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

2021 ◽  
Author(s):  
Ziyu Li ◽  
Qiyuan Tian ◽  
Chanon Ngamsombat ◽  
Samuel Cartmell ◽  
John Conklin ◽  
...  
Keyword(s):  
Parallel Imaging ◽  
Brain Mri ◽  
High Fidelity ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals High-fidelity fast volumetric brain MRI using a hybrid denoising generative adversarial network (HDnGAN)

2021 ◽  
Author(s):  
Ziyu Li ◽  
Qiyuan Tian ◽  
Chanon Ngamsombat ◽  
Samuel Cartmell ◽  
John Conklin ◽  
...  
Keyword(s):  
Parallel Imaging ◽  
Mean Squared Error ◽  
Brain Mri ◽  
Similarity Index ◽  
Image Synthesis ◽  
Image Sharpness ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Significant Difference ◽  
Multiple Sclerosis Patients

Purpose: To improve the signal-to-noise ratio (SNR) of highly accelerated volumetric MRI while preserve realistic textures using a generative adversarial network (GAN). Methods: A 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. Results: HDnGAN (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. Conclusion: HDnGAN 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.

scholarly journals PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 61
Author(s):  
Jun Lv ◽  
Chengyan Wang ◽  
Guang Yang
Keyword(s):  
Parallel Imaging ◽  
Single Channel ◽  
Low Noise ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Knee Mri ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Abdominal Mri ◽  
Mri Reconstruction

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10−5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

scholarly journals 3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2978
Author(s):  
Hongtao Zhang ◽  
Yuki Shinomiya ◽  
Shinichi Yoshida
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Missing Values ◽  
Brain Mri ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Frequency Information ◽  
Training Costs ◽  
Adversarial Network

The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

scholarly journals Alzheimer's Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks

2021 ◽  
Author(s):  
surabhi sinha ◽  
Sophia I. Thomopoulos ◽  
Pradeep Lam ◽  
Alexandra Muir ◽  
Paul M. Thompson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Domain Adaptation ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network

Alzheimer's disease (AD) accounts for 60% of dementia cases worldwide; patients with the disease typically suffer from irreversible memory loss and progressive decline in multiple cognitive domains. With brain imaging techniques such as magnetic resonance imaging (MRI), microscopic brain changes are detectable even before abnormal memory loss is detected clinically. Patterns of brain atrophy can be measured using MRI, which gives us an opportunity to facilitate AD detection using image classification techniques. Even so, MRI scanning protocols and scanners differ across studies. The resulting differences in image contrast and signal to noise make it important to train and test classification models on multiple datasets, and to handle shifts in image characteristics across protocols (also known as domain transfer or domain adaptation). Here, we examined whether adversarial domain adaptation can boost the performance of a Convolutional Neural Network (CNN) model designed to classify AD. To test this, we used an Attention-Guided Generative Adversarial Network (GAN) to harmonize images from three publicly available brain MRI datasets - ADNI, AIBL and OASIS - adjusting for scanner-dependent effects. Our AG-GAN optimized a joint objective function that included attention loss, pixel loss, cycle-consistency loss and adversarial loss; the model was trained bidirectionally in an end-to-end fashion. For AD classification, we adapted the popular 2D AlexNet CNN to handle 3D images. Classification based on harmonized MR images significantly outperformed classification based on the three datasets in non-harmonized form, motivating further work on image harmonization using adversarial techniques.

Super-resolution of low-fidelity flow solutions via generative adversarial networks

SIMULATION ◽  
2021 ◽  
pp. 003754972110612
Author(s):  
Mahdi Pourbagian ◽  
Ali Ashrafizadeh
Keyword(s):  
Incompressible Flows ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
High Fidelity ◽  
Generative Adversarial Network ◽  
Flow Problems ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
High Fidelity Simulations ◽  
Computational Resources

While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.

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