Brinegar C, Haldar JP et al (2010) High-resolution cardiac MRI using partially separable functions and weighted spatial smoothness regularization. Conf Proc IEEE Eng Med Biol Soc 2010:871–874. https://doi.org/10.1109/IEMBS.2010.5627889

65.

Lingala S, Hu Y, Dibella E, Jacob M (2011) Accelerated dynamic MRI exploiting sparsity and low-rank structure: K-t SLR. IEEE Trans Med Imaging 30(5):1042–1054. https://doi.org/10.1109/TMI.2010.2100850 PubMedPubMedCentralCrossRef

66.

Hansen MS, Sørensen TS, Arai AE, Kellman P (2012) Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction. Magn Reson Med 68(3):741–750. https://doi.org/10.1002/mrm.23284 PubMedCrossRef

67.

Asif MS, Hamilton L, Brummer M, Romberg J (2013) Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI. Magn Reson Med 70(3):800–812. https://doi.org/10.1002/mrm.24524 PubMedCrossRef

68.

Feng L, Srichai MB, Lim RP et al (2013) Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE. Magn Reson Med 70(1):64–74. https://doi.org/10.1002/mrm.24440 PubMedPubMedCentralCrossRef

69.

Usman M, Atkinson D, Odille F et al (2013) Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn Reson Med 70(2):504–516. https://doi.org/10.1002/mrm.24463 PubMedCrossRef

70.

Xue H, Kellman P, LaRocca G, Arai AE, Hansen MS (2013) High spatial and temporal resolution retrospective cine cardiovascular magnetic resonance from shortened free breathing real-time acquisitions. J Cardiovasc Magn Reson 15(1):102. https://doi.org/10.1186/1532-429X-15-102 PubMedPubMedCentralCrossRef

71.

Schmidt M, Ekinci O, Liu J et al (2013) Novel highly accelerated real-time CINE-MRI featuring compressed sensing with k-t regularization in comparison to TSENSE segmented and real-time Cine imaging. J Cardiovasc Magn Reson 15(1):P36. https://doi.org/10.1186/1532-429X-15-S1-P36

72.

Trémoulhéac B, Dikaios N, Atkinson D, Arridge SR (2014) Dynamic MR image reconstruction–separation from undersampled (k,t)-space via low-rank plus sparse prior. IEEE Trans Med Imaging 33(8):1689–1701. https://doi.org/10.1109/TMI.2014.2321190 PubMedCrossRef

73.

Wang Y, Ying L (2014) Compressed sensing dynamic cardiac cine MRI using learned spatiotemporal dictionary. IEEE Trans Biomed Eng 61(4):1109–1120. https://doi.org/10.1109/TBME.2013.2294939 PubMedCrossRef

74.

Vincenti G, Monney P, Chaptinel J et al (2014) Compressed sensing single–breath-hold CMR for fast quantification of LV function, volumes, and mass. JACC Cardiovasc Imaging 7(9):882–892. https://doi.org/10.1016/j.jcmg.2014.04.016 PubMed

75.

Royuela-del Val J, Usman M, Cordero-Grande L et al (2015) Single breath hold whole heart cine MRI with iterative groupwise cardiac motion compensation and sparse regularization (kt-WiSE). In: Proceedings of the 23rd International Society for Magnetic Resonance in Medicine, 2015

76.

Velikina JV, Samsonov AA (2015) Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO). Magn Reson Med 74(5):1279–1290. https://doi.org/10.1002/mrm.25513 PubMedCrossRef

77.

Poddar S, Jacob M (2016) Dynamic MRI using SmooThness Regularization on Manifolds (SToRM). IEEE Trans Med Imaging 35(4):1106–1115. https://doi.org/10.1109/TMI.2015.2509245 PubMedCrossRef

78.

Royuela-del Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C (2016) Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath-hold cardiac cine MRI. Magn Reson Med 75(4):1525–1536. https://doi.org/10.1002/mrm.25733 PubMedCrossRef

79.

