Magnetic Resonance Data Acquisition and Image Reconstruction Techniques
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 164
Special Issue Editors
Interests: inverse problems; image processing; medical imaging; image reconstruction; magnetic resonance imaging (MRI); computational imaging; quantitative MRI; deep learning image reconstruction and processing; DCE-MRI; MR angiography
Interests: medical imaging; radiomics; myocardial perfusion imaging; PET/CT; PET/MRI
Interests: neuroimaging; deep-learning-based method; magnetic resonance imaging reconstruction; regularization and optimization method
Special Issue Information
Dear Colleagues,
Magnetic resonance imaging (MRI) has become an indispensable tool in modern medicine, providing critical anatomical and functional information. The continuous advancements in MR data acquisition and image reconstruction techniques have significantly enhanced the quality, speed, and versatility of MRI, enabling more precise and comprehensive diagnostic capabilities.
Traditional MRI methods, while effective, often face limitations such as long acquisition times and artifacts that can impede accurate diagnosis. Recent innovations in MR technology, including accelerated acquisition methods, novel reconstruction algorithms, and the integration of artificial intelligence, have begun to address these challenges, offering faster imaging and improved image quality.
This Special Issue of Information invites contributions focusing on the latest developments in MR data acquisition and image reconstruction techniques. We welcome both reviews and original research articles that explore innovative approaches to the following topics:
- Accelerated MRI acquisition methods;
- Advanced image reconstruction algorithms;
- Artifact-reduction techniques;
- AI and machine learning applications in MRI;
- High-resolution and quantitative imaging;
- Multimodal and functional imaging enhancements.
Our goal is to showcase cutting-edge research that enhances the robustness, reproducibility, and clinical applicability of MRI. We encourage submissions that aim to translate these technological advancements into routine clinical practice, ultimately improving patient outcomes through more accurate and efficient imaging solutions.
Dr. Zhifeng Chen
Prof. Dr. Lijun Lu
Prof. Dr. Mingfeng Jiang
Dr. Hongfu Sun
Guest Editors
Manuscript Submission Information
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Keywords
- magnetic resonance imaging (MRI)
- MR data acquisition
- image reconstruction techniques
- accelerated MRI
- advanced reconstruction algorithms
- artifact reduction
- artificial intelligence in MRI
- high-resolution imaging
- quantitative imaging
- multimodal imaging
- functional imaging
- clinical applicability
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: MRI Super-Resolution Using Plug-And-Play Priors and Rigid Transformation
Authors: Lisimachos P. Kondi; Matina Christina Zerva
Affiliation: Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece
Abstract: Advancements in medical imaging have greatly benefited from the development of super-resolution techniques, which enhance the spatial resolution of images, offering improved diagnostic capabilities. In this paper, we propose a novel approach for brain magnetic resonance imaging (MRI) super-resolution that combines the power of plug-and-play priors (PPP) and rigid transformations. The PPP framework incorporates prior information into the super-resolution process, while the integration of rigid transformation allows for precise alignment of low-resolution images to the high-resolution reference space. We demonstrate the effectiveness of our method through quantitative evaluations and comparisons with existing techniques. Our method successfully enhances the spatial resolution of LR images, resulting in improved image quality and diagnostic capabilities. The experimental results demonstrate the superiority of our approach over existing techniques, underscoring its potential for clinical applications in neuroimaging.
