Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.
accelerated simulation
error characterization
magnetic resonance fingerprinting
pulse sequence optimization
undersampling artifacts
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
09 May 2024
09 May 2024
Historique:
revised:
31
03
2024
received:
14
02
2024
accepted:
24
04
2024
medline:
10
5
2024
pubmed:
10
5
2024
entrez:
10
5
2024
Statut:
aheadofprint
Résumé
For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NINDS NIH HHS
ID : R01 NS109439
Pays : United States
Organisme : NIBIB NIH HHS
ID : R21 EB029658
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA269604
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA282516
Pays : United States
Informations de copyright
© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
Références
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495:187‐192.
Badve C, Yu A, Dastmalchian S, et al. MR fingerprinting of adult brain tumors: initial experience. AJNR Am J Neuroradiol. 2017;38:492‐499. doi:10.3174/ajnr.A5035
Yu AC, Badve C, Ponsky LE, et al. Development of a combined Mr fingerprinting and diffusion examination for prostate cancer. Radiology. 2017;283:729‐738. doi:10.1148/radiol.2017161599
Ma D, Jones SE, Deshmane A, et al. Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging. 2019;49:1333‐1346. doi:10.1002/jmri.26319
Liao C, Wang K, Cao X, et al. Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology. 2018;288:804‐812. doi:10.1148/radiol.2018172131
Ma D, Badve C, Sun JEP, et al. Motion robust MR fingerprinting scan to image neonates with prenatal opioid exposure. J Magn Reson Imaging. 2023;59:1758‐1768. doi:10.1002/jmri.28907
Zhao B, Setsompop K, Adalsteinsson E, et al. Improved magnetic resonance fingerprinting reconstruction with low‐rank and subspace modeling. Magn Reson Med. 2018;79:933‐942. doi:10.1002/mrm.26701
Hamilton JI, Jiang Y, Ma D, et al. Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction. NMR Biomed. 2019;32:e4041. doi:10.1002/NBM.4041
Assländer J, Cloos MA, Knoll F, Sodickson DK, Hennig J, Lattanzi R. Low rank alternating direction method of multipliers reconstruction for MR fingerprinting. Magn Reson Med. 2018;79:83‐96. doi:10.1002/mrm.26639
Lima da Cruz G, Bustin A, Jaubert O, Schneider T, Botnar RM, Prieto C. Sparsity and locally low rank regularization for MR fingerprinting. Magn Reson Med. 2019;81:3530‐3543. doi:10.1002/mrm.27665
Hu Y, Li P, Chen H, Zou L, Wang H. High‐quality MR fingerprinting reconstruction using structured low‐rank matrix completion and subspace projection. IEEE Trans Med Imaging. 2022;41:1150‐1164. doi:10.1109/TMI.2021.3133329
Mazor G, Weizman L, Tal A, Eldar YC. Low‐rank magnetic resonance fingerprinting. Med Phys. 2018;45:4066‐4084. doi:10.1002/mp.13078
Hoppe E, Körzdörfer G, Würfl T, et al. Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. Stud Health Technol Inform. 2017;243:202‐206. doi:10.3233/978‐1‐61499‐808‐2‐202
Chen Y, Fang Z, Hung SC, Chang WT, Shen D, Lin W. High‐resolution 3D MR fingerprinting using parallel imaging and deep learning. Neuroimage. 2020;206:116329. doi:10.1016/j.neuroimage.2019.116329
Cohen O, Zhu B, Rosen MS. MR fingerprinting deep RecOnstruction NEtwork (DRONE). Magn Reson Med. 2018;80:885‐894. doi:10.1002/mrm.27198
Fang Z, Chen Y, Lin W, Shen D. Quantification of relaxation times in MR fingerprinting using deep learning. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib. 2017;25:3307.
Fang Z, Chen Y, Liu M, et al. Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting. IEEE Trans Med Imaging. 2019;38:2364‐2374. doi:10.1109/TMI.2019.2899328
Hamilton JI. A self‐supervised deep learning reconstruction for shortening the Breathhold and acquisition window in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med. 2022;9:928546. doi:10.3389/fcvm.2022.928546
Zhao B, Haldar JP, Liao C, et al. Optimal experiment design for magnetic resonance fingerprinting: Cramér‐rao bound meets spin dynamics. IEEE Trans Med Imaging. 2019;38:844‐861. doi:10.1109/TMI.2018.2873704
Assländer J, Lattanzi R, Sodickson DK, Cloos MA. Optimized quantification of spin relaxation times in the hybrid state. Magn Reson Med. 2019;82:1385‐1397. doi:10.1002/mrm.27819
Maidens J, Packard A, Arcak M. Parallel dynamic programming for optimal experiment design in nonlinear systems. 2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc; 2016:2894‐2899. doi:10.1109/CDC.2016.7798700
Kara D, Fan M, Hamilton J, Griswold M, Seiberlich N, Brown R. Parameter map error due to normal noise and aliasing artifacts in MR fingerprinting. Magn Reson Med. 2019;81:3108‐3123. doi:10.1002/mrm.27638
Cohen O, Rosen MS. Algorithm comparison for schedule optimization in MR fingerprinting. Magn Reson Imaging. 2017;41:15‐21. doi:10.1016/j.mri.2017.02.010
Sommer K, Amthor T, Doneva M, Koken P, Meineke J, Börnert P. Towards predicting the encoding capability of MR fingerprinting sequences. Magn Reson Imaging. 2017;41:7‐14. doi:10.1016/j.mri.2017.06.015
Stolk CC, Sbrizzi A. Understanding the combined effect of k ‐space Undersampling and transient states excitation in MR fingerprinting reconstructions. IEEE Trans Med Imaging. 2019;38:2445‐2455. doi:10.1109/TMI.2019.2900585
Heesterbeek DGJ, Koolstra K, van Osch MJP, van Gijzen MB, Vos FM, Nagtegaal MA. Mitigating undersampling errors in MR fingerprinting by sequence optimization. Magn Reson Med. 2023;89:2076‐2087. doi:10.1002/mrm.29554
Jordan SP, Hu S, Rozada I, et al. Automated design of pulse sequences for magnetic resonance fingerprinting using physics‐inspired optimization. Proc Natl Acad Sci U S A. 2021;118:e2020516118. doi:10.1073/PNAS.2020516118/‐/DCSUPPLEMENTAL
Hu S, Jordan S, Boyacioglu R, et al. A fast MR fingerprinting simulator for direct error estimation and sequence optimization. Magn Reson Imaging. 2023;98:105‐114. doi:10.1016/j.mri.2023.01.011
Jiang Y, Hamilton J, Lo WC, et al. Simultaneous T1, T2 and diffusion quantification using multiple contrast prepared magnetic resonance fingerprinting. Paper presented at: The 25th Annual Meeting, International Society of Magnetic Resonance in Medicine; 2017: pp. 2–4.
Afzali M, Mueller L, Sakaie K, et al. MR fingerprinting with b‐tensor encoding for simultaneous quantification of relaxation and diffusion in a single scan. Magn Reson Med. 2022;88:2043‐2057. doi:10.1002/mrm.29352
Wang CY, Coppo S, Mehta BB, Seiberlich N, Yu X, Griswold MA. Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2* simultaneously with δf, T1, and T2. Magn Reson Med. 2019;81:1849‐1862. doi:10.1002/mrm.27543
Boyacioglu R, Wang C, Ma D, McGivney DF, Yu X, Griswold MA. 3D magnetic resonance fingerprinting with quadratic RF phase. Magn Reson Med. 2021;85:2084‐2094. doi:10.1002/mrm.28581
Su P, Mao D, Liu P, et al. Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL. Magn Reson Med. 2017;78:1812‐1823. doi:10.1002/mrm.26587
Wright KL, Jiang Y, Ma D, et al. Estimation of perfusion properties with MR fingerprinting arterial spin labeling. Magn Reson Imaging. 2018;50:68‐77. doi:10.1016/j.mri.2018.03.011
Hilbert T, Xia D, Block KT, et al. Magnetization transfer in magnetic resonance fingerprinting. Magn Reson Med. 2020;84:128‐141. doi:10.1002/mrm.28096
Liu J, Liu H, Liu Q, et al. Encoding capability prediction of acquisition schedules in CEST MR fingerprinting for pH quantification. Magn Reson Med. 2022;87:2044‐2052. doi:10.1002/mrm.29074
Perlman O, Herz K, Zaiss M, Cohen O, Rosen MS, Farrar CT. CEST MR‐fingerprinting: practical considerations and insights for acquisition schedule design and improved reconstruction. Magn Reson Med. 2020;83:462‐478. doi:10.1002/mrm.27937
Fessler JA, Sutton BP. Nonuniform fast fourier transforms using min‐max interpolation. IEEE Trans. Signal Process. 2003;51:560‐574.
Qiu Z, Hu S, Zhao W, et al. Self‐calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification. Magn Reson Med. 2024;91:1978‐1993. doi:10.1002/mrm.29969
Cohen O, Yu VY, Tringale KR, et al. CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med. 2023;89:233‐249. doi:10.1002/mrm.29448
Chen Y, Panda A, Pahwa S, et al. Three‐dimensional MR fingerprinting for quantitative breast imaging. Radiology. 2018;290:33‐40. doi:10.1148/radiol.2018180836
Rumac S, Pavon AG, Hamilton JI, et al. Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers. Sci Rep. 2022;12:18705. doi:10.1038/s41598‐022‐23573‐3
Benjamin AJV, Gómez PA, Golbabaee M, et al. Multi‐shot Echo planar imaging for accelerated cartesian MR fingerprinting: an alternative to conventional spiral MR fingerprinting. Magn Reson Imaging. 2019;61:20‐32. doi:10.1016/j.mri.2019.04.014
Koolstra K, Beenakker JWM, Koken P, Webb A, Börnert P. Cartesian MR fingerprinting in the eye at 7T using compressed sensing and matrix completion‐based reconstructions. Magn Reson Med. 2019;81:2551‐2565. doi:10.1002/mrm.27594
Mickevicius NJ, Glide‐Hurst CK. Low‐rank inversion reconstruction for through‐plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T. Magn Reson Med. 2022;88:840‐848. doi:10.1002/mrm.29244
Yu VY, Otazo R, Wu C, et al. Quantitative longitudinal mapping of radiation‐treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction. Magn Reson Imaging. 2023;101:25‐34. doi:10.1016/j.mri.2023.03.019