Self‐Calibrating Wave‐Encoded Variable‐Density Single‐Shot Fast Spin Echo Imaging

Chen, Feiyu; Taviani, Valentina; Tamir, Jonathan I.; Cheng, Joseph Y.; Zhang, Tao; Song, Qiong; Hargreaves, Brian A.; Pauly, John M.; Vasanawala, Shreyas

doi:10.1002/jmri.25853

Cited by 16 publications

(29 citation statements)

References 32 publications

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“…A variable-density sampling scheme (3,4) (Fig 1, A) was developed for single-shot fast SE sequences to enable full-Fourier acquisitions with variable-refocusing flip angles (6). Imaging parameters are shown in Table 1.…”

Section: Variable-density Single-shot Fast Se Sequences and Pics Recomentioning

confidence: 99%

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Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

Chen

Taviani²,

Malkiel³

et al. 2018

Radiology

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103

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Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

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Section: Variable-density Single-shot Fast Se Sequences and Pics Recomentioning

confidence: 99%

“…An empirically tuned regularization factor of 0.001 was used for all iterations. These parameters were optimized based on the results of previous studies (3,4) and the radiologists' feedback on image quality.…”

Section: Image Reconstruction With a Vnmentioning

confidence: 99%

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

Chen

Taviani²,

Malkiel³

et al. 2018

Radiology

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103

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“…This approach reduces the scan time of T 2 ‐weighted imaging to less than a second per slice. However, in abdominal applications conventional SSFSE (or HASTE) still requires more than 30 seconds to achieve full‐abdomen coverage . This duration is usually too long for single breath‐holds, and may result in degraded image quality due to respiratory and cardiac motion or inconsistency between two separated breath‐holds.…”

mentioning

confidence: 99%

“…On the hardware side, multichannel coils with an increased number of channels up to 128 have been developed to improve parallel imaging performance and enable higher acceleration factors of up to 8‐fold . On the sequence development side, variable refocusing flip angles, variable‐density sampling, and wave encoding have been developed to enable compressed sensing reconstruction and improve image sharpness. Among these techniques, wave‐encoded SSFSE has been previously demonstrated to achieve improved sharpness and reduced scan time in comparison with standard SSFSE …”

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confidence: 99%

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Data‐driven self‐calibration and reconstruction for non‐cartesian wave‐encoded single‐shot fast spin echo using deep learning

Chen

Cheng

Taviani³

et al. 2019

Magnetic Resonance Imaging

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Background Current self‐calibration and reconstruction methods for wave‐encoded single‐shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed. Purpose To develop and investigate the clinical feasibility of data‐driven self‐calibration and reconstruction of wave‐encoded SSFSE imaging for computation time reduction and quality improvement. Study Type Prospective controlled clinical trial. Subjects With Institutional Review Board approval, the proposed method was assessed on 29 consecutive adult patients (18 males, 11 females, range, 24–77 years). Field Strength/Sequence A wave‐encoded variable‐density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable 3.5× acceleration with full‐Fourier acquisitions. Data‐driven calibration of wave‐encoding point‐spread function (PSF) was developed using a trained deep neural network. Data‐driven reconstruction was developed with another set of neural networks based on the calibrated wave‐encoding PSF. Training of the calibration and reconstruction networks was performed on 15,783 2D wave‐encoded SSFSE abdominal images. Assessment Image quality of the proposed data‐driven approach was compared independently and blindly with a conventional approach using iterative self‐calibration and reconstruction with parallel imaging and compressed sensing by three radiologists on a scale from –2 to 2 for noise, contrast, sharpness, artifacts, and confidence. Computation time of these two approaches was also compared. Statistical Tests Wilcoxon signed‐rank tests were used to compare image quality and two‐tailed t‐tests were used to compare computation time with P values of under 0.05 considered statistically significant. Results An average 2.1‐fold speedup in computation was achieved using the proposed method. The proposed data‐driven self‐calibration and reconstruction approach significantly reduced the perceived noise level (mean scores 0.82, P < 0.0001). Data Conclusion The proposed data‐driven calibration and reconstruction achieved twice faster computation with reduced perceived noise, providing a fast and robust self‐calibration and reconstruction for clinical abdominal SSFSE imaging. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:841–853.

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Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

et al. 2023

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BackgroundWave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto‐calibration signals (ACS) signal acquisition and is time‐consuming, while the latter requires a large amount of training data.PurposeTo tackle the above issues, an untrained neural network (UNN) model incorporating wave‐encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS‐ and training data‐free.MethodsGenerally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k‐space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k‐space linear predictability. And then, the iterative paradigm to recover the full k‐space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six‐fold acceleration, respectively.ResultsIn vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.ConclusionsThe model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time‐consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

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Self‐Calibrating Wave‐Encoded Variable‐Density Single‐Shot Fast Spin Echo Imaging

Abstract: 1 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:954-966.

Cited by 16 publications

References 32 publications

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

Data‐driven self‐calibration and reconstruction for non‐cartesian wave‐encoded single‐shot fast spin echo using deep learning

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

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