Miao X, Lingala SG, Guo Y et al (2016) Accelerated cardiac cine MRI using locally low rank and finite difference constraints. Magn Reson Imaging 34(6):707–714. https://doi.org/10.1016/j.mri.2016.03.007 PubMedCrossRef

80.

Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R (2016) XD-GRASP: golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 75(2):775–788. https://doi.org/10.1002/mrm.25665 PubMedCrossRef

81.

Chen C, Li Y, Axel L, Huang J (2016) Real time dynamic MRI by exploiting spatial and temporal sparsity. Magn Reson Imaging 34(4):473–482. https://doi.org/10.1016/j.mri.2015.10.033 PubMedCrossRef

82.

Royuela-del Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C (2017) Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI. Magn Reson Med 77(3):1208–1215. https://doi.org/10.1002/mrm.26198 PubMedCrossRef

83.

Mohsin Y, Lingala S, DiBella E, Jacob M (2017) Accelerated dynamic MRI using patch regularization for implicit motion compensation. Magn Reson Med 77(3):1238–1248. https://doi.org/10.1002/mrm.26215 PubMedCrossRef

84.

Chen J, Liu S, Huang M (2017) Low-rank and sparse decomposition model for accelerating dynamic MRI reconstruction. J Healthc Eng 2017. https://doi.org/10.1155/2017/9856058

85.

Xu F, Han J, Wang Y et al (2017) Dynamic magnetic resonance imaging via nonconvex low-rank matrix approximation. IEEE Access 5:1958–1966. https://doi.org/10.1109/ACCESS.2017.2657645 CrossRef

86.

Roohi SF, Zonoobi D, Kassim AA, Jaremko JL (2017) Multi-dimensional low rank plus sparse decomposition for reconstruction of under-sampled dynamic MRI. Pattern Recogn 63:667–679. https://doi.org/10.1016/j.patcog.2016.09.040 CrossRef

87.

Royuela-del-Val J. et al (2017) Whole-heart single breath-hold cardiac cine: a robust motion-compensated compressed sensing reconstruction method. In: Zuluaga M, Bhatia K, Kainz B, Moghari M, Pace D (eds) Reconstruction, Segmentation, and Analysis of Medical Images. RAMBO 2016, HVSMR 2016. Lecture Notes in Computer Science. Springer, Cham, 10129:58–69CrossRef

88.

Wang Y, Cao N, Liu Z, Zhang Y (2017) Real-time dynamic MRI using parallel dictionary learning and dynamic total variation. Neurocomputing 238:410–419. https://doi.org/10.1016/j.neucom.2017.01.083 CrossRef

89.

Tolouee A, Alirezaie J, Babyn P (2018) Nonrigid motion compensation in compressed sensing reconstruction of cardiac cine MRI. Magn Reson Imaging 46:114–120. https://doi.org/10.1016/j.mri.2017.11.004 PubMedCrossRef

90.

Li YY, Rashid S, Cheng YJ et al (2018) Real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction. Magn Reson Imaging 53:98–104. https://doi.org/10.1016/j.mri.2018.07.008 PubMedPubMedCentralCrossRef

91.

Jung H, Park J, Yoo J, Ye JC (2010) Radial k-t FOCUSS for high-resolution cardiac cine MRI. Magn Reson Med 63(1):68–78. https://doi.org/10.1002/mrm.22172 PubMedCrossRef

92.

Usman M, Atkinson D, Heathfield E, Greil G, Schaeffter T, Prieto C (2015) Whole left ventricular functional assessment from two minutes free breathing multi-slice CINE acquisition. Phys Med Biol 60(7):N93–N107. https://doi.org/10.1088/0031-9155/60/7/N93 PubMedCrossRef

93.

