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J. Imaging
Special Issues
Emerging Technologies for Less Invasive Diagnostic Imaging
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Emerging Technologies for Less Invasive Diagnostic Imaging

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1322

Special Issue Editors

Dr. Francesca Angelone

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: advanced medical image analysis; artificial intelligence for denoising; segmentation and classification in medical imaging; computer-aided diagnosis solutions
Dr. Noemi Pisani

E-Mail Website
Guest Editor
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy
Interests: artificial intelligence and machine learning in medical imaging; advanced image processing techniques; radiomics feature extraction; texture analysis; development of imaging biomarkers for precision medicine
Dr. Armando Ricciardi

E-Mail Website
Guest Editor
Department of Engineering, University of Sannio, 82100 Benevento, Italy
Interests: lab-on-fiber technology; optical fiber sensors; optical biosensors; medical devices; metamaterials; plasmonics; smart materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging is a cornerstone of modern diagnosis, yet many techniques remain invasive to patients. In recent years, several innovative approaches have emerged, ranging from hardware development to advanced image analysis, with the potential to transform the diagnostic imaging landscape by reducing invasiveness while improving image quality, speed, and diagnostic accuracy.

This Special Issue is dedicated to exploring how new technologies and processing techniques, including artificial intelligence (AI), can help make diagnostic imaging less invasive and more patient centered. Among these, optical imaging stands out as a promising non-invasive modality that, when integrated with AI-based methods, can be enhanced with diagnostic capabilities traditionally limited to more invasive procedures, such as tissue characterization and functional assessment.

We welcome original research articles, comprehensive reviews, and technical notes that explore how modern technologies and advanced processing methods can support the development of safer, faster, and more accurate diagnostic pathways.

Topics of interest include, but are not limited to the following:

  • AI-based reconstruction techniques to reduce or eliminate the need for contrast agents and ionizing radiation.
  • AI-enhanced optical imaging approaches to extract diagnostic features from superficial and subsurface tissue structures.
  • Image enhancement, denoising, and super-resolution techniques for improved image quality and reduced acquisition times.
  • Virtual biopsies, decision support, and computational modeling as alternatives to invasive sampling and follow-up procedures.
  • Integration of multimodal data and AI for predictive modeling and personalized diagnostic strategies.
  • Innovative optoelectronic technologies for diagnostics to complement diagnostic imaging.
  • Molecular imaging and advanced spectroscopic techniques for in vivo detection and quantification of disease-specific biomarkers, enabling early and precise diagnosis.
  • Raman spectroscopy and other marker-free approaches combined with AI for non-invasive characterization of tissues.
  • Advanced optical microscopy methods, such as confocal, multiphoton, and light sheet microscopy.

By bringing together diverse perspectives and pioneering studies, this Special Issue aims to foster a multidisciplinary dialog that encourages the integration of artificial intelligence and emerging technologies to achieve less invasive and high-precision diagnostic imaging.

Dr. Francesca Angelone
Dr. Noemi Pisani
Dr. Armando Ricciardi
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 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. Journal of Imaging 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 1800 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

  • non-invasive diagnostics
  • less invasive imaging
  • patient-centered imaging
  • tissue characterization
  • functional assessment
  • biomarker quantification
  • real-time intraoperative guidance
  • early diagnosis
  • personalized diagnostics
  • safer and faster diagnostic pathways

