Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic
Resonance
- URL: http://arxiv.org/abs/2209.10298v1
- Date: Wed, 21 Sep 2022 12:13:18 GMT
- Title: Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic
Resonance
- Authors: Chen Qin and Daniel Rueckert
- Abstract summary: In recent years, there has been a significant growth in the use of AI and Machine Learning algorithms, especially Deep Learning (DL) based methods, for medical image reconstruction.
The use of DL-based image reconstruction also provides promising opportunities to transform the way cardiac images are acquired and reconstructed.
In this chapter, we will review recent advances in DL-based reconstruction techniques for cardiac imaging, with emphasis on cardiac magnetic resonance (CMR) image reconstruction.
- Score: 10.60359890890818
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence (AI) and Machine Learning (ML) have shown great
potential in improving the medical imaging workflow, from image acquisition and
reconstruction to disease diagnosis and treatment. Particularly, in recent
years, there has been a significant growth in the use of AI and ML algorithms,
especially Deep Learning (DL) based methods, for medical image reconstruction.
DL techniques have shown to be competitive and often superior over conventional
reconstruction methods in terms of both reconstruction quality and
computational efficiency. The use of DL-based image reconstruction also
provides promising opportunities to transform the way cardiac images are
acquired and reconstructed. In this chapter, we will review recent advances in
DL-based reconstruction techniques for cardiac imaging, with emphasis on
cardiac magnetic resonance (CMR) image reconstruction. We mainly focus on
supervised DL methods for the application, including image post-processing
techniques, model-driven approaches and k-space based methods. Current
limitations, challenges and future opportunities of DL for cardiac image
reconstruction are also discussed.
Related papers
- Deep Learning for Accelerated and Robust MRI Reconstruction: a Review [28.663292249133864]
Deep learning (DL) has emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI)
This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction.
arXiv Detail & Related papers (2024-04-24T07:02:03Z) - Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction [42.95604565673447]
This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models.
We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/ computed tomography (CT) datasets.
arXiv Detail & Related papers (2024-04-12T18:21:08Z) - Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and
Unsupervised Learning [13.17680480211064]
We propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction.
Each proposed trained block consists of a deterministic MBIR solver and a neural network.
We demonstrate the efficacy of this learned hybrid model for low-dose CT image reconstruction with limited training data.
arXiv Detail & Related papers (2023-11-19T20:23:59Z) - Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks.
In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics.
Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
arXiv Detail & Related papers (2022-09-19T09:43:19Z) - 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) - Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
CT Reconstruction [108.06731611196291]
We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies.
We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
arXiv Detail & Related papers (2022-03-10T14:22:54Z) - AI-Enabled Ultra-Low-Dose CT Reconstruction [8.135337706680097]
In this paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image quality at an ultra-low-dose level comparable to that of radiography.
The reconstruction results from clinical datasets show that excellent images can be reconstructed using SUGAR from 36 projections.
arXiv Detail & Related papers (2021-06-17T22:13:11Z) - 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) - Explaining Clinical Decision Support Systems in Medical Imaging using
Cycle-Consistent Activation Maximization [112.2628296775395]
Clinical decision support using deep neural networks has become a topic of steadily growing interest.
clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend.
We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
arXiv Detail & Related papers (2020-10-09T14:39:27Z) - Learned Spectral Computed Tomography [0.0]
We propose a Deep Learning imaging method for Spectral Photon-Counting Computed Tomography.
The method takes the form of a two-step learned primal-dual algorithm that is trained using case-specific data.
The proposed approach is characterised by fast reconstruction capability and high imaging performance, even in limited-data cases.
arXiv Detail & Related papers (2020-03-09T13:39:12Z)
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.