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Sensors
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Sensing Technologies in Digital Radiology and Image Analysis
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Sensing Technologies in Digital Radiology and Image Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1999

Special Issue Editors

Prof. Dr. Tie-Qiang Li

E-Mail Website
Guest Editor
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
Interests: MRI of the brain; deep learning methods
Special Issues, Collections and Topics in MDPI journals
Dr. Chunliang Wang

E-Mail Website
Guest Editor
Division of Medical Imaging, School of Technology and Health, Royal Institute of Technology, Stockholm, Sweden
Interests: medical imaging; radiology; AI technology

Special Issue Information

Dear Colleagues,

The rapid evolution of digital radiology and image analysis is transforming medical diagnostics, being primarily driven by cutting-edge sensing technologies. Advanced sensors, including high-resolution detectors, multispectral imaging systems, and AI-integrated sensor arrays, are enhancing the precision, speed, and accessibility of radiological imaging. These innovations enable the earlier detection of diseases, improved image quality, and reduced radiation exposure, addressing challenges in traditional imaging modalities. This Special Issue of Sensors aims to showcase the latest advancements in sensing technologies that are redefining digital radiology and image analysis. We invite contributions on novel sensor designs, AI-driven image processing, real-time imaging systems, and their clinical applications. Topics include, but are not limited to, low-dose imaging sensors, photon-counting detectors, wearable radiological sensors, and machine learning for automated image analysis. By highlighting interdisciplinary research, this Special Issue seeks to bridge sensor technology with clinical radiology, fostering innovations that improve patient outcomes and diagnostic workflows.

Prof. Dr. Tie-Qiang Li
Dr. Chunliang Wang
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. Sensors 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

  • digital radiology
  • image analysis
  • sensing technologies
  • high-resolution detectors
  • multispectral imaging
  • AI-driven imaging
  • photon-counting detectors
  • low-dose imaging
  • wearable sensors
  • machine learning

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Published Papers (2 papers)

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25 pages, 4607 KB  
Article
CHARMS: A CNN-Transformer Hybrid with Attention Regularization for MRI Super-Resolution
by Xia Li, Haicheng Sun and Tie-Qiang Li
Sensors 2026, 26(2), 738; https://doi.org/10.3390/s26020738 - 22 Jan 2026
Cited by 1 | Viewed by 669
Abstract
Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field [...] Read more.
Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article
(This article belongs to the Special Issue Sensing Technologies in Digital Radiology and Image Analysis)
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Review

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22 pages, 3866 KB  
Review
Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability
by Francesco Felicetti, Francesco Lamonaca, Domenico Luca Carnì and Sandra Costanzo
Sensors 2026, 26(3), 1039; https://doi.org/10.3390/s26031039 - 5 Feb 2026
Cited by 1 | Viewed by 980
Abstract
This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and [...] Read more.
This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and segmentation, ultimately impacting diagnostic outcomes. From the analysis of approximately 20,000 papers sourced from three databases (PubMed, Scopus, IEEE Xplore), last searched in December 2025, the need for standardized imaging protocols and quality control measures emerges as a critical theme. Studies were included if they involved radiomic feature extraction and evaluated the impact of image quality variations on feature robustness and no formal risk-of-bias assessment was performed. A total of 105 studies were included, covering different medical imaging modalities. Across the included studies, noise, motion, acquisition and reconstruction parameters, and other artifacts consistently emerged as major sources of radiomic feature instability. Indeed, in most papers, IQA is neglected, while the effect of poor-quality images is reported. This research identifies and discusses the relevant issues reported in clinical practice, as well as the main metrics adopted for image quality evaluation. Through a comprehensive review of current literature and an analysis of emerging trends, this paper highlights the urgent need for innovative solutions in image quality metrics tailored to radiomics applications. Full article
(This article belongs to the Special Issue Sensing Technologies in Digital Radiology and Image Analysis)
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