Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings.

BACKGROUND AND PURPOSE: The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings. MATERIALS AND METHODS: Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists. RESULTS: The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05). CONCLUSIONS: The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN).

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.