Usman M, Atkinson D, Kolbitsch C, Schaeffter T, Prieto C (2015) Manifold learning based ECG-free free-breathing cardiac CINE MRI. J Magn Reson Imaging 41(6):1521–1527. https://doi.org/10.1002/jmri.24731 PubMedCrossRef

94.

Naegel B, Cernicanu A, Hyacinthe JN, Tognolini M, Valle JP (2009) SNR enhancement of highly-accelerated real-time cardiac MRI acquisitions based on non-local means algorithm. Med Image Anal 13(4):598–608. https://doi.org/10.1016/j.media.2009.05.006 PubMedCrossRef

95.

Wech T, Pickl W, Tran-Gia J et al (2014) Whole-heart cine MRI in a single breath-hold – a compressed sensing accelerated 3D acquisition technique for assessment of cardiac function. Rofo 186:37–41. https://doi.org/10.1055/s-0033-1350521 CrossRef

96.

Coppo S, Piccini D, Bonanno G et al (2015) Free-running 4D whole-heart self-navigated golden angle MRI: initial results. Magn Reson Med 74(5):1306–1316. https://doi.org/10.1002/mrm.25523 PubMedCrossRef

97.

Jeong D, Schiebler ML, Lai P, Wang K, Vigen KK, François CJ (2015) Single breath hold 3D cardiac cine MRI using kat-ARC: preliminary results at 1.5T. Int J Cardiovasc Imaging 31(4):851–857. https://doi.org/10.1007/s10554-015-0615-0 PubMedCrossRef

98.

Menchón-Lara RM, del Val JR, Godino-Moya A et al (2017) An efficient multi-resolution reconstruction scheme with motion compensation for 5D free-breathing whole-heart MRI. In: M. J. C et al (eds) Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment. LNCS, vol. 10555. Springer international publishing AG, chap. 14. https://doi.org/10.1007/978-3-319-67564-0_14

99.

Wetzl J, Schmidt M, Pontana F et al (2018) Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling. MAGMA 31(1):19–31. https://doi.org/10.1007/s10334-017-0624-1 CrossRef

100.

Feng L, Coppo S, Piccini D et al (2018) 5D whole-heart sparse MRI. Magn Reson Med 79(2):826–838. https://doi.org/10.1002/mrm.26745 PubMedPubMedCentralCrossRef

101.

Spincemaille P, Liu J, Nguyen T, Prince MR, Wang Y (2011) Z intensity-weighted position self-respiratory gating method for free-breathing 3D cardiac CINE imaging. Magn Reson Imaging 29(6):861–868. https://doi.org/10.1016/j.mri.2011.02.011 PubMedPubMedCentralCrossRef

102.

Fukushima K (1980) Magnetron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybernetics 36:193–202CrossRef

103.

Zhu B, Liu JZ, Rosen BR, Rosenl MS (2018) Image reconstruction by domain transform manifold learning. Nature 555:487–492PubMedCrossRef

104.

Quin C, Hajnal JV, Rueckert D, Schlemper J, Caballero J, Price A (2018) Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2018.2863670 PubMedCrossRef

105.

Yang G, Yu S, Dong H et al (2018) DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Trans Med Imaging 37(6):1310–1321PubMedCrossRef

106.

Ronneberge O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham

107.

Sandino CM, Dixit N, Vasanawala SS, Cheng JY (2017) Deep convolutional neural networks for accelerated dynamic magnetic resonance imaging. In: 31th Conference on Neural Information Processing Systems. Medical Imaging Meets NIPS Workshop, 2017

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Reconstruction techniques for cardiac cine MRI

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Chest imaging using signs, symbols, and naturalistic images: a practical guide for radiologists and non-radiologists

Dynamic magnetic resonance imaging of the female pelvic floor—a pictorial review

MRI assessment of hepatocellular carcinoma after locoregional therapy

Gender discrepancy in research activities during radiology residency

The elbow: review of anatomy and common collateral ligament complex pathology using MRI

A systematic review on the use of the breast lesion excision system in breast disease