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 5227 KB  
Article
Dynamic Fractional Flow Reserve from 4D-CTA: A Novel Framework for Non-Invasive Coronary Assessment
by Shuo Wang, Rong Liu and Li Zhang
J. Imaging 2025, 11(10), 330; https://doi.org/10.3390/jimaging11100330 - 24 Sep 2025
Viewed by 36
Abstract
Current fractional flow reserve computed tomography (FFRCT) methods use static imaging, potentially missing critical hemodynamic changes during the cardiac cycle. We developed a novel dynamic FFRCT framework using 4D-CTA data to capture temporal coronary dynamics throughout the complete cardiac cycle. [...] Read more.
Current fractional flow reserve computed tomography (FFRCT) methods use static imaging, potentially missing critical hemodynamic changes during the cardiac cycle. We developed a novel dynamic FFRCT framework using 4D-CTA data to capture temporal coronary dynamics throughout the complete cardiac cycle. Our automated pipeline integrates 4D-CTA processing, temporally weighted geometric modeling, and patient-specific boundary conditions derived from actual flow measurements. Preliminary validation in three patients (four vessels) showed that dynamic FFRCT values (0.720, 0.797, 0.811, and 0.952) closely matched invasive FFR measurements (0.70, 0.78, 0.78, and 0.94) with improved accuracy compared to conventional static methods. The dynamic approach successfully captured physiologically relevant hemodynamic variations, addressing inter-patient variability limitations of standardized approaches. This study establishes the clinical feasibility of dynamic FFRCT computation, potentially improving non-invasive coronary stenosis assessment for clinical decision-making and treatment planning. Full article
(This article belongs to the Special Issue Emerging Technologies for Less Invasive Diagnostic Imaging)
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16 pages, 1247 KB  
Article
Non-Invasive Retinal Pathology Assessment Using Haralick-Based Vascular Texture and Global Fundus Color Distribution Analysis
by Ouafa Sijilmassi
J. Imaging 2025, 11(9), 321; https://doi.org/10.3390/jimaging11090321 - 19 Sep 2025
Viewed by 221
Abstract
This study analyzes retinal fundus images to distinguish healthy retinas from those affected by diabetic retinopathy (DR) and glaucoma using a dual-framework approach: vascular texture analysis and global color distribution analysis. The texture-based approach involved segmenting the retinal vasculature and extracting eight Haralick [...] Read more.
This study analyzes retinal fundus images to distinguish healthy retinas from those affected by diabetic retinopathy (DR) and glaucoma using a dual-framework approach: vascular texture analysis and global color distribution analysis. The texture-based approach involved segmenting the retinal vasculature and extracting eight Haralick texture features from the Gray-Level Co-occurrence Matrix. Significant differences in features such as energy, contrast, correlation, and entropy were found between healthy and pathological retinas. Pathological retinas exhibited lower textural complexity and higher uniformity, which correlates with vascular thinning and structural changes observed in DR and glaucoma. In parallel, the global color distribution of the full fundus area was analyzed without segmentation. RGB intensity histograms were calculated for each channel and averaged across groups. Statistical tests revealed significant differences, particularly in the green and blue channels. The Mahalanobis distance quantified the separability of the groups per channel. These results indicate that pathological changes in retinal tissue can also lead to detectable chromatic shifts in the fundus. The findings underscore the potential of both vascular texture and color features as non-invasive biomarkers for early retinal disease detection and classification. Full article
(This article belongs to the Special Issue Emerging Technologies for Less Invasive Diagnostic Imaging)
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14 pages, 4724 KB  
Article
Uncertainty-Guided Active Learning for Access Route Segmentation and Planning in Transcatheter Aortic Valve Implantation
by Mahdi Islam, Musarrat Tabassum, Agnes Mayr, Christian Kremser, Markus Haltmeier and Enrique Almar-Munoz
J. Imaging 2025, 11(9), 318; https://doi.org/10.3390/jimaging11090318 - 17 Sep 2025
Viewed by 361
Abstract
Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure for treating severe aortic stenosis, where optimal vascular access route selection is critical to reduce complications. It requires careful selection of the iliac artery with the most favourable anatomy, specifically, one with the [...] Read more.
Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure for treating severe aortic stenosis, where optimal vascular access route selection is critical to reduce complications. It requires careful selection of the iliac artery with the most favourable anatomy, specifically, one with the largest diameters and no segments narrower than 5 mm. This process is time-consuming when carried out manually. We present an active learning-based segmentation framework for contrast-enhanced Cardiac Magnetic Resonance (CMR) data, guided by probabilistic uncertainty and pseudo-labelling, enabling efficient segmentation with minimal manual annotation. The segmentations are then fed into an automated pipeline for diameter quantification, achieving a Dice score of 0.912 and a mean absolute percentage error (MAPE) of 4.92%. An ablation study using pre- and post-contrast CMR showed superior performance with post-contrast data only. Overall, the pipeline provides accurate segmentation and detailed diameter profiles of the aorto-iliac route, helping the assessment of the access route. Full article
(This article belongs to the Special Issue Emerging Technologies for Less Invasive Diagnostic Imaging)
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