Innovations in Diagnostic Radiology: AI, Advanced Imaging and Precision Medicine

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: 31 August 2026 | Viewed by 808

Special Issue Editor


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Guest Editor
Institute for Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
Interests: image processing; multimodal imaging; radiology; computed tomography; magnetic resonance imaging; diagnostic radiology; imaging; interventional radiology
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Special Issue Information

Dear Colleagues,

This Special Issue, Innovations in Diagnostic Radiology: AI, Advanced Imaging and Precision Medicine, explores the broader and more holistic role of artificial intelligence (AI) across the entire radiological workflow—not just in diagnosis, but in supporting and optimizing every step of the clinical process.

We welcome submissions that examine how AI technologies—including agents, automation tools, foundation models, and vision-language models (VLMs)—can enhance efficiency, consistency, and precision across medical imaging practices. Relevant topics include automated report generation, AI-guided protocol planning, intelligent triage systems, quality control, and robust classification algorithms that reduce human error. We are particularly interested in contributions demonstrating how AI can streamline operations, improve communication between departments, and support personalized clinical decision-making.

Beyond model performance, this Issue focuses on clinical integration, interpretability, and the practical utility of AI systems in real-world environments. Studies addressing challenges like data heterogeneity, limited annotations, workflow integration, and explainability are especially encouraged.

We welcome original research and reviews that highlight the impact of AI across diverse imaging modalities (MRI, CT, PET, ultrasound, and X-ray), with a strong focus on real-world application, reliability, and improved patient care.

Prof. Dr. Thomas Frauenfelder
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

  • medical imaging
  • worklfow optimization
  • vision-language models
  • clinical decision support
  • diagnostic radiology
  • AI agents in healthcare
  • explainable AI

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Published Papers (1 paper)

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Research

13 pages, 2412 KB  
Article
AI-Based Brain Volumetry Without MPRAGE? Evaluation of Synthetic T1-MPRAGE from 2D T2/FLAIR
by Ludwig Singer, Tim Alexius Möhle, Angelika Mennecke, Konstantin Huhn, Veit Rothhammer, Manuel Alexander Schmidt, Arnd Doerfler and Stefan Lang
Diagnostics 2026, 16(2), 317; https://doi.org/10.3390/diagnostics16020317 - 19 Jan 2026
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Abstract
Background: Automated AI-based brain volumetry is increasingly used in clinical practice. T1-weighted sequences (e.g., MPRAGE) are considered the current state-of-the art. However, due to faster acquisition and higher in-plane resolution, 2D anisotropic sequences are often preferred in clinical routine. However, these sequences [...] Read more.
Background: Automated AI-based brain volumetry is increasingly used in clinical practice. T1-weighted sequences (e.g., MPRAGE) are considered the current state-of-the art. However, due to faster acquisition and higher in-plane resolution, 2D anisotropic sequences are often preferred in clinical routine. However, these sequences cannot be processed with currently available AI-volumetry software. Thus, we here aimed to evaluate volumetric data from synthetic MPRAGE-like sequences (mprAIge). Methods: We analyzed 412 datasets (206 conventional MPRAGE and 206 T2w/FLAIR) from healthy volunteers (n = 36) and patients with multiple sclerosis (n = 140). Synthetic mprAIge was generated using SynthSR-CNN and assessed via assemblyNET on the volBrain platform. Total brain volume (TBV), gray and white matter volume (GMV/WMV), and key substructures were compared between mprAIge and conventional MPRAGE. Average volume differences (AVDs) and correlations were calculated. Results: Synthetic mprAIge was generated successfully in all 206 cases. Quantitative analysis demonstrated strong correlation and high agreement for key substructures. TBV showed excellent agreement (AVD: 2.75% for controls, 3.90% for MS patients; r = 0.99 and 0.97, respectively). White matter volume exhibited excellent agreement (AVD: −1.92% for controls, 0.28% for MS patients; r = 0.95). Hippocampal volume also demonstrated good to excellent agreement (AVD: 1.13% for controls, −1.92% for MS patients; r = 0.91 and 0.89, respectively). Conclusions: Synthetic mprAIge enables AI-volumetry software application without limitations. Its volumetric assessments align well with conventional MPRAGE, opening new opportunities for volumetric post-processing and mapping of disease progression. Full article
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