Lateralization of temporal lobe epileptic foci with automated chemical exchange saturation transfer measurements at 3 Tesla

Wang, Kang; Wen, Qingqing; Wu, Dang; Hsu, Yi‐Cheng; Heo, Hye Young; Wang, Wenqi; Sun, Yi; Ma, Yue; Wu, Dan; Zhang, Yi

doi:10.1016/j.ebiom.2023.104460

Cited by 2 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CEST MRI has proven to be a powerful and promising technique that can sensitively detect a wide range of biomolecules [1][2][3][4] in various diseases such as cancers [5][6][7] and epilepsies. [8][9][10] However, the widespread clinical adoption of this technique has been hampered by its prolonged scan time due to multiple image frames acquired over a range of saturation frequency offsets. 11 Several image reconstruction strategies have been developed to enable rapid CEST data acquisition, which recovers images from undersampled k-space measurements by exploiting redundancies in the data.…”

Section: Introductionmentioning

confidence: 99%

“…CEST MRI has proven to be a powerful and promising technique that can sensitively detect a wide range of biomolecules 1–4 in various diseases such as cancers 5–7 and epilepsies 8–10 . However, the widespread clinical adoption of this technique has been hampered by its prolonged scan time due to multiple image frames acquired over a range of saturation frequency offsets 11 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Xu,

Zu,

Hsu

et al. 2023

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

PurposeTo develop a model‐based deep neural network for high‐quality image reconstruction of undersampled multi‐coil CEST data.Theory and MethodsInspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST‐VN) with a k‐space data‐sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial–frequential convolution kernels that exploit correlations in the x‐ω domain. Additionally, a new pipeline based on multiple‐pool Bloch–McConnell simulations is devised to synthesize multi‐coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST‐specific loss function that jointly measures the structural and CEST contrast. The performance of CEST‐VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state‐of‐the‐art reconstruction methods.ResultsThe proposed CEST‐VN method generated high‐quality CEST source images and amide proton transfer‐weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST‐VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST‐specific loss function and data‐sharing block used.ConclusionsThe proposed CEST‐VN method can offer high‐quality CEST source images and amide proton transfer‐weighted maps from highly undersampled multi‐coil data by integrating the deep learning prior and multi‐coil sensitivity encoding model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Xu,

Zu,

Hsu

et al. 2023

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…Amide proton transfer (APT) is one type of CEST contrast for a large group of amide protons (‐NH) with an average chemical shift of about 3.5 ppm downfield from water. APT‐weighted MRI has shown great potential in the diagnosis of many pathologies, such as cancer, stroke, and neurodegenerative disease 14–29 . When applying RF saturation at 3.5 ppm for APT imaging, however, free bulk water and semisolid macromolecular protons may also be saturated due to overlap in the chemical shift, leading to an inevitable interference with the desirable APT contrast.…”

Section: Introductionmentioning

confidence: 99%

“…APT-weighted MRI has shown great potential in the diagnosis of many pathologies, such as cancer, stroke, and neurodegenerative disease. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] When applying RF saturation at 3.5 ppm for APT imaging, however, free bulk water and semisolid macromolecular protons may also be saturated due to overlap in the chemical shift, leading to an inevitable interference with the desirable APT contrast. Although a conventional MTR asymmetry (MTR asym ) analysis can largely remove symmetric, direct water saturation and MTC effects, the APT-weighted (APTw) signal, measured by the MTR asymmetry analysis, is still confounded by inherent MT asymmetry, both from the relayed nuclear Overhauser effect (rNOE) in mobile macromolecules upfield from water and asymmetry in the semisolid MTC.…”

Section: Introductionmentioning

confidence: 99%

Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging

Singh

Jiang

et al. 2023

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose To develop a unified deep‐learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. Methods The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross‐linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization‐transfer ratio asymmetry effect was evaluated in MTC‐MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test–retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep‐learning framework. Results Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC‐MRF dictionary or a training data set reduced the computation time by 181‐fold, without compromising MRF profile accuracy. The recurrent neural network–based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC‐MRF framework for tissue‐parameter quantification, the test–retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. Conclusion Bloch simulator–driven, deep‐learning MTC‐MRF can provide robust and repeatable multiple‐tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

show abstract

Lateralization of temporal lobe epileptic foci with automated chemical exchange saturation transfer measurements at 3 Tesla

Cited by 2 publications

References 39 publications

Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging

Contact Info

Product

Resources

About