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Diagnostics
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Artificial Intelligence in Magnetic Resonance Imaging
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Artificial Intelligence in Magnetic Resonance Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 673

Special Issue Editor

Dr. Aydin Eresen

E-Mail Website
Guest Editor
Department of Radiological Sciences, University of California, Irvine, CA 92868, USA
Interests: image and signal processing; machine learning; MRI; cancer treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Magnetic resonance imaging (MRI) has become a cornerstone of modern diagnostic medicine due to its non-invasive nature, absence of ionizing radiation, and exceptional sensitivity to soft tissue contrast. Nevertheless,  the full promise of MRI in clinical medicine has yet to be realized, constrained by inherent physical limitations and reliance on conventional analysis techniques.

The rapid advancements made in computational techniques, particularly in artificial intelligence (AI), have significantly transformed MRI data analysis. AI encompasses a range of methodologies—such as supervised, unsupervised, and reinforcement learning—extending to deep learning architectures that can extract complex patterns from large datasets. Over the past decade, the availability of extensive MRI datasets, combined with advances in fast MRI acquisition techniques and the development of novel sequences, has accelerated AI-driven innovations in this field. Applications now extend beyond post-processing and interpretation to include the generation of multiparametric MRI data from single-sequence acquisitions and the enhancement of spatial resolution.

This Special Issue of Diagnostics aims to highlight cutting-edge AI applications in MRI, spanning the full spectrum from data acquisition and sequence optimization to advanced image reconstruction and automated analysis. We welcome contributions that report novel algorithms, technical developments, and innovative strategies addressing current challenges and expanding the capabilities of MRI in both clinical and research settings.

Dr. Aydin Eresen
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 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. Diagnostics 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
  • artificial intelligence
  • diagnostic medicine
  • MRI data analysis
  • image processing

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Published Papers (1 paper)

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Research

14 pages, 14702 KB  
Article
Multi-Task Deep Learning on MRI for Tumor Segmentation and Treatment Response Prediction in an Experimental Model of Hepatocellular Carcinoma
by Guangbo Yu, Zigeng Zhang, Aydin Eresen, Qiaoming Hou, Vahid Yaghmai and Zhuoli Zhang
Diagnostics 2025, 15(22), 2844; https://doi.org/10.3390/diagnostics15222844 - 10 Nov 2025
Viewed by 560
Abstract
Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity [...] Read more.
Background: Assessing the efficacy of combination therapies in hepatocellular carcinoma (HCC) requires both accurate tumor delineation and biologically validated prediction of therapeutic response. Conventional MRI-based criteria, which rely primarily on tumor size, often fail to capture treatment efficacy due to tumor heterogeneity and pseudo-progression. This study aimed to develop and biologically validate a multi-task deep learning model that simultaneously segments HCC tumors and predicts treatment outcomes using clinically relevant multi-parametric MRI in a preclinical rat model. Methods: Orthotopic HCC tumors were induced in rats assigned to Control, Sorafenib, NK cell immunotherapy, and combination treatment groups. Multi-parametric MRI (T1w, T2w, and contrast enhanced MRI) scans were performed weekly. We developed a U-Net++ architecture incorporating a pre-trained EfficientNet-B0 encoder, enabling simultaneous segmentation and classification tasks. Model performance was evaluated through Dice coefficients and area under the receiver operator characteristic curve (AUROC) scores, and histological validation (H&E for viability, TUNEL for apoptosis) assessed biological correlations using linear regression analysis. Results: The multi-task model achieved precise tumor segmentation (Dice coefficient = 0.92, intersection over union (IoU) = 0.86) and reliably predicted therapeutic outcomes (AUROC = 0.97, accuracy = 85.0%). MRI-derived deep learning biomarkers correlated strongly with histological markers of tumor viability and apoptosis (root mean squared error (RMSE): viability = 0.1069, apoptosis = 0.013), demonstrating that the model captures biologically relevant imaging features associated with treatment-induced histological changes. Conclusions: This multi-task deep learning framework, validated against histology, demonstrates the feasibility of leveraging widely available clinical MRI sequences for non-invasive monitoring of therapeutic response in HCC. By linking imaging features with underlying tumor biology, the model highlights a translational pathway toward more clinically applicable strategies for evaluating treatment efficacy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Magnetic Resonance Imaging)
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