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Recent Innovations in X-Ray Sensing and Imaging
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Recent Innovations in X-Ray Sensing and Imaging

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1776

Special Issue Editors

Dr. Xin Liu

E-Mail Website
Guest Editor
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Interests: X-ray phase-contrast imaging; computational imaging; X-ray single-pixel
Dr. Jianheng Huang

E-Mail Website
Guest Editor
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Interests: X-ray phase-contrast imaging

Special Issue Information

Dear Colleagues,

X-ray sensing and imaging underpin critical applications in medical diagnostics, industrial nondestructive testing (NDT), security screening, and materials science due to their unparalleled penetration depth and non-invasive capabilities.

This Special Issue seeks original research and reviews on ​emerging X-ray imaging, detection, algorithms, and applications​ that push the boundaries of X-ray sensing.

Potential topics include, but are not limited to:

  • Dual-energy;
  • X-ray phase-contrast imaging;
  • X-ray scatter imaging;
  • X-ray imaging with deep learning;
  • X-ray dark-field imaging;
  • Scintillators and detectors.

Dr. Xin Liu
Dr. Jianheng Huang
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

  • X-ray imaging
  • X-ray detection
  • X-ray sensing
  • medical diagnostics
  • industrial nondestructive testing (NDT)

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

Published Papers (2 papers)

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Research

21 pages, 14892 KB  
Article
Comparative Evaluation of Machine Learning and Conventional Material Decomposition Algorithms for Spectral Chest Radiography Using a CdTe Photon-Counting Detector
by Sriharsha Marupudi and Bahaa Ghammraoui
Sensors 2026, 26(10), 3202; https://doi.org/10.3390/s26103202 - 19 May 2026
Viewed by 172
Abstract
Spectral chest radiography with photon-counting detectors (PCDs) enables energy-resolved acquisition for bone/soft-tissue separation, but quantitative performance depends on detector cross-talk and the selected material decomposition algorithm. We performed a controlled simulation study comparing a conventional low-order polynomial decomposition model with two machine learning [...] Read more.
Spectral chest radiography with photon-counting detectors (PCDs) enables energy-resolved acquisition for bone/soft-tissue separation, but quantitative performance depends on detector cross-talk and the selected material decomposition algorithm. We performed a controlled simulation study comparing a conventional low-order polynomial decomposition model with two machine learning regressors (multilayer perceptron (MLP) and support vector regression (SVR)) for a cadmium telluride (CdTe) PCD. A Geant4-derived detector response model, coupled with a charge-transport model, was integrated into a physics-forward model including charge sharing and Poisson quantum noise. Digital LucAl/IEC 62220-2-1 phantoms with aluminum and polymethyl methacrylate inserts were used for quantitative bias/root mean square error (RMSE) evaluation, and task-based low-contrast detectability that was evaluated using an exponential transformation of the free-response operating characteristic (EFROC) method using a matched-filter template. Performance was evaluated over clinically relevant dose levels (0.07–7.5 mAs), calibration grid densities (3×3 to 8×8), and numbers of energy thresholds (M=2–6). Polynomial decomposition was stable under sparse calibration, whereas ML methods benefited strongly from denser calibration and additional thresholds; SVR achieved the lowest RMSE under dense calibration, while MLP produced smoother maps and improved soft-tissue detectability at low-to-intermediate dose. At high dose, all methods approached near-ideal detection performance. These results quantify practical trade-offs between calibration requirements, quantitative accuracy, and low-contrast detectability for PCD-based spectral chest radiography. Full article
(This article belongs to the Special Issue Recent Innovations in X-Ray Sensing and Imaging)
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10 pages, 1603 KB  
Article
Beam Tracking X-Ray Phase-Contrast Imaging Using a Conventional X-Ray Source
by Jiaqi Li, Jianheng Huang, Xin Liu, Yaohu Lei, Botao Mai and Chenggong Zhang
Sensors 2025, 25(19), 6089; https://doi.org/10.3390/s25196089 - 2 Oct 2025
Viewed by 1212
Abstract
To address the issue of insufficient contrast in conventional X-ray absorption imaging for biological soft tissues and weakly absorbing materials, this paper proposes a beam tracking X-ray phase-contrast imaging system using a conventional X-ray source. A periodic pinhole array mask is placed between [...] Read more.
To address the issue of insufficient contrast in conventional X-ray absorption imaging for biological soft tissues and weakly absorbing materials, this paper proposes a beam tracking X-ray phase-contrast imaging system using a conventional X-ray source. A periodic pinhole array mask is placed between the X-ray source and the sample to spatially modulate the X-ray beam, dividing it into multiple independent sub-beams. Each sub-beam is deflected due to the modulation effect of the sample, resulting in slight positional shifts in the intensity patterns formed on the detector. The experiments employ an X-ray source with a 400 μm focal spot and use a two-dimensional step-scanning approach to acquire image sequences of various samples. The experimental results show that this method can extract the edge profile and structural changes in the samples to some extent, and it demonstrates good contrast and detail recovery under weak absorption conditions. These results suggest that this method has certain application potential in material inspection, non-destructive testing, and related fields. Full article
(This article belongs to the Special Issue Recent Innovations in X-Ray Sensing and Imaging)
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