Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction

• URL: http://arxiv.org/abs/2301.00127v1
• Date: Sat, 31 Dec 2022 05:43:21 GMT
• Title: Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
• Authors: Jie Feng, Ruimin Feng, Qing Wu, Zhiyong Zhang, Yuyao Zhang and Hongjiang Wei
• Abstract summary: Implicit Neuraltruth (INR) has appeared as powerful DL-based tool for solving the inverse problem. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data. The proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks.
• Score: 11.661657147506519
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.

Related papers

• Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models [2.5412006057370893]
  Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems. Our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks.
  arXiv  Detail & Related papers  (2024-07-03T01:37:56Z)
• A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging [14.493622422645053]
  fMRI compressing poses unique challenges due to its intricate temporal dynamics, low signal-to-noise ratio, and complicated underlying redundancies. This paper reports a novel compression paradigm specifically tailored for fMRI data based on Implicit Neural Representation (INR)
  arXiv  Detail & Related papers  (2023-11-30T05:54:37Z)
• Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction [15.444386058967579]
  There are still problems with dynamic MRI k-space reconstruction based on Compressed Sensing (CS) In this paper, we propose a novel robust lowrank dynamic MRI reconstruction optimization model via highly under-sampled Fourier Transform (DFT) Experiments on dynamic MRI data demonstrate the superior performance proposed method in terms of both reconstruction accuracy and time complexity.
  arXiv  Detail & Related papers  (2023-10-23T13:34:59Z)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) [68.8204255655161]
  Cycle Consistent Generative Adversarial Network (GAN) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images. Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases. This work demonstrates the use of a generative deep learning model that can outperform classical DAEs to improve low-field MRI images and does not require image pairs.
  arXiv  Detail & Related papers  (2023-07-12T00:01:00Z)
• A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation [9.388253054229155]
  implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object. The proposed method outperforms existing methods by suppressing the aliasing artifacts and noise. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.
  arXiv  Detail & Related papers  (2022-10-19T10:16:03Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction [33.82162420709648]
  We propose modeling the proximal operators of unrolled neural networks with scale-equivariant convolutional neural networks. Our approach demonstrates strong improvements over the state-of-the-art unrolled neural networks under the same memory constraints.
  arXiv  Detail & Related papers  (2022-04-21T23:29:52Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• Over-and-Under Complete Convolutional RNN for MRI Reconstruction [57.95363471940937]
  Recent deep learning-based methods for MR image reconstruction usually leverage a generic auto-encoder architecture. We propose an Over-and-Under Complete Convolu?tional Recurrent Neural Network (OUCR), which consists of an overcomplete and an undercomplete Convolutional Recurrent Neural Network(CRNN) The proposed method achieves significant improvements over the compressed sensing and popular deep learning-based methods with less number of trainable parameters.
  arXiv  Detail & Related papers  (2021-06-16T15:56:34Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)

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.