Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Recent Advances in X-Ray Sensing and Imaging
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances 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: 20 March 2025 | Viewed by 5090

Special Issue Editor

Dr. Bahaa Ghammraoui

E-Mail Website
Guest Editor
Center for Diagnostic and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
Interests: diagnostic imaging; X-ray spectral imaging; quantitative imaging; statistical assessment; machine learning; clinical study design

Special Issue Information

Dear Colleagues,

The realm of X-ray imaging and sensing is currently undergoing a transformative era marked by remarkable advancements in detector technologies. Photon-counting X-ray detectors (PCDs) and high-spatial-resolution detectors, particularly those utilizing direct conversion methods, are leading this revolution. These technological leaps have significantly enhanced spectral imaging applications, heralding a new era of ultra-high-resolution computed tomography (CT) and pioneering applications in fields like X-ray phase-contrast imaging and X-ray coherent scatter imaging.

The impacts of these advancements are profound. PCDs, for instance, allow for the simultaneous capturing and discernment of spectral information, a feat previously unattainable with traditional detectors. This capability is pivotal in differentiating between materials and tissues based on their energy-dependent attenuation characteristics, opening new avenues in diagnostic imaging and material sciences.

Furthermore, the development of high-spatial-resolution detectors has enabled unprecedented detail in imaging, pushing the boundaries of what can be visualized and diagnosed. Enhanced resolution is particularly beneficial in fields requiring meticulous detail, such as neurology, orthopedics, and oncology.

Major CT manufacturers are actively developing PCD prototypes, demonstrating the significant industry investment and recognition of the potential for these innovative detectors to transform medical diagnostics and industrial applications. This is further evidenced by the early adoption of these detectors in clinical practice.

The purpose of this Special Issue is to showcase the latest developments of X-ray sensing and imaging technologies and explore their future trajectories. We aim to bring together a diverse range of research, covering (but not limited to) the following topics:

  1. Innovations in photon-counting X-ray detectors and their applications.
  2. Advances in high-spatial-resolution detectors using direct conversion methods.
  3. The role of these technologies in enhancing spectral imaging applications.
  4. The integration and impact of these detectors in ultra-high-resolution CT.
  5. Emerging applications in X-ray phase-contrast imaging and X-ray coherent scatter imaging.
  6. Clinical and industrial case studies demonstrating the practical applications of these advanced detectors.
  7. Quantitative data (e.g., improved resolution, reduced radiation dose) illustrating the impact of these advancements.

We invite researchers and practitioners from academia and industry to contribute their latest findings, reviews, and perspectives on these exciting developments.

Dr. Bahaa Ghammraoui
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 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. 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
  • photon-counting detectors
  • spectral imaging
  • high-spatial-resolution detectors
  • direct conversion detectors
  • X-ray phase-contrast imaging
  • X-ray coherent scatter imaging
  • material characterization
  • machine learning in imaging
  • industrial X-ray applications

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

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

Research

14 pages, 7140 KiB  
Article
Hybrid Reconstruction Approach for Polychromatic Computed Tomography in Highly Limited-Data Scenarios
by Alessandro Piol, Daniel Sanderson, Carlos F. del Cerro, Antonio Lorente-Mur, Manuel Desco and Mónica Abella
Sensors 2024, 24(21), 6782; https://doi.org/10.3390/s24216782 - 22 Oct 2024
Viewed by 524
Abstract
Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially [...] Read more.
Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm. Full article
(This article belongs to the Special Issue Recent Advances in X-Ray Sensing and Imaging)
►▼ Show Figures

Figure 1

18 pages, 1561 KiB  
Article
Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation
by Raziye Kubra Kumrular and Thomas Blumensath
Sensors 2024, 24(20), 6654; https://doi.org/10.3390/s24206654 - 16 Oct 2024
Viewed by 770
Abstract
Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral [...] Read more.
Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. Measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods—namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method—but also does not require complex parameter tuning. Full article
(This article belongs to the Special Issue Recent Advances in X-Ray Sensing and Imaging)
►▼ Show Figures

Figure 1

8 pages, 3943 KiB  
Communication
Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector
by Sabri Amer, Andrew Xu, Aldo Badano and Eshan Dahal
Sensors 2024, 24(16), 5307; https://doi.org/10.3390/s24165307 - 16 Aug 2024
Viewed by 663
Abstract
Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that [...] Read more.
Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications. Full article
(This article belongs to the Special Issue Recent Advances in X-Ray Sensing and Imaging)
►▼ Show Figures

Figure 1

11 pages, 2740 KiB  
Article
Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT
by Afrouz Ataei, Vasantha Vasan, Todd C. Soesbe, Cecelia C. Brewington, Zhongxing Zhou, Lifeng Yu, Kristina A. Hallam and Liqiang Ren
Sensors 2024, 24(11), 3567; https://doi.org/10.3390/s24113567 - 1 Jun 2024
Viewed by 892
Abstract
Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human [...] Read more.
Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting. Full article
(This article belongs to the Special Issue Recent Advances in X-Ray Sensing and Imaging)
►▼ Show Figures

Figure 1

16 pages, 9035 KiB  
Article
Nanoscale Three-Dimensional Imaging of Integrated Circuits Using a Scanning Electron Microscope and Transition-Edge Sensor Spectrometer
by Nathan Nakamura, Paul Szypryt, Amber L. Dagel, Bradley K. Alpert, Douglas A. Bennett, William Bertrand Doriese, Malcolm Durkin, Joseph W. Fowler, Dylan T. Fox, Johnathon D. Gard, Ryan N. Goodner, James Zachariah Harris, Gene C. Hilton, Edward S. Jimenez, Burke L. Kernen, Kurt W. Larson, Zachary H. Levine, Daniel McArthur, Kelsey M. Morgan, Galen C. O’Neil, Nathan J. Ortiz, Christine G. Pappas, Carl D. Reintsema, Daniel R. Schmidt, Peter A. Schultz, Kyle R. Thompson, Joel N. Ullom, Leila Vale, Courtenay T. Vaughan, Christopher Walker, Joel C. Weber, Jason W. Wheeler and Daniel S. Swetzadd Show full author list remove Hide full author list
Sensors 2024, 24(9), 2890; https://doi.org/10.3390/s24092890 - 30 Apr 2024
Cited by 1 | Viewed by 1414
Abstract
X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron [...] Read more.
X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but it is difficult to implement due to the competing requirements of X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while addressing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot on a metal target held micrometers away from the sample of interest, while the TES spectrometer isolates target photons with a high signal-to-noise ratio. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enables nanoscale, element-specific X-ray imaging in a compact footprint. The proof of concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in six layers of a Cu-SiO2 integrated circuit, and a path toward finer resolution and enhanced imaging capabilities is discussed. Full article
(This article belongs to the Special Issue Recent Advances in X-Ray Sensing and 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 1-5
Back to TopTop