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

Abstract

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.

Figure 1:

A, Illustration shows proposed variable-density (VD) sampling pattern for VD single-shot fast spin-echo imaging. B, Flowchart shows variational network (VN) architecture implemented for VD single-shot fast spin-echo imaging. Input k-space measurements and corresponding coil sensitivity maps were used as inputs to VN architecture. C, Image shows that each VN block contains data-consistency bypass, regularization bypass, and input bypass. These bypasses were summed together as output of VN block. Variables in green and weights of 31 Gaussian radial basis functions in f were optimized during training.

Figure 2:

A, Graph shows image assessments in percentage for variational network (VN) and conventional parallel imaging and compressed sensing (PICS) reconstruction when evaluated independently by three blinded readers in terms of overall image quality (IQ), perceived signal-to-noise ratio (SNR), image contrast, image sharpness, and residual artifacts. Each color bar represents percentage of cases with same score. B, Graph shows image assessments in mean scores for VN and conventional PICS reconstruction. Each color bar represents mean score of each category. Error bars are standard error of mean. * = Statistically different results with P < .05.

Figure 3:

Non–contrast-enhanced coronal images in a 15-year-old girl show, A,C, conventional parallel imaging and compressed sensing (PICS) reconstruction and, B,D, improved perceived signal-to-noise ratio (SNR) with variational network (VN) approach. Zoomed images of regions indicated by dashed lines in A and B are shown in C and D. SNR improvement is indicated with VN (arrow in D) compared with PICS reconstruction (arrow in C).

Figure 4:

Non–contrast-enhanced coronal images in a 14-year-old boy show perceived signal-to-noise ratio (SNR) and mitigation of residual artifacts with, A, conventional parallel imaging and compressed sensing (PICS) reconstruction and, B, variational network (VN) approach. Improved delineation of vessels in liver is seen with VN approach (arrow in B) compared with PICS (arrow in A) because of reduced residual artifacts.

Figure 5:

Non–contrast-enhanced coronal images of liver lesion (arrow) in a 19-year-old man reconstructed with, A, conventional parallel imaging and compressed sensing (PICS) reconstruction and, B, variational network (VN) approach. VN approach achieved comparable images of lesion with improved perceived SNR.