Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization

Jordan, Stephen P.; Hu, Siyuan; Rozada, Ignacio; McGivney, Debra; Boyacıoğlu, Rasim; Jacob, Darryl C.; Huang, Sherry; Beverland, Michael E.; Katzgraber, Helmut G.; Troyer, Matthias; Griswold, Mark A.; Ma, Dan

doi:10.1073/pnas.2020516118

Cited by 27 publications

(43 citation statements)

References 39 publications

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“…The mdMRF pulse sequence can also be further optimized because the current implementations, including excitations, timing, location, and values of the preparation modules, were heuristically designed. Our group proposed a physics‐inspired optimization framework for MRF recently that uses a cost‐function based on explicit first‐principle simulation of systematic errors arising from undersampling and phase errors 78 . This framework will be applied to optimize mdMRF for better quantification of relaxation and diffusion simultaneously.…”

Section: Discussionmentioning

confidence: 99%

“…Our group proposed a physics-inspired optimization framework for MRF recently that uses a cost-function based on explicit first-principle simulation of systematic errors arising from undersampling and phase errors. 78 This framework will be applied to optimize mdMRF for better quantification of relaxation and diffusion simultaneously. In terms of extending the current implementation to volumetric acquisitions, fast imaging techniques, such as simultaneous multi-slice acquisition, [79][80][81] 3D sampling strategies, 39,40 and optimized interleaved scans 36,69 will be investigated.…”

Section: Discussionmentioning

confidence: 99%

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MR Fingerprinting with b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

Afzali

Mueller

Sakaie

et al. 2022

Magnetic Resonance in Med

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Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. Methods:We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.Results: T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. Conclusion:We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice.The map quality and quantitative values are consistent with the reference scans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

MR Fingerprinting with b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

Afzali

Mueller

Sakaie

et al. 2022

Magnetic Resonance in Med

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“…This effect may limit the applicability of MRF—in its present implementation at 0.35 T—for the characterization of long‐T 2 tissues such as CSF and edema 10,30 . Optimizing the flip‐angle pattern and timing of the MRF acquisition to minimize the variance in the estimation of T 1 and T 2 for a range of expected relaxation times may reduce the uncertainty and improve the reliability of MRF 17,31,32 . Although this effect is present in the both the accelerated and unaccelerated acquisitions, improving the optimality of the MRF experiment at 0.35 T remains an active area of investigation to reduce uncertainty in the characterization of long‐T 2 tissues.…”

Section: Discussionmentioning

confidence: 99%

“…10,30 Optimizing the flip-angle pattern and timing of the MRF acquisition to minimize the variance in the estimation of T 1 and T 2 for a range of expected relaxation times may reduce the uncertainty and improve the reliability of MRF. 17,31,32 Although this effect is present in the both the accelerated and unaccelerated acquisitions, improving the optimality of the MRF experiment at 0.35 T remains an active area of investigation to reduce uncertainty in the characterization of long-T 2 tissues. Every effort to minimize uncertainty in quantitative parameter mapping with MRF will be made to ensure its reliability when using qMRI to characterize, predict, or assess a tumor's response to treatment on low-field MR-guided radiation therapy systems.…”

Section: F I G U R Ementioning

confidence: 99%

Low‐rank inversion reconstruction for through‐plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T

Mickevicius

Glide-Hurst

2022

Magnetic Resonance in Med

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To reduce scan time, methods to accelerate phase-encoded/non-Cartesian MR fingerprinting (MRF) acquisitions for variable density spiral acquisitions have recently been developed. These methods are not applicable to MRF acquisitions, wherein a single k-space spoke is acquired per frame. Therefore, we propose a low-rank inversion method to resolve MRF contrast dynamics from through-plane accelerated Cartesian/radial measurements applied to quantitative relaxation-time mapping on a 0.35T system.Methods: An algorithm was implemented to reconstruct through-plane aliased low-rank images describing the contrast dynamics occurring because of the transient-state MRF acquisition. T 1 and T 2 times from accelerated acquisitions were compared with those from unaccelerated linear reconstructions in a standardized system phantom and within in vivo brain and prostate experiments on a hybrid 0.35T MRI/linear accelerator.Results: No significant differences between T 1 and T 2 times for the accelerated reconstructions were observed compared to fully sampled acquisitions (p = 0.41 and p = 0.36, respectively). The mean absolute errors in T 1 and T 2 were 5.6% and 2.9%, respectively, between the full and accelerated acquisitions. The SDs in T 1 and T 2 decreased with the advanced accelerated reconstruction compared with the unaccelerated reconstruction (p = 0.02 and p = 0.03, respectively). The quality of the T 1 and T 2 maps generated with the proposed approach are comparable to those obtained using the unaccelerated data sets.Conclusions: Through-plane accelerated MRF with radial k-space coverage was demonstrated at a low field strength of 0.35 T. This method enabled 3D T 1 and T 2 mapping at 0.35 T with a 3-min scan.

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“…This statistical tool looks for the lower bound of the variance of unbiased estimators and has been already utilized by MRF community to optimize FA and TR patterns for optimal sequence design ( 108 – 111 ). Apart from statistical-based optimizers, physics knowledge could be also included in the model, as in Jordan et al ( 112 ).…”

Section: Artificial Intelligence In Cardiac Mrfmentioning

confidence: 99%

Artificial intelligence in cardiac magnetic resonance fingerprinting

Velasco

Fletcher

Botnar

et al. 2022

Front. Cardiovasc. Med.

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Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

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Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization

Cited by 27 publications

References 39 publications

MR Fingerprinting with b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

MR Fingerprinting with b‐Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

Low‐rank inversion reconstruction for through‐plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T

Artificial intelligence in cardiac magnetic resonance fingerprinting

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