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

Abstract

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.

MAGnitude-Image-to-Complex K-space (MAGIC-K) Net: A Data Augmentation Network for Image Reconstruction

“…Augmentation of images from different patients is necessary to further improve the generalization performance. It is possible to validate the proposed method in other organs, e.g., abdomen [42], cardiac [43], knees [17], etc. It is also possible to apply the proposed augmentation method to unsupervised learning-based reconstructions when ground truth images are difficult to obtain.…”

“…Duan et al [16] formulated the generalized PI reconstruction as an energy minimization problem and derived a variable splitting optimization method. Lv et al [17] combined PI reconstruction with GAN to recover aliasing artifacts from undersampled k-space data, with an acceleration factor as high as six.…”

Compressed Sensing via Measurement-Conditional Generative Models

“…Chen et al [43] improved GAN-based MRI reconstruction by using wavelet packet decomposition in the network to effectively remove the input size constraints of GAN. Lv et al [44] improved GAN-based MRI reconstruction by introducing an additional regularizer that removes aliasing artifacts between the reconstructed image and its corresponding ground truth image. Yurt et al [45] allowed a GAN to efficiently learn to produce fully sampled reference volume from its undersampled volume by decomposing the complex volumetric image recovery tasks into a cascade of progressive cross-sectional tasks defined across the rectilinear orientations (i.e., axial, coronal, and sagittal).…”

Semi-supervised CycleGAN for domain transformation of chest CT images and its application to opacity classification of diffuse lung diseases

“…In [25], a supervised learning model of Cycle-GAN is proposed to transform low-dose PET images to full-dose images. In [14], a model combining parallel imaging with GAN for the reconstruction of MRI is proposed. This method effectively reconstructs multi-channel MR images at a low noise level for undersampling patterns.…”

“…The aim of this paper is to perform a domain transformation of chest CT images taken by different CT devices in two hospitals, and we propose a semi-supervised CycleGAN with a classification loss function to achieve domain transformation with high classification accuracy. For example, when we compare our method with image generation using the GAN-based method [4,14,15,22,24] that aim at generating high-quality images from undersampled images, our method aims to transform CT images taken at a certain hospital so that they can be accurately classified by the classifier trained in another hospital. The proposed method is trained with a semi-supervised learning manner to reduce the cost of annotation by combining CycleGAN and an additional loss based on the classification accuracy.…”