Special Issue "X-ray Luminescence and Fluorescence"

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 7839

Special Issue Editors

Dr. Changqing Li
E-Mail Website
Guest Editor
School of Engineering, University of California, Merced, Merced, CA 95343, USA
Interests: biomedical optics; fluorescence molecular tomography; x-ray luminescence optical tomography; Cerenkov luminescence imaging/tomography; x-ray computerized tomography; positron emission tomography; multimodality imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ge Wang
E-Mail Website
Guest Editor
Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
Interests: biomedical imaging; artificial intelligence; deep learning
Prof. Dr. Jeffrey N. Anker
E-Mail Website
Guest Editor
Department of Chemistry, Bioengineering Department, Center for Optical Materials Science and Engineering (COMSET) and Environmental Toxicology Program, Clemson University, Clemson, SC 29634, USA
Interests: sensors; biosensors; medical imaging; nanoparticles; spectroscopy; microscopy; implanted medical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The past decade has witnessed the emergence of new X-ray molecular imaging modalities that uniquely combine the fine spatial resolution of X-ray imaging and tomography with the molecular sensitivity and specificity provided by X-ray excited optical luminescence and X-ray fluorescence. Among these techniques, X-ray luminescence imaging (XLI), X-ray luminescence computed tomography (XLCT), X-ray fluorescence computed tomography (XFCT), and radioluminescence imaging (RLI) have attracted great attention for their wide-ranging applications in imaging cancer, detecting infection, studying cellular microenvironments, monitoring response to therapy, etc.

This Special Issue will focus on state-of-the-art research in X-ray molecular imaging including XLI, XLCT, XFCT, or RLI, covering system improvements, imaging probes, reconstruction algorithms, and applications. Both original research papers and reviews are welcome.

The manuscripts should focus on, but are not limited to, the following topics:

  • XLI/XLCT instrumentation;
  • XFCT instrumentation;
  • Radioluminescence imaging (RLI);
  • Any approach to improving the performance of XLI/XLCT or XFCT or RLI;
  • Algorithms for XLI/XFCT or XFCT or RLI image reconstruction;
  • Probes for XLI/XLCT or XFCT or RLI imaging;
  • Applications of XLI/XLCT, XFCT, or RLI.

Dr. Changqing Li
Prof. Dr. Ge Wang
Prof. Dr. Jeffrey N. Anker
Guest Editors

Manuscript Submission Information

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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. Photonics is an international peer-reviewed open access monthly 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 1800 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.

Published Papers (6 papers)

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Research

Article
Fast and Inexpensive Separation of Bright Phosphor Particles from Commercial Sources by Gravitational and Centrifugal Sedimentation for Deep Tissue X-ray Luminescence Imaging
Photonics 2022, 9(5), 347; https://doi.org/10.3390/photonics9050347 - 15 May 2022
Cited by 1 | Viewed by 1068
Abstract
X-ray luminescence tomography (XLT) detects X-ray scintillators contrast agents using a focused or collimated X-ray beam to provide high spatial resolution excitation through thick tissue. The approach requires bright nanophosphors that are either synthesized or purchased. However, currently available commercial nanophosphors are mostly [...] Read more.
X-ray luminescence tomography (XLT) detects X-ray scintillators contrast agents using a focused or collimated X-ray beam to provide high spatial resolution excitation through thick tissue. The approach requires bright nanophosphors that are either synthesized or purchased. However, currently available commercial nanophosphors are mostly composed of a polydisperse mixture of several micro- to nano-sized particles that are unsuitable for biomedical imaging applications because of their size and aggregated form. Here, we demonstrate a fast and robust method to obtain uniform nano to submicron phosphor particles from a commercial source of polydisperse Eu- and Tb-doped Gd2O2S particles by separating the smaller particles present using gravitational and centrifugal sedimentation. In contrast to ball milling for 15–60 min, which drastically degraded the particles’ brightness while reducing their size, our sedimentation method enabled the extraction of comparatively bright nanophosphors (≈100–300 nm in size) with a luminescence intensity of ≈10–20% of the several micron particles in the sample. Moreover, if scale up for higher yielding is required, the sedimentation process can be accelerated using fixed-angle and/or swinging bucket rotating centrifugation. Finally, after separation and characterization, nano and submicron phosphors were suspended and imaged through 5 mm thick porcine tissue using our in-house-built scanning X-ray induced luminescence chemical imaging (XELCI) system. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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Article
ADMM-SVNet: An ADMM-Based Sparse-View CT Reconstruction Network
Photonics 2022, 9(3), 186; https://doi.org/10.3390/photonics9030186 - 14 Mar 2022
Viewed by 1203
Abstract
In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT. In the process of optimizing the iterative model, the approach of directly solving the quadratic penalty [...] Read more.
In clinical medical applications, sparse-view computed tomography (CT) imaging is an effective method for reducing radiation doses. The iterative reconstruction method is usually adopted for sparse-view CT. In the process of optimizing the iterative model, the approach of directly solving the quadratic penalty function of the objective function can be expected to perform poorly. Compared with the direct solution method, the alternating direction method of multipliers (ADMM) algorithm can avoid the ill-posed problem associated with the quadratic penalty function. However, the regularization items, sparsity transform, and parameters in the traditional ADMM iterative model need to be manually adjusted. In this paper, we propose a data-driven ADMM reconstruction method that can automatically optimize the above terms that are difficult to choose within an iterative framework. The main contribution of this paper is that a modified U-net represents the sparse transformation, and the prior information and related parameters are automatically trained by the network. Based on a comparison with other state-of-the-art reconstruction algorithms, the qualitative and quantitative results show the effectiveness of our method for sparse-view CT image reconstruction. The experimental results show that the proposed method performs well in streak artifact elimination and detail structure preservation. The proposed network can deal with a wide range of noise levels and has exceptional performance in low-dose reconstruction tasks. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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Article
Reduction of Compton Background Noise for X-ray Fluorescence Computed Tomography with Deep Learning
Photonics 2022, 9(2), 108; https://doi.org/10.3390/photonics9020108 - 14 Feb 2022
Viewed by 1316
Abstract
