Fast 3D brain MR fingerprinting based on multi‐axis spiral projection trajectory

Cao, Xiaozhi; Ye, Huihui; Liao, Congyu; Li, Qing; He, Hongjian; Zhong, Jianhui

doi:10.1002/mrm.27726

Cited by 53 publications

(86 citation statements)

References 51 publications

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“…Compared with the 2D MRF approach developed in the current study, high‐resolution MRF has also been explored with 3D acquisitions in the literature . Although MRF with volumetric imaging can provide better spatial coverage and higher SNR to improve spatial resolution, it typically requires longer scan times and is more sensitive to subject motions, especially with pediatric populations.…”

Section: Discussionmentioning

confidence: 99%

Submillimeter MR fingerprinting using deep learning–based tissue quantification

Fang

Chen

Hung

et al. 2019

Magnetic Resonance in Med

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Purpose:To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach. Methods: A rapid and high-resolution MR fingerprinting technique was developed for brain T 1 and T 2 quantification. The 2D acquisition was performed using a FISPbased MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning-based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects, and the performance of the proposed approach was compared with several existing methods in the literature. Results: In vivo measurements with both adult and pediatric subjects show that high-quality T 1 and T 2 mapping with 0.8-mm in-plane resolution can be achieved in 7.5 seconds per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared with the standard U-Net, high-resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. Experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside reduced data acquisition time, a 5-fold acceleration in tissue property mapping was also achieved with the proposed method. Conclusion: A rapid and high-resolution MR fingerprinting technique was developed, which enables high-quality T 1 and T 2 quantification with 0.8-mm in-plane resolution in 7.5 seconds per slice. K E Y W O R D Sdeep learning, MR fingerprinting, pediatric imaging, quantitative imaging 580 | FANG et Al.

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Section: Discussionmentioning

confidence: 99%

Submillimeter MR fingerprinting using deep learning–based tissue quantification

Fang

Chen

Hung

et al. 2019

Magnetic Resonance in Med

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“…We only covered temporal lobes in this study, but MTLE is well‐recognized as a network disease extending beyond temporal lobe. The newly developed 3D‐MRF technique with higher resolution covering the whole brain may overcome these shortcomings, providing extra information with more accurate T1, T2, and PD estimation . Secondly, although we prospectively collected all the data, our study design was cross‐sectional.…”

Section: Discussionmentioning

confidence: 99%

Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy

Wang

Cao

et al. 2019

Ann Clin Transl Neurol

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Objective Mesial temporal lobe epilepsy (MTLE) is a network disorder. We aimed to quantify the white matter alterations in the temporal lobe of MTLE patients with hippocampal sclerosis (MTLE‐HS) by using magnetic resonance fingerprinting (MRF), a novel imaging technique, which allows simultaneous measurements of multiple parameters with a single acquisition. Methods We consecutively recruited 27 unilateral MTLE‐HS patients and 22 healthy controls. Measurements including T1, T2, and PD values in the temporopolar white matter and temporal stem were recorded and analyzed. Results We found increased T2 value in both sides, and increased T1 value in the ipsilateral temporopolar white matter of MTLE‐HS patients, as compared with healthy controls. The T1 and T2 values were higher in the ipsilateral than the contralateral side. In the temporal stem, increased T1 and T2 values in the ipsilateral side of the MTLE‐HS patients were also observed. Only increased T2 values were observed in the contralateral temporal stem. No significant differences in PD values were observed in either the temporopolar white matter or temporal stem of the MTLE‐HS patients. Correlation analysis revealed that T1 and T2 values in the ipsilateral temporopolar white matter were negatively correlated with the age at epilepsy onset. Interpretation By using MRF, we were able to assess the alterations of T1 and T2 in the temporal lobe white matter of MTLE‐HS patients. MRF could be a promising imaging technique in identifying mild changes in MTLE patients, which might optimize the pre‐surgical evaluation and therapeutic interventions in these patients.

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“…However, these modifications resulted in a reconstruction time of 20 hours with multiple computing cores running in parallel . Cao et al enabled further acceleration by undersampling in all dimensions using multiaxis spiral projection imaging, with an acquisition time of 5 minutes.…”

Section: Current Sequence Challengesmentioning

confidence: 99%

Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations

Poorman

Martin

et al. 2019

Magnetic Resonance Imaging

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Magnetic resonance fingerprinting (MRF) is a powerful quantitative MRI technique capable of acquiring multiple property maps simultaneously in a short timeframe. The MRF framework has been adapted to a wide variety of clinical applications, but faces challenges in technical development, and to date has only demonstrated repeatability and reproducibility in small studies. In this review, we discuss the current implementations of MRF and their use in a clinical setting. Based on this analysis, we highlight areas of need that must be addressed before MRF can be fully adopted into the clinic and make recommendations to the MRF community on standardization and validation strategies of MRF techniques. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:675–692.

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Fast 3D brain MR fingerprinting based on multi‐axis spiral projection trajectory

Cited by 53 publications

References 51 publications

Submillimeter MR fingerprinting using deep learning–based tissue quantification

Submillimeter MR fingerprinting using deep learning–based tissue quantification

Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy

Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations

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