基于深度学习的数据驱动自校准与重建，用于非笛卡尔波编码单次快速自旋回波成像
Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.
作者信息
Chen Feiyu, Cheng Joseph Y, Taviani Valentina, Sheth Vipul R, Brunsing Ryan L, Pauly John M, Vasanawala Shreyas S
机构信息
Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Department of Radiology, Stanford University, Stanford, California, USA.
出版信息
J Magn Reson Imaging. 2020 Mar;51(3):841-853. doi: 10.1002/jmri.26871. Epub 2019 Jul 19.
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.
背景
当前用于波编码单次激发快速自旋回波成像（SSFSE）的自校准和重建方法需要较长的计算时间，尤其是在需要高精度时。
目的
开发并研究数据驱动的波编码SSFSE成像自校准和重建方法在减少计算时间和提高图像质量方面的临床可行性。
研究类型
前瞻性对照临床试验。
研究对象
经机构审查委员会批准，对29例连续成年患者（18例男性，11例女性，年龄范围24 - 77岁）进行了该方法的评估。
场强/序列：开发了一种用于临床3.0T腹部扫描的波编码可变密度SSFSE序列，以实现全傅里叶采集下3.5倍的加速。使用经过训练的深度神经网络开发了波编码点扩散函数（PSF）的数据驱动校准。基于校准后的波编码PSF，用另一组神经网络开发了数据驱动重建。在校准和重建网络的训练中使用了15783幅二维波编码SSFSE腹部图像。
评估
由三位放射科医生对所提出的数据驱动方法的图像质量与使用迭代自校准和并行成像及压缩感知重建的传统方法进行独立且盲法比较，在噪声、对比度、锐度、伪影和可信度方面按-2至2的等级进行评分。还比较了这两种方法的计算时间。
统计检验
采用Wilcoxon符号秩检验比较图像质量，采用双尾t检验比较计算时间，P值小于0.05被认为具有统计学意义。
结果
使用所提出的方法计算速度平均加快了2.1倍。所提出的数据驱动自校准和重建方法显著降低了可感知的噪声水平（平均得分0.82，P < 0.0001）。
数据结论
所提出的数据驱动校准和重建实现了两倍的计算速度提升且降低了可感知噪声，为临床腹部SSFSE成像提供了快速且稳健的自校准和重建。
证据水平
1 技术效能：1期 《磁共振成像杂志》2020年；51:841 - 853。
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