sensors-logo

Journal Browser

Journal Browser

AI-Driven Innovations for Enhanced Signal Intelligence: Applications in Radar and Biomedical Imaging Sensing

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1124

Special Issue Editors

Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
Interests: antenna arrays; RF/microwave circuits and systems; radar systems; additive manufacturing techniques

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310, USA
Interests: RF/microwave circuits and systems; smart antenna/phased array; remote sensing technique; RF/analog signal processing
1. Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
2. School of Electrical Engineering and Automation, Changshu Institute of Technology, Suzhou 215500, China
Interests: deep learning in medical/optical imaging; image reconstruction; tomography

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) and machine learning have revolutionized the field of signal processing, enabling unprecedented capabilities in radar systems and biomedical imaging technologies. This Special Issue focuses on innovative applications of deep learning and machine learning algorithms that enhance signal intelligence and imaging capabilities in these critical domains.

We invite contributions that explore the ways in which AI methodologies can address challenges in signal acquisition, processing, and interpretation, specifically within radar systems and biomedical imaging contexts. Of particular interest are approaches that demonstrate improved target detection, classification, and tracking in radar applications, as well as enhanced tissue characterization, anomaly detection, and diagnostic accuracy in biomedical imaging.

Dr. Hong Tang
Dr. Hanxiang Zhang
Dr. Yu Shi
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

  • artificial intelligence (AI)
  • machine learning
  • biomedical imaging

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
Select all
Export citation of selected articles as:

Research

18 pages, 3733 KB  
Article
Dual-Head Pix2Pix Network for Material Decomposition of Conventional CT Projections with Photon-Counting Guidance
by Yanyun Liu, Zhiqiang Li, Yang Wang, Ruitao Chen, Dinghong Duan, Xiaoyi Liu, Xiangyu Liu, Yu Shi, Songlin Li and Shouping Zhu
Sensors 2025, 25(19), 5960; https://doi.org/10.3390/s25195960 - 25 Sep 2025
Viewed by 807
Abstract
Material decomposition in X-ray imaging is essential for enhancing tissue differentiation and reducing the radiation dose, but the clinical adoption of photon-counting detectors (PCDs) is limited by their high cost and technical complexity. To address this, we propose Dual-head Pix2Pix, a PCD-guided deep [...] Read more.
Material decomposition in X-ray imaging is essential for enhancing tissue differentiation and reducing the radiation dose, but the clinical adoption of photon-counting detectors (PCDs) is limited by their high cost and technical complexity. To address this, we propose Dual-head Pix2Pix, a PCD-guided deep learning framework that enables simultaneous iodine and bone decomposition from single-energy X-ray projections acquired with conventional energy-integrating detectors. The model was trained and tested on 1440 groups of energy-integrating detector (EID) projections with their corresponding iodine/bone decomposition images. Experimental results demonstrate that the Dual-head Pix2Pix outperforms baseline models. For iodine decomposition, it achieved a mean absolute error (MAE) of 5.30 ± 1.81, representing an ~10% improvement over Pix2Pix (5.92) and a substantial advantage over CycleGAN (10.39). For bone decomposition, the MAE was reduced to 9.55 ± 2.49, an ~6% improvement over Pix2Pix (10.18). Moreover, Dual-head Pix2Pix consistently achieved the highest MS-SSIM, PSNR, and Pearson correlation coefficients across all benchmarks. In addition, we performed a cross-domain validation using projection images acquired from a conventional EID-CT system. The results show that the model successfully achieved the effective separation of iodine and bone in this new domain, demonstrating a strong generalization capability beyond the training distribution. In summary, Dual-head Pix2Pix provides a cost-effective, scalable, and hardware-friendly solution for accurate dual-material decomposition, paving the way for the broader clinical and industrial adoption of material-specific imaging without requiring PCDs. Full article
Show Figures

Figure 1

Back to TopTop