Literature DB >> 32720408

Myocardial T1 and T2 quantification and water-fat separation using cardiac MR fingerprinting with rosette trajectories at 3T and 1.5T.

Yuchi Liu1,2, Jesse Hamilton1,2, Brendan Eck1,3, Mark Griswold1,4, Nicole Seiberlich1,2,4.   

Abstract

PURPOSE: This work aims to develop an approach for simultaneous water-fat separation and myocardial T1 and T2 quantification based on the cardiac MR fingerprinting (cMRF) framework with rosette trajectories at 3T and 1.5T.
METHODS: Two 15-heartbeat cMRF sequences with different rosette trajectories designed for water-fat separation at 3T and 1.5T were implemented. Water T1 and T2 maps, water image, and fat image were generated with B0 inhomogeneity correction using a B0 map derived from the cMRF data themselves. The proposed water-fat separation rosette cMRF approach was validated in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and water/oil phantoms. It was also applied for myocardial tissue mapping of healthy subjects at both 3T and 1.5T.
RESULTS: Water T1 and T2 values measured using rosette cMRF in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom agreed well with the reference values. In the water/oil phantom, oil was well suppressed in the water images and vice versa. Rosette cMRF yielded comparable T1 but 2~3 ms higher T2 values in the myocardium of healthy subjects than the original spiral cMRF method. Epicardial fat deposition was also clearly shown in the fat images.
CONCLUSION: Rosette cMRF provides fat images along with myocardial T1 and T2 maps with significant fat suppression. This technique may improve visualization of the anatomical structure of the heart by separating water and fat and could provide value in diagnosing cardiac diseases associated with fibrofatty infiltration or epicardial fat accumulation. It also paves the way toward comprehensive myocardial tissue characterization in a single scan.
© 2020 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  T1 mapping; T2 mapping; cardiac MR fingerprinting; fat imaging; rosette trajectory; water-fat separation

Mesh:

Substances:

Year:  2020        PMID: 32720408     DOI: 10.1002/mrm.28404

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  7 in total

Review 1.  Magnetic resonance fingerprinting: an overview.

Authors:  Charit Tippareddy; Walter Zhao; Jeffrey L Sunshine; Mark Griswold; Dan Ma; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-26       Impact factor: 9.236

Review 2.  Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world.

Authors:  Vineeta Ojha; Omar K Khalique; Rishabh Khurana; Daniel Lorenzatti; Steve W Leung; Benny Lawton; Timothy C Slesnick; Joao C Cavalcante; Chiara-Bucciarelli Ducci; Amit R Patel; Claudia C Prieto; Sven Plein; Subha V Raman; Michael Salerno; Purvi Parwani
Journal:  J Cardiovasc Magn Reson       Date:  2022-06-20       Impact factor: 6.903

Review 3.  Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications.

Authors:  Brendan L Eck; Scott D Flamm; Deborah H Kwon; W H Wilson Tang; Claudia Prieto Vasquez; Nicole Seiberlich
Journal:  Prog Nucl Magn Reson Spectrosc       Date:  2020-11-06       Impact factor: 9.795

4.  Feasibility of Magnetic Resonance Fingerprinting on Aging MRI Hardware.

Authors:  Brendan Lee Eck; Kecheng Liu; Wei-Ching Lo; Yun Jiang; Vikas Gulani; Nicole Seiberlich
Journal:  Tomography       Date:  2021-12-23

5.  Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting.

Authors:  Gastao José Lima da Cruz; Carlos Velasco; Begoña Lavin; Olivier Jaubert; Rene Michael Botnar; Claudia Prieto
Journal:  Magn Reson Med       Date:  2022-01-26       Impact factor: 3.737

6.  A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting.

Authors:  Jesse I Hamilton
Journal:  Front Cardiovasc Med       Date:  2022-06-23

7.  Cardiac MRF using rosette trajectories for simultaneous myocardial T1, T2, and proton density fat fraction mapping.

Authors:  Yuchi Liu; Jesse Hamilton; Yun Jiang; Nicole Seiberlich
Journal:  Front Cardiovasc Med       Date:  2022-09-20
  7 in total

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