Endogenous assessment of myocardial injury with single-shot model-based non-rigid motion-corrected T1 rho mapping.
Model-based
Motion correction
Myocardial
Non-rigid
Parameter mapping
T1ρ mapping
Journal
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
ISSN: 1532-429X
Titre abrégé: J Cardiovasc Magn Reson
Pays: England
ID NLM: 9815616
Informations de publication
Date de publication:
21 10 2021
21 10 2021
Historique:
received:
17
12
2020
accepted:
26
05
2021
entrez:
21
10
2021
pubmed:
22
10
2021
medline:
4
11
2021
Statut:
epublish
Résumé
Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction. A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference. Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01). The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.
Sections du résumé
BACKGROUND
Cardiovascular magnetic resonance T1ρ mapping may detect myocardial injuries without exogenous contrast agent. However, multiple co-registered acquisitions are required, and the lack of robust motion correction limits its clinical translation. We introduce a single breath-hold myocardial T1ρ mapping method that includes model-based non-rigid motion correction.
METHODS
A single-shot electrocardiogram (ECG)-triggered balanced steady state free precession (bSSFP) 2D adiabatic T1ρ mapping sequence that collects five T1ρ-weighted (T1ρw) images with different spin lock times within a single breath-hold is proposed. To address the problem of residual respiratory motion, a unified optimization framework consisting of a joint T1ρ fitting and model-based non-rigid motion correction algorithm, insensitive to contrast change, was implemented inline for fast (~ 30 s) and direct visualization of T1ρ maps. The proposed reconstruction was optimized on an ex vivo human heart placed on a motion-controlled platform. The technique was then tested in 8 healthy subjects and validated in 30 patients with suspected myocardial injury on a 1.5T CMR scanner. The Dice similarity coefficient (DSC) and maximum perpendicular distance (MPD) were used to quantify motion and evaluate motion correction. The quality of T1ρ maps was scored. In patients, T1ρ mapping was compared to cine imaging, T2 mapping and conventional post-contrast 2D late gadolinium enhancement (LGE). T1ρ values were assessed in remote and injured areas, using LGE as reference.
RESULTS
Despite breath holds, respiratory motion throughout T1ρw images was much larger in patients than in healthy subjects (5.1 ± 2.7 mm vs. 0.5 ± 0.4 mm, P < 0.01). In patients, the model-based non-rigid motion correction improved the alignment of T1ρw images, with higher DSC (87.7 ± 5.3% vs. 82.2 ± 7.5%, P < 0.01), and lower MPD (3.5 ± 1.9 mm vs. 5.1 ± 2.7 mm, P < 0.01). This resulted in significantly improved quality of the T1ρ maps (3.6 ± 0.6 vs. 2.1 ± 0.9, P < 0.01). Using this approach, T1ρ mapping could be used to identify LGE in patients with 93% sensitivity and 89% specificity. T1ρ values in injured (LGE positive) areas were significantly higher than in the remote myocardium (68.4 ± 7.9 ms vs. 48.8 ± 6.5 ms, P < 0.01).
CONCLUSIONS
The proposed motion-corrected T1ρ mapping framework enables a quantitative characterization of myocardial injuries with relatively low sensitivity to respiratory motion. This technique may be a robust and contrast-free adjunct to LGE for gaining new insight into myocardial structural disorders.
Identifiants
pubmed: 34670572
doi: 10.1186/s12968-021-00781-w
pii: 10.1186/s12968-021-00781-w
pmc: PMC8529795
doi:
Substances chimiques
Contrast Media
0
Gadolinium
AU0V1LM3JT
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
119Subventions
Organisme : European Research Council
ID : ERC n715093
Pays : International
Informations de copyright
© 2021. The Author(s).
Références
J Magn Reson Imaging. 2011 Jan;33(1):232-8
pubmed: 21182145
Acad Radiol. 2004 Jul;11(7):741-9
pubmed: 15217591
PLoS One. 2016 Mar 22;11(3):e0151144
pubmed: 27003184
J Cardiovasc Magn Reson. 2020 Apr 16;22(1):24
pubmed: 32299445
Magn Reson Med. 2013 May;69(5):1389-95
pubmed: 22736543
Magn Reson Imaging. 2009 Oct;27(8):1074-87
pubmed: 19559559
IEEE Trans Med Imaging. 2016 Jan;35(1):197-207
pubmed: 26259015
J Cardiovasc Magn Reson. 2020 Feb 3;22(1):12
pubmed: 32014001
Physiol Chem Phys Med NMR. 1984;16(1):49-55
pubmed: 6541347
Magn Reson Med. 1995 May;33(5):713-9
pubmed: 7596276
J Magn Reson Imaging. 2006 Mar;23(3):298-308
pubmed: 16450367
Radiology. 2011 Jun;259(3):712-9
pubmed: 21436087
Magn Reson Med. 2008 Jul;60(1):146-57
pubmed: 18581355
J Cardiovasc Magn Reson. 2019 Jan 10;21(1):5
pubmed: 30626437
N Engl J Med. 2000 Nov 16;343(20):1445-53
pubmed: 11078769
Magn Reson Med. 2012 Jun;67(6):1644-55
pubmed: 22135227
Radiology. 1998 Nov;209(2):483-9
pubmed: 9807578
Comput Biol Med. 2018 May 1;96:106-115
pubmed: 29567482
J Cardiovasc Magn Reson. 2014 Dec 20;16:104
pubmed: 25526973
Eur Radiol. 2019 Jun;29(6):3006-3016
pubmed: 30643944
Med Image Anal. 2016 Apr;29:65-78
pubmed: 26802910
Magn Reson Med. 2015 Apr;73(4):1469-82
pubmed: 24798588
Magn Reson Med. 2019 Dec;82(6):2118-2132
pubmed: 31321816
J Cardiovasc Magn Reson. 2012 Jun 15;14:37
pubmed: 22704222
Magn Reson Med. 2010 Nov;64(5):1453-60
pubmed: 20677236
J Cardiovasc Magn Reson. 2017 Feb 15;19(1):17
pubmed: 28196494
J Magn Reson. 2007 May;186(1):75-85
pubmed: 17291799
J Cardiol. 2015 Dec;66(6):520-6
pubmed: 25981868
J Magn Reson Imaging. 2016 Sep;44(3):723-31
pubmed: 26889749
Neuroimage. 2008 Jul 15;41(4):1199-205
pubmed: 18479942
J Magn Reson Imaging. 2018 May;47(5):1397-1405
pubmed: 28960659
Invest Radiol. 2016 Aug;51(8):505-12
pubmed: 26895195
J Magn Reson Imaging. 2017 Jan;45(1):132-138
pubmed: 27309545