Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Advances in Medical Physics and Quantitative Imaging
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Medical Physics and Quantitative Imaging

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 532

Special Issue Editor

Dr. Oleksandra V. Ivashchenko

E-Mail Website
Guest Editor
Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, 9711 GZ Groningen, The Netherlands
Interests: medical imaging; cancer imaging; image analysis; nuclear medicine; radiation dosimetry

Special Issue Information

Dear Colleagues,

Medical physics and quantitative imaging have rapidly evolved in recent years, driven by advances in imaging technologies, computational modeling, and data analysis methods. These advances allow for the increasingly precise, quantitative assessment of anatomy, function, and molecular processes, enabling improved diagnostics, treatment planning, and therapy monitoring. However, challenges remain in translating innovations from research settings into robust clinical applications and in fully exploiting quantitative imaging for personalized medicine.

This Special Issue aims to provide a platform for the latest research on cutting-edge developments in medical physics and quantitative imaging, emphasizing both methodological advances and their applications in clinical or preclinical settings. We invite contributions that address fundamental principles, innovative imaging techniques, quantitative data analysis methods, and integration with other diagnostic or therapeutic modalities.

Potential topics include, but are not limited to, the following:

  • Novel imaging modalities and hybrid imaging systems (PET/MR, SPECT/CT, multi-spectral CT, functional MRI, etc.);
  • Advances in image reconstruction, modeling, and quantitative analysis;
  • Radiation dose optimization and dosimetry in clinical imaging and therapy;
  • Machine learning and AI-driven quantitative imaging;
  • Multi-parametric and multi-modal imaging for disease characterization;
  • Development and validation of imaging biomarkers;
  • Real-time imaging techniques and in-procedure guidance systems;
  • Standardization, reproducibility, and quality assurance in quantitative imaging;
  • Translational studies bridging preclinical imaging and clinical applications;
  • Novel computational methods for image processing, the correction of artifacts, or uncertainty quantification.

Original research articles highlighting innovative methods, comparative studies, and translational applications are particularly encouraged. Review articles that provide a comprehensive overviews of emerging technologies or methodological approaches are also welcome.

We aim to gather a collection of high-quality contributions that advance the understanding, application, and clinical translation of medical physics and quantitative imaging technologies.

Dr. Oleksandra V. Ivashchenko
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. Applied Sciences 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 2400 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 physics
  • quantitative imaging
  • hybrid imaging
  • image reconstruction
  • dosimetry and radiation dose optimization
  • imaging biomarkers
  • functional and molecular imaging
  • machine learning in medical imaging
  • multi-parametric imaging
  • translational imaging studies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 973 KB  
Article
ViTUNet: Vision Transformer U-Net Hybrid Model for Carious Lesions Segmentation on Bitewing Dental Images
by Vincent Majanga, Ernest Mnkandla, Ekundayo Olufisayo Sunday, Bosun Ajala and Thottempundi Sree
Appl. Sci. 2026, 16(8), 3693; https://doi.org/10.3390/app16083693 - 9 Apr 2026
Viewed by 232
Abstract
Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer [...] Read more.
Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer U-NeT (ViTUNet) model framework, which combines the self-attention mechanism of the vision transformer (ViT) to capture global information while maintaining the extraction of local features via U-NeT. This proposed architecture introduces vision transformers to the existing residual convolution blocks in the U-Net encoder path, thereby capturing both local and global features. The decoder path then rebuilds this information into high-quality segmentation maps with accurately highlighted boundaries/edges. This model is utilized to segment carious lesions in bitewing dental radiographs. These images are pre-processed using augmentation, morphological operations, and segmentation to identify the boundaries/edges of the regions of interest (caries/cavity). The proposed method is evaluated on an augmented dataset containing 3000 image–watershed mask pairs. It was trained on 2400 training images and tested on 600 testing images. The experimental results exemplified significant improvements in segmentation performance, achieving 98.45% validation accuracy, 97.88% validation Dice coefficient, and 95.87% validation intersection over union (IoU) metric scores. These results are superior compared to other conventional and state-of-the-art U-NeT models, thus highlighting the impact of transformer-based hybrid architectures in improving medical image segmentation tasks. Full article
(This article belongs to the Special Issue Advances in Medical Physics and Quantitative Imaging)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop