Introduction

MRI is capable of acquiring various contrast images based on the physical properties of the tissue. Therefore, MRI examinations usually contain multi-contrast images, which however can bring the problem of prolonged acquisition time. As such, much work has been done to improve the acquisition efficiency of MRI. Multi-contrast images may share redundant information. For example, T2w-FLAIR contrast ¹ relies on property of the T2 relaxation and water component of the tissue, which also present in T2- and diffusion-weighted images. Recently, deep-learning using a neural network has been applied in contrast synthesis. For instance, Nencka et al. ² proposed to use multiple contrast images, such as T1w, T2w, and diffusion-weighted images, to generate T2w-FLAIR images. However, the DWI images used were acquired using the single-shot EPI technique which suffers from low-resolution and distortion problem. Thus the spatial mismatch among different contrasts introduces errors inevitably. Recently, a fast distortion-free multi-contrast method using point-spread-function (PSF) encoded EPI (PSF-EPI) ³, including T1w, T2w-FLAIR, T2w and diffusion-weighted imaging, was proposed. To further improve the time efficiency, in this study, DWI acquired with PSF-EPI ^4,5 were combined with T2W images to generate T2w-FLAIR by taking the advantages of high-resolution and distortion-free of PSF-EPI.

Methods

(1) Data acquisition: Detailed data acquisition protocols can be found in Table. 1, in which images of different contrasts were acquired for the brain, including PSF-EPI DWI, T2w-TSE and T2w-FLAIR. PSF-EPI DWI was acquired with 4-fold acceleration along the PE direction and 14-fold acceleration along the PSF encoding direction. Additional sensitivity and calibration data were acquired for the reconstruction of PSF-EPI ⁵. All images were acquired using FOV = 220×220×100 mm³ with 25 axial slices covering the whole brain. The acquisition matrix size of PSF-EPI is 221×220. The images of T2w-TSE and T2w-FLAIR were interpolated to the same matrix size. Ten healthy volunteers provided with written informed consent were scanned on a Philips 3T scanner (Philips Healthcare, Best, The Netherlands). The mean DWIs (calculated across 6 diffusion directions) along with the b=0 s/mm² images and T2w-TSE images were used as the input and the T2w-FLAIR images were used as the labels to train the neural network, thus to achieve T2w-FLAIR generation.
(2) Network architecture: The overall flowchart of the proposed method is based on conditional generative adversarial network (CGAN) ⁶, as illustrated in Fig. 1. The mean DWI along with the b=0 s/mm² and T2w-TSE images were fed into the generator to generate the T2w-FLAIR images, which were paired with the corresponding acquired T2w-FLAIR images and fed into the discriminator. The network architectures of the generator and discriminator are shown in Fig. 2. A 2D U-net ⁷ was used as the base structure of the generator. The network consisted of 19 convolutional layers, 4 convolutional layers with strides for downsampling, 4 deconvolutional layers with strides for upsampling, and 4 feature contracting paths. The discriminator network consisted of 4 convolutional layers and 4 convolutional layers with strides for downsampling. Batch-normalization (BN) and ReLU were used for each layer. The input layer of the generator consisted of 3 channels (including 1 b = 0 s/mm² image and 1 mean DWI and 1 T2w-TSE image) and the input of the discriminator consisted of 2 channels (the generated and the acquired T2w-FLAIR images).
(3) Training and evaluation: Images from 7 volunteers were used for training, images from 2 volunteers were used as a validation set, and images from 1 volunteer were used as a test set. Data were augmented through patching with the patch size of 64. Thus the input dimension of the generator network was 64×64×3, and the output dimension was 64×64×1. The input dimension of the discriminator was 64×64×2, and the output dimension was 8×8×1. To verify the importance of DWI, T2w-FLAIR generation without PSF-DWI was also implemented (i.e. only using T2w-TSE, thus the input dimension of the generator consisted of 1 channel). SSIM (Structural Similarity Index) ⁸ was used to evaluate the accuracy. The network was trained and evaluated using Keras ⁹ and RMSprop was used as the optimizer.

Results and Discussion

Fig. 3 shows the generated T2w-FLAIR images using the proposed method with or without PSF-DWI (denoted by CGAN and CGAN w/o DWI, respectively). Compared with the acquired T2w-FLAIR, both results show good consistencies in structures and contrasts. However, the T2w-FLAIR generated with PSF-DWI shows higher SSIM values than the results without PSF-DWI. Meanwhile, the former can also keep the detailed structures better than the latter, as in the areas pointed by the red arrowheads. The high-resolution and distortion-free images from PSF-EPI can ensure that the images across different contrasts would not be affected by the spatial mismatch due to the geometry distortion and low-resolution of conventional SS-EPI. Thus these high quality images can help to improve the performances of the proposed method.

Conclusion

The proposed deep-learning based method can generate high-quality T2w-FLAIR images with the aids of distortion-free PSF-EPI DWI, thus to improve the time efficiency of MRI. Further systematic clinical studies are warranted to clarify its generalization ability.

Acknowledgements

No acknowledgement found.