Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing

Fujita, Shohei; Hagiwara, Akifumi; Takei, Naoyuki; Hwang, Ken Pin; Fukunaga, Issei; Kato, Shimpei; Andica, Christina; Kamagata, Koji; Yokoyama, Kazumasa; Abe, Osamu; Aoki, Shigeki

doi:10.1097/rli.0000000000000744

Cited by 23 publications

(33 citation statements)

References 46 publications

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“…CS has already been applied to MRF and QALAS, and when applied to QALAS, the imaging time is reduced from 11.2 minutes to 5.9 minutes. 18,19 The same effect can be expected for QPM.…”

Section: Discussionsupporting

confidence: 63%

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo

et al. 2023

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Purpose: MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T 1 , T 2 *, PD, and susceptibility) and B 1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments. Methods: Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching. Results: A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods. Conclusion:The proposed QPM method can map T 1 , T 2 *, PD, susceptibility, and B 1 simultaneously within a scan time that can be applied to human subjects.

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“…CS has already been applied to MRF and QALAS, and when applied to QALAS, the imaging time is reduced from 11.2 minutes to 5.9 minutes. 18,19 The same effect can be expected for QPM.…”

Section: Discussionsupporting

confidence: 63%

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo

et al. 2023

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“…Established methods for reducing the scan time include parallel imaging, compressed sensing, and adjusting MR imaging acquisition parameters such as the receiver bandwidth, number of excitations, and in-plane/through-plane resolution. [9][10][11] However, accelerated techniques generally reduce the SNR and/or spatial resolution, resulting in degradation of image quality. Recently, deep learning-based reconstruction (DLR) techniques have been proposed to address the trade-off between the image quality and scan time.…”

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confidence: 99%

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Kim

Cho

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learningbased reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND METHODS:This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets. RESULTS:The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P , .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P . .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 . 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, ,0.5%; volumetry, À1.46%-0.83%). CONCLUSIONS:The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.

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“…Recent years have also seen the development of a 3D technique in the brain, 76,77 based on a sequence originally developed for cardiac imaging 78 . This is a segmented spoiled gradient echo sequence that executes five gradient echo trains per cycle to acquire five separate contrasts: one train preceded by a set of T 2 ‐preparation pulses, followed by an inversion pulse with four equally spaced trains similar to that used in the Look‐Locker T 1 ‐mapping technique (Figure 5).…”

Section: Future Directionsmentioning

confidence: 99%

“…By applying a deep learningbased reconstruction to the base images before parameter fitting, the resulting parameter maps and synthetic images also exhibit less noise without any additional bias. 74 Networks have also been trained to jointly reconstruct the multiple contrast base images Recent years have also seen the development of a 3D technique in the brain, 76,77 based on a sequence originally developed for cardiac imaging. 78 This is a segmented spoiled gradient echo sequence that executes five gradient echo trains per cycle to acquire five separate contrasts: one train preceded by a set of T 2preparation pulses, followed by an inversion pulse with four equally spaced trains similar to that used in the Look-Locker T 1 -mapping technique (Figure 5).…”

Section: Future Directionsmentioning

confidence: 99%

Synthetic MR: Physical principles, clinical implementation, and new developments

Hwang

Fujita

2022

Medical Physics

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Current clinical MR imaging practices rely on the qualitative assessment of images for diagnosis and treatment planning. While contrast in MR images is dependent on the spin parameters of the imaged tissue, pixel values on MR images are relative and are not scaled to represent any tissue properties. Synthetic MR is a fully featured imaging workflow consisting of efficient multiparameter mapping acquisition, synthetic image generation, and volume quantitation of brain tissues. As the application becomes more widely available on multiple vendors and scanner platforms, it has also gained widespread adoption as clinicians begin to recognize the benefits of rapid quantitation. This review will provide details about the sequence with a focus on the physical principles behind its relaxometry mechanisms. It will present an overview of the products in their current form and some potential issues when implementing it in the clinic. It will conclude by highlighting some recent advances of the technique, including a 3D mapping method and its associated applications.

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Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing

Abstract: Supplemental digital content is available in the text.

Cited by 23 publications

References 46 publications

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Synthetic MR: Physical principles, clinical implementation, and new developments

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