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Magnetochemistry
Special Issues
Magnetic Resonances: Current Applications and Future Perspectives
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Magnetic Resonances: Current Applications and Future Perspectives

A special issue of Magnetochemistry (ISSN 2312-7481).

Deadline for manuscript submissions: closed (30 March 2026) | Viewed by 1584

Special Issue Editors

Dr. Federico Bruno

E-Mail Website
Guest Editor
Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
Interests: neuroradiology; MRI; focused ultrasound; tremor; spine imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Natella

E-Mail Website
Guest Editor
Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
Interests: MRI; MSK Imaging; bone tumors; soft tiffue tumors; DTI; radiomics; cardiac CT; cardiac MRI; prostate MRI; MSK ultrasound
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, MR imaging has shown notable applications in both the optimization of the image acquisition techniques that are already in use but also in a plethora of new techniques. This continuous technological growth has allowed for ever greater pathological characterization in all fields of interest, from musculoskeletal pathologies to pathologies of the brain, heart, and abdomen, with a high correlation between anatomical and pathophysiological data, shifting the attention from the definition of statical data to functional ones.

This Special Issue collects various reviews that, despite tackling different discussion topics, have all been collected with the aim of showing the extent to which the current state of the art and the implementation of new MRI techniques allow us to adopt a different way of looking at familiar pathologies, highly impacting prognostication and patient management.

Dr. Federico Bruno
Dr. Raffaele Natella
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Magnetochemistry is an international peer-reviewed open access monthly 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 2200 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

  • MRI
  • DTI
  • T1 mapping
  • T2 mapping
  • MR elastography
  • radiomics
  • ECV
  • DWI/ADC

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Published Papers (1 paper)

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Research

18 pages, 10969 KB  
Article
Simulation Data-Based Dual Domain Network (Sim-DDNet) for Motion Artifact Reduction in MR Images
by Seong-Hyeon Kang, Jun-Young Chung, Youngjin Lee and for The Alzheimer’s Disease Neuroimaging Initiative
Magnetochemistry 2026, 12(1), 14; https://doi.org/10.3390/magnetochemistry12010014 - 20 Jan 2026
Viewed by 806
Abstract
Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with [...] Read more.
Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article
(This article belongs to the Special Issue Magnetic Resonances: Current Applications and Future Perspectives)
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