Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks

• URL: http://arxiv.org/abs/2306.02886v1
• Date: Mon, 5 Jun 2023 13:53:57 GMT
• Title: Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks
• Authors: Xiaohan Liu, Yanwei Pang, Xuebin Sun, Yiming Liu, Yonghong Hou, Zhenchang Wang, Xuelong Li
• Abstract summary: Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. We propose a novel convolutional operator called Faster Fourier Convolution (FasterFC) to replace the two consecutive convolution operations. A 2D accelerated MRI method, FasterFC-End-to-End-VarNet, which uses FasterFC to improve the sensitivity maps and reconstruction quality. A 3D accelerated MRI method called FasterFC-based Single-to-group Network (FAS-Net) that utilizes a single-to-group algorithm to guide k-space domain reconstruction
• Score: 87.87578529398019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. However, accurately reconstructing images from partial scan data (i.e., incomplete k-space matrices) remains challenging due to lack of an effectively global receptive field in both spatial and k-space domains. To address this problem, we propose the following: (1) a novel convolutional operator called Faster Fourier Convolution (FasterFC) to replace the two consecutive convolution operations typically used in convolutional neural networks (e.g., U-Net, ResNet). Based on the spectral convolution theorem in Fourier theory, FasterFC employs alternating kernels of size 1 in 3D case) in different domains to extend the dual-domain receptive field to the global and achieves faster calculation speed than traditional Fast Fourier Convolution (FFC). (2) A 2D accelerated MRI method, FasterFC-End-to-End-VarNet, which uses FasterFC to improve the sensitivity maps and reconstruction quality. (3) A multi-stage 3D accelerated MRI method called FasterFC-based Single-to-group Network (FAS-Net) that utilizes a single-to-group algorithm to guide k-space domain reconstruction, followed by FasterFC-based cascaded convolutional neural networks to expand the effective receptive field in the dual-domain. Experimental results on the fastMRI and Stanford MRI Data datasets demonstrate that FasterFC improves the quality of both 2D and 3D reconstruction. Moreover, FAS-Net, as a 3D high-resolution multi-coil (eight) accelerated MRI method, achieves superior reconstruction performance in both qualitative and quantitative results compared with state-of-the-art 2D and 3D methods.

Related papers

• StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations [3.4453266252081645]
  We introduce StoDIP, a new algorithm that extends the ground-truth-free Deep Image Prior (DIP) reconstruction to 3D MRF imaging. tested on a dataset of whole-brain scans from healthy volunteers, StoDIP demonstrated superior performance over the ground-truth-free reconstruction baselines, both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2024-08-05T10:32:06Z)
• Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction [15.537910100051866]
  We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI) We propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN) Our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets.
  arXiv  Detail & Related papers  (2024-06-18T15:15:12Z)
• Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models [0.0]
  We create a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. We show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image).
  arXiv  Detail & Related papers  (2024-04-18T17:40:23Z)
• Deep Learning-based MRI Reconstruction with Artificial Fourier Transform (AFT)-Net [14.146848823672677]
  We introduce a unified complex-valued deep learning framework-Artificial Fourier Transform Network (AFTNet) AFTNet can be readily used to solve image inverse problems in domain transformation. We show that AFTNet achieves superior accelerated MRI reconstruction compared to existing approaches.
  arXiv  Detail & Related papers  (2023-12-18T02:50:45Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• Cross-Modality High-Frequency Transformer for MR Image Super-Resolution [100.50972513285598]
  We build an early effort to build a Transformer-based MR image super-resolution framework. We consider two-fold domain priors including the high-frequency structure prior and the inter-modality context prior. We establish a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution images.
  arXiv  Detail & Related papers  (2022-03-29T07:56:55Z)
• DONet: Dual-Octave Network for Fast MR Image Reconstruction [98.04121143761017]
  The Dual-Octave Network (DONet) is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data. Our framework provides three appealing benefits.
  arXiv  Detail & Related papers  (2021-05-12T21:41:02Z)
• Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction [75.35200719645283]
  We propose the Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components. By reformulating the complex operations using octave convolutions, our model shows a strong ability to capture richer representations of MR images.
  arXiv  Detail & Related papers  (2021-04-12T10:51:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.