High‐fidelity fast volumetric brain MRI using synergistic wave‐controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)
Authors: Li Ziyu , Tian Qiyuan , Ngamsombat Chanon , Cartmell Samuel , Conklin John , Filho Augusto Lio M. Gonçalves , Lo Wei‐Ching , Wang Guangzhi , Ying Kui , Setsompop Kawin , Fan Qiuyun , Bilgic Berkin , Cauley Stephen , Huang Susie Y. 
Published: December 2021

Purpose

The goal of this study is to leverage an advanced fast imaging technique, wave‐controlled aliasing in parallel imaging (Wave‐CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high‐quality high‐signal‐to‐noise‐ratio (SNR) volumetric magnetic resonance imaging (MRI).

Methods

Three‐dimensional (3D) T2‐weighted fluid‐attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave‐CAIPI sequence (acceleration factor R = 3 × 2, 2.75 min) and a standard T2‐sampling perfection with application‐optimized contrasts by using flip angle evolution (SPACE) FLAIR sequence (R = 2, 7.25 min). A hybrid denoising GAN entitled “HDnGAN” consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave‐CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from eight MS patients not seen during training. HDnGAN was compared to other denoising methods including adaptive optimized nonlocal means (AONLM), block matching with 4D filtering (BM4D), modified U‐Net (MU‐Net), and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and Visual Geometry Group (VGG) perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise.

Results

HDnGAN effectively denoised low‐SNR Wave‐CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ = 10–3) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ = 10–3) significantly improved the SNR of Wave‐CAIPI images (p < 0.001), outperformed AONLM (p = 0.015), BM4D (p < 0.001), MU‐Net (p < 0.001), and 3D GAN (λ = 10–3) (p < 0.001) regarding image sharpness, and outperformed MU‐Net (p < 0.001) and 3D GAN (λ = 10–3) (p = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ = 10–3) (4.25 ± 0.43) was significantly higher than those from Wave‐CAIPI (3.69 ± 0.46, p = 0.003), BM4D (3.50 ± 0.71, p = 0.001), MU‐Net (3.25 ± 0.75, p < 0.001), and 3D GAN (λ = 10–3) (3.50 ± 0.50, p < 0.001), with no significant difference compared to standard FLAIR images (4.38 ± 0.48, p = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels.

Conclusion

HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave‐CAIPI to achieve high‐fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs.