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

• Published in: Journal of magnetic resonance imaging Vol. 51; no. 3; pp. 841 - 853
• Main Authors: Chen, Feiyu, Cheng, Joseph Y., Taviani, Valentina, Sheth, Vipul R., Brunsing, Ryan L., Pauly, John M., Vasanawala, Shreyas S.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2020; Wiley Subscription Services, Inc
• Subjects: Abdomen; Acceleration; Adult; Aged; Artificial neural networks; Calibration; Cartesian coordinates; Coding; Computer applications; Computing time; Confidence; Data acquisition; data‐driven; Deep Learning; Female; Females; Field strength; Humans; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Iterative methods; Magnetic Resonance Imaging; Male; Males; Middle Aged; Neural networks; Noise; Noise levels; parallel imaging and compressed sensing; Prospective Studies; Quality assessment; Quality control; Rank tests; Sharpness; Shot; single‐shot fast spin echo; Statistical analysis; Statistical tests; wave encoding; Young Adult; single-shot fast spin echo; deep learning; wave encoding; data-driven; parallel imaging and compressed sensing
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26871

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.