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Accelerated MRI Based on Compressed Sensing and Deep Learning
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Accelerated MRI Based on Compressed Sensing and Deep Learning

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3792

Special Issue Editor

Dr. Hyungseok Jang

E-Mail Website
Guest Editor
Department of Radiology, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
Interests: MRI; UTE; MSK imaging; neuro imaging; deep learning

Special Issue Information

Dear Colleagues,

Magnetic resonance imaging (MRI) is one of most important medical imaging modalities used in clinic due to its excellent soft tissue contrast. To form an MR image, a radiofrequency signal from spins of targeted nuclei (e.g., 1H, 13C, 19F, 31P, or 23Na) is acquired to form a 2D or 3D k-space (i.e., Fourier transform domain). To achieve the desired tissue contrasts or quantitative parameters using MRI, a long signal preparation or acquisition time is required, and thus a clinical MR exam that includes multiple imaging series with different image contrasts typically takes ~30 min to ~1 hours. Therefore, accelerated MR acquisition is crucial to minimizing both patient motion and discomfort. Moreover, accelerated MRI scans directly help in reducing healthcare costs by allowing more flexibility in patient scheduling.

Compressed sensing (CS) was first investigated in the field of conventional signal processing and allows the detection of signals from largely undersampled data by exploiting a property known as “sparsity.” CS has recently been incorporated into MRI by utilizing sparsity in either the native image domain or transformed image domain (i.e., the wavelet transform). More recently, deep learning (DL)-based accerlation techniques have been investigated in MRI.

In this Special Issue, novel accelerated MRI techniques based on CS and DL are presented.

Dr. Hyungseok Jang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic resonance imaging
  • compressed sensing
  • deep learning
  • accelerated imaging
  • undersampled data
  • MRI reconstruction

Published Papers (2 papers)

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Research

14 pages, 3879 KiB  
Article
Accelerated Quantitative 3D UTE-Cones Imaging Using Compressed Sensing
by Jiyo S. Athertya, Yajun Ma, Amir Masoud Afsahi, Alecio F. Lombardi, Dina Moazamian, Saeed Jerban, Sam Sedaghat and Hyungseok Jang
Sensors 2022, 22(19), 7459; https://doi.org/10.3390/s22197459 - 1 Oct 2022
Cited by 10 | Viewed by 1670
Abstract
In this study, the feasibility of accelerated quantitative Ultrashort Echo Time Cones (qUTE-Cones) imaging with compressed sensing (CS) reconstruction is investigated. qUTE-Cones sequences for variable flip angle-based UTE T1 mapping, UTE adiabatic T1ρ mapping, and UTE quantitative magnetization transfer modeling of [...] Read more.
In this study, the feasibility of accelerated quantitative Ultrashort Echo Time Cones (qUTE-Cones) imaging with compressed sensing (CS) reconstruction is investigated. qUTE-Cones sequences for variable flip angle-based UTE T1 mapping, UTE adiabatic T1ρ mapping, and UTE quantitative magnetization transfer modeling of macromolecular fraction (MMF) were implemented on a clinical 3T MR system. Twenty healthy volunteers were recruited and underwent whole-knee MRI using qUTE-Cones sequences. The k-space data were retrospectively undersampled with different undersampling rates. The undersampled qUTE-Cones data were reconstructed using both zero-filling and CS reconstruction. Using CS-reconstructed UTE images, various parameters were estimated in 10 different regions of interests (ROIs) in tendons, ligaments, menisci, and cartilage. Structural similarity, percentage error, and Pearson’s correlation were calculated to assess the performance. Dramatically reduced streaking artifacts and improved SSIM were observed in UTE images from CS reconstruction. A mean SSIM of ~0.90 was achieved for all CS-reconstructed images. Percentage errors between fully sampled and undersampled CS-reconstructed images were below 5% for up to 50% undersampling (i.e., 2× acceleration). High linear correlation was observed (>0.95) for all qUTE parameters estimated in all subjects. CS-based reconstruction combined with efficient Cones trajectory is expected to achieve a clinically feasible scan time for qUTE imaging. Full article
(This article belongs to the Special Issue Accelerated MRI Based on Compressed Sensing and Deep Learning)
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Graphical abstract

16 pages, 13654 KiB  
Article
An End-to-End Recurrent Neural Network for Radial MR Image Reconstruction
by Changheun Oh, Jun-Young Chung and Yeji Han
Sensors 2022, 22(19), 7277; https://doi.org/10.3390/s22197277 - 26 Sep 2022
Cited by 6 | Viewed by 1551
Abstract
Recent advances in deep learning have contributed greatly to the field of parallel MR imaging, where a reduced amount of k-space data are acquired to accelerate imaging time. In our previous work, we have proposed a deep learning method to reconstruct MR images [...] Read more.
Recent advances in deep learning have contributed greatly to the field of parallel MR imaging, where a reduced amount of k-space data are acquired to accelerate imaging time. In our previous work, we have proposed a deep learning method to reconstruct MR images directly from k-space data acquired with Cartesian trajectories. However, MRI utilizes various non-Cartesian trajectories, such as radial trajectories, with various numbers of multi-channel RF coils according to the purpose of an MRI scan. Thus, it is important for a reconstruction network to efficiently unfold aliasing artifacts due to undersampling and to combine multi-channel k-space data into single-channel data. In this work, a neural network named ‘ETER-net’ is utilized to reconstruct an MR image directly from k-space data acquired with Cartesian and non-Cartesian trajectories and multi-channel RF coils. In the proposed image reconstruction network, the domain transform network converts k-space data into a rough image, which is then refined in the following network to reconstruct a final image. We also analyze loss functions including adversarial and perceptual losses to improve the network performance. For experiments, we acquired k-space data at a 3T MRI scanner with Cartesian and radial trajectories to show the learning mechanism of the direct mapping relationship between the k-space and the corresponding image by the proposed network and to demonstrate the practical applications. According to our experiments, the proposed method showed satisfactory performance in reconstructing images from undersampled single- or multi-channel k-space data with reduced image artifacts. In conclusion, the proposed method is a deep-learning-based MR reconstruction network, which can be used as a unified solution for parallel MRI, where k-space data are acquired with various scanning trajectories. Full article
(This article belongs to the Special Issue Accelerated MRI Based on Compressed Sensing and Deep Learning)
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