Invertible Sharpening Network for MRI Reconstruction Enhancement
- URL: http://arxiv.org/abs/2206.02838v1
- Date: Mon, 6 Jun 2022 18:21:48 GMT
- Title: Invertible Sharpening Network for MRI Reconstruction Enhancement
- Authors: Siyuan Dong, Eric Z. Chen, Lin Zhao, Xiao Chen, Yikang Liu, Terrence
Chen, Shanhui Sun
- Abstract summary: We propose an invertible sharpening network (InvSharpNet) to improve the visual quality of MRI reconstructions.
Unlike the traditional methods that learn to map the input data to the ground truth, InvSharpNet adapts a backward training strategy that learns a blurring transform.
Experiments on various MRI datasets demonstrate that InvSharpNet can improve reconstruction sharpness with few artifacts.
- Score: 17.812760964428165
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-quality MRI reconstruction plays a critical role in clinical
applications. Deep learning-based methods have achieved promising results on
MRI reconstruction. However, most state-of-the-art methods were designed to
optimize the evaluation metrics commonly used for natural images, such as PSNR
and SSIM, whereas the visual quality is not primarily pursued. Compared to the
fully-sampled images, the reconstructed images are often blurry, where
high-frequency features might not be sharp enough for confident clinical
diagnosis. To this end, we propose an invertible sharpening network
(InvSharpNet) to improve the visual quality of MRI reconstructions. During
training, unlike the traditional methods that learn to map the input data to
the ground truth, InvSharpNet adapts a backward training strategy that learns a
blurring transform from the ground truth (fully-sampled image) to the input
data (blurry reconstruction). During inference, the learned blurring transform
can be inverted to a sharpening transform leveraging the network's
invertibility. The experiments on various MRI datasets demonstrate that
InvSharpNet can improve reconstruction sharpness with few artifacts. The
results were also evaluated by radiologists, indicating better visual quality
and diagnostic confidence of our proposed method.
Related papers
- On the Foundation Model for Cardiac MRI Reconstruction [6.284878525302227]
We propose a foundation model that uses adaptive unrolling, channel-shifting, and Pattern and Contrast-Prompt-UNet to tackle the problem.
The PCP-UNet is equipped with an image contrast and sampling pattern prompt.
arXiv Detail & Related papers (2024-11-15T18:15:56Z) - Attention Hybrid Variational Net for Accelerated MRI Reconstruction [7.046523233290946]
The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem.
This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image.
We propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domain.
arXiv Detail & Related papers (2023-06-21T16:19:07Z) - 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) - 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) - Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
Deep image prior was introduced as an effective prior for image reconstruction.
Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques.
We develop a two-stage learning paradigm to address the computational challenge.
arXiv Detail & Related papers (2021-11-23T15:08:26Z) - 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) - Multi-institutional Collaborations for Improving Deep Learning-based
Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
Deep learning methods have been shown to produce superior performance on MR image reconstruction.
These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations.
We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
arXiv Detail & Related papers (2021-03-03T03:04:40Z) - Deep Parallel MRI Reconstruction Network Without Coil Sensitivities [4.559089047554929]
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data.
The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image.
arXiv Detail & Related papers (2020-08-04T08:39:36Z) - Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI
Acquisition [19.422926534305837]
We propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition.
Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images.
Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data.
arXiv Detail & Related papers (2020-01-13T19:01:17Z)
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.