Accelerated Post-contrast Wave-CAIPI T1 SPACE Achieves Equivalent Diagnostic Performance Compared With Standard T1 SPACE for the Detection of Brain Metastases in Clinical 3T MRI

Filho, Augusto Lio M. Gonçalves; Conklin, John; Longo, Maria Gabriela Figueiró; Cauley, Stephen F.; Polak, Daniel; Liu, Wei; Splitthoff, Daniel Nicolas; Lo, Wei‐Ching; Kirsch, John E.; Setsompop, Kawin; Schaefer, Pamela W.; Huang, Susie Y.; Rapalino, Otto

doi:10.3389/fneur.2020.587327

Cited by 18 publications

(14 citation statements)

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“…The performance could be further improved by optimizing the parameters of Wave‐CAIPI 52 and parallel MRI using wave encoding and virtual conjugate coils 53 . Further, HDnGAN can be used for a variety of other Wave‐CAIPI‐accelerated 3D sequences beyond T 2 ‐weighted FLAIR, 45 including 3D T 1 ‐weighted magnetization prepared rapid gradient echo (MPRAGE), 15,54 susceptibility‐weighted imaging, 55 T 1 ‐weighted SPACE, 56,57 and 3D balanced steady‐state free precession (bSSFP), 58 as well as images reconstructed using other accelerated methods such as compressed sensing and LORAKS 10,59 . HDnGAN can also benefit other clinical applications of Wave‐CAIPI such as whole‐brain direct myelin water imaging, 60 4D lung MRI, 61 and aortic 4D flow MRI 62 .…”

Section: Discussionmentioning

confidence: 99%

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

Tian

Ngamsombat

et al. 2022

Medical Physics

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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) T 2 -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 T 2 -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 1000

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Section: Discussionmentioning

confidence: 99%

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

Tian

Ngamsombat

et al. 2022

Medical Physics

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“…6, Table 2). Further, HDnGAN can be used for a variety of other Wave-CAIPI-accelerated 3D sequences beyond T 2 -weighted FLAIR 41 , including 3D T 1 -weighted magnetization prepared rapid gradient echo (MPRAGE) 15,52 , susceptibility-weighted imaging 53 , and T 1 -weighted SPACE 54,55 , as well as images reconstructed using other accelerated methods such as compressed sensing and LORAKS 10,56 . Finally, the generator of HDnGAN can be replaced with any CNN (e.g., variational network 57 ) that reconstructs images directly from k-space data in order to recover image sharpness and realistic textural details in the reconstructed images.…”

Section: Discussionmentioning

confidence: 99%

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

Tian

Ngamsombat

et al. 2021

Preprint

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PurposeTo improve the signal-to-noise ratio (SNR) of highly accelerated volumetric MRI while preserve realistic textures using a generative adversarial network (GAN).MethodsA hybrid GAN for denoising entitled “HDnGAN” with a 3D generator and a 2D discriminator was proposed to denoise 3D T2-weighted fluid-attenuated inversion recovery (FLAIR) images acquired in 2.75 minutes (R=3×2) using wave-controlled aliasing in parallel imaging (Wave-CAIPI). HDnGAN was trained on data from 25 multiple sclerosis patients by minimizing a combined mean squared error and adversarial loss with adjustable weight λ. Results were evaluated on eight separate patients by comparing to standard T2-SPACE FLAIR images acquired in 7.25 minutes (R=2×2) using mean absolute error (MAE), peak SNR (PSNR), structural similarity index (SSIM), and VGG perceptual loss, and by two neuroradiologists using a five-point score regarding gray-white matter contrast, sharpness, SNR, lesion conspicuity, and overall quality.ResultsHDnGAN (λ=0) produced the lowest MAE, highest PSNR and SSIM. HDnGAN (λ=10−3) produced the lowest VGG loss. In the reader study, HDnGAN (λ=10−3) significantly improved the gray-white contrast and SNR of Wave-CAIPI images, and outperformed BM4D and HDnGAN (λ=0) regarding image sharpness. The overall quality score from HDnGAN (λ=10−3) was significantly higher than those from Wave-CAIPI, BM4D, and HDnGAN (λ=0), with no significant difference compared to standard images.ConclusionHDnGAN concurrently benefits from improved image synthesis performance of 3D convolution and increased training samples for training the 2D discriminator on limited data. HDnGAN generates images with high SNR and realistic textures, similar to those acquired in longer times and preferred by neuroradiologists.

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“…spatially varying g-factor) [6]. The two readers used a predefined 5-point scale, where positive numbers favored the sequence on the right and negative numbers favored the sequence on the left of the screen (Table 3) [11]. A third neuroradiologist (P.J.C.…”

Section: Semiquantitative Evaluationmentioning

confidence: 99%

Evaluation of highly accelerated wave controlled aliasing in parallel imaging (Wave-CAIPI) susceptibility-weighted imaging in the non-sedated pediatric setting: a pilot study

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Accelerated Post-contrast Wave-CAIPI T1 SPACE Achieves Equivalent Diagnostic Performance Compared With Standard T1 SPACE for the Detection of Brain Metastases in Clinical 3T MRI

Cited by 18 publications

References 22 publications

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

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

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

Evaluation of highly accelerated wave controlled aliasing in parallel imaging (Wave-CAIPI) susceptibility-weighted imaging in the non-sedated pediatric setting: a pilot study

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