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Compressed Sensing and MRI Reconstruction
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Compressed Sensing and MRI Reconstruction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 12040

Special Issue Editor

Prof. Dr. Tie-Qiang Li

E-Mail Website
Guest Editor
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
Interests: MRI of the brain; deep learning methods

Special Issue Information

Dear Colleagues,

Compressed sensing (CS) is a promising approach that employs the sparsity property as a precondition for signal recovery. The sparsity as the main premise in designing CS algorithms for signal compression or reconstruction is characterized by a few nonzero coefficients in one of the transformation domains. Therefore, the sparse signals can be fully reconstructed from a reduced set of incoherent measurements. The developed CS frameworks for the sparse signals’ reconstruction span a wide range of techniques that can be largely divided into the following categories: matching pursuit, constrained convex optimization, and the Bayesian approach. CS-based techniques have been increasingly applied to improve the time efficiency of various biomedical imaging modalities, such as computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). More recently, inspired by the success in the field of computer vision, deep-learning technique has emerged as one of the most prominent approaches for the reconstruction of CS-based MRI. In this special issue, the most up-to-date original research papers and reviews are invited in the areas of CS applications to biomedical signal recovery and image reconstruction, while a greater focus will be given to recent advances in deep-learning based CS-MRI reconstruction.

Prof. Dr. Tie-Qiang Li
Guest Editor

Manuscript Submission Information

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Published Papers (3 papers)

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Research

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23 pages, 7431 KiB  
Article
Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE
by Emilio López-Ales, Rosa-María Menchón-Lara, Federico Simmross-Wattenberg, Manuel Rodríguez-Cayetano, Marcos Martín-Fernández and Carlos Alberola-López
Sensors 2024, 24(4), 1313; https://doi.org/10.3390/s24041313 - 18 Feb 2024
Viewed by 1240
Abstract
Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring [...] Read more.
Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment. Full article
(This article belongs to the Special Issue Compressed Sensing and MRI Reconstruction)
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16 pages, 22864 KiB  
Article
CS-MRI Reconstruction Using an Improved GAN with Dilated Residual Networks and Channel Attention Mechanism
by Xia Li, Hui Zhang, Hao Yang and Tie-Qiang Li
Sensors 2023, 23(18), 7685; https://doi.org/10.3390/s23187685 - 6 Sep 2023
Cited by 5 | Viewed by 2617
Abstract
Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to [...] Read more.
Compressed sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically generative adversarial networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning reconstruction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study, we present a novel GAN-based model that delivers superior performance without the model complexity escalating. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network’s receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby focusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies affirmed the efficacy of the modified modules within the network. Incorporating DR networks and CAM elevated the peak signal-to-noise ratios (PSNR) of the reconstructed images by about 1.2 and 0.8 dB, respectively, on average, even at 10× CS acceleration. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent stability but also outperforming most of the compared networks in terms of PSNR and SSIM. When compared with U-net, DR-CAM-GAN’s average gains in SSIM and PSNR were 14% and 15%, respectively. Its MSE was reduced by a factor that ranged from two to seven. The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction. Full article
(This article belongs to the Special Issue Compressed Sensing and MRI Reconstruction)
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Review

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26 pages, 3805 KiB  
Review
A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction
by Md. Biddut Hossain, Rupali Kiran Shinde, Sukhoon Oh, Ki-Chul Kwon and Nam Kim
Sensors 2024, 24(3), 753; https://doi.org/10.3390/s24030753 - 24 Jan 2024
Cited by 9 | Viewed by 7052
Abstract
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clinical decision-making, enhancing patient safety, facilitating efficient workflows, and contributing to the validity [...] Read more.
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clinical decision-making, enhancing patient safety, facilitating efficient workflows, and contributing to the validity of research studies and clinical trials. Recently, deep learning has demonstrated several advantages over conventional MRI reconstruction methods. Conventional methods rely on manual feature engineering to capture complex patterns and are usually computationally demanding due to their iterative nature. Conversely, DL methods use neural networks with hundreds of thousands of parameters and automatically learn relevant features and representations directly from the data. Nevertheless, there are some limitations to DL-based techniques concerning MRI reconstruction tasks, such as the need for large, labeled datasets, the possibility of overfitting, and the complexity of model training. Researchers are striving to develop DL models that are more efficient, adaptable, and capable of providing valuable information for medical practitioners. We provide a comprehensive overview of the current developments and clinical uses by focusing on state-of-the-art DL architectures and tools used in MRI reconstruction. This study has three objectives. Our main objective is to describe how various DL designs have changed over time and talk about cutting-edge tactics, including their advantages and disadvantages. Hence, data pre- and post-processing approaches are assessed using publicly available MRI datasets and source codes. Secondly, this work aims to provide an extensive overview of the ongoing research on transformers and deep convolutional neural networks for rapid MRI reconstruction. Thirdly, we discuss several network training strategies, like supervised, unsupervised, transfer learning, and federated learning for rapid and efficient MRI reconstruction. Consequently, this article provides significant resources for future improvement of MRI data pre-processing and fast image reconstruction. Full article
(This article belongs to the Special Issue Compressed Sensing and MRI Reconstruction)
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