Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Rumac, Simone; Pavon, Anna Giulia; Hamilton, Jesse; Rodrigues, David; Seiberlich, Nicole; Schwitter, Juerg; Heeswijk, Ruud B. van

doi:10.1038/s41598-022-23573-3

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Another prevalent approach in MRF sequence design involves incorporating magnetization preparation modules dedicated to enhancing tissue property encoding in the signal rather than for image acquisition, such as the diffusion preparation in mdMRF scans. This strategy proves especially valuable in scenarios like breast MRF 41 and cardiac MRF, 8,42 where signal sensitivity to tissue properties is low because of short acquisition windows. The three example MRF applications presented here illustrate a variety of MRF sequence architectures, each accompanied by a prototype optimizer.…”

Section: Discussionmentioning

confidence: 99%

Efficient pulse sequence design framework for high‐dimensional MR fingerprinting scans using systematic error index

Hu,

Qiu,

Adams

et al. 2024

Magnetic Resonance in Med

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PurposeFor 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.MethodsBy 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.ResultsThe 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.ConclusionWe 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.

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Section: Discussionmentioning

confidence: 99%

Efficient pulse sequence design framework for high‐dimensional MR fingerprinting scans using systematic error index

Hu,

Qiu,

Adams

et al. 2024

Magnetic Resonance in Med

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“…Table 2 summarizes several studies that have validated various cardiac MRF techniques against conventional cardiac mapping techniques 147,148,153–159,162,163 . Generally, although absolute T1 and T2 values differ between MRF-derived mapping and conventional mapping, there is good correlation between MRF and conventional mapping sequences in all studies.…”

Section: Current Clinical Applicationsmentioning

confidence: 99%

Magnetic Resonance Fingerprinting

et al. 2023

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Magnetic Resonance (MR) is an exceptionally powerful and versatile measurement technique. The basic structure of an MR experiment has remained nearly constant for almost 50 years. Here we introduce a novel paradigm, Magnetic Resonance Fingerprinting (MRF) that permits the noninvasive quantification of multiple important properties of a material or tissue simultaneously through a new approach to data acquisition, post-processing and visualization. MRF provides a new mechanism to quantitatively detect and analyze complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to specifically identify the presence of a target material or tissue, which will increase the sensitivity, specificity, and speed of an MR study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern recognition algorithm, MRF inherently suppresses measurement errors and thus can improve accuracy compared to previous approaches.

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“…The long acquisition window in cMRF makes it particularly vulnerable to artifacts in the case of a subject with a high heart rate. In [105], the authors demonstrated that cMRF The long acquisition window in cMRF makes it particularly vulnerable to artifacts in the case of a subject with a high heart rate. In [105], the authors demonstrated that cMRF is less precise than conventional quantitative MRI acquisitions especially for subjects with high heart rates.…”

Section: Cardiac Imagingmentioning

confidence: 99%

“…In [105], the authors demonstrated that cMRF The long acquisition window in cMRF makes it particularly vulnerable to artifacts in the case of a subject with a high heart rate. In [105], the authors demonstrated that cMRF is less precise than conventional quantitative MRI acquisitions especially for subjects with high heart rates. The advantage of cMRF is that it allows for higher-resolution acquisition.…”

Section: Cardiac Imagingmentioning

confidence: 99%

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Monga,

Singh,

de Moura

et al. 2024

Bioengineering

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Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

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Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Cited by 7 publications

References 35 publications

Efficient pulse sequence design framework for high‐dimensional MR fingerprinting scans using systematic error index

Efficient pulse sequence design framework for high‐dimensional MR fingerprinting scans using systematic error index

Magnetic Resonance Fingerprinting

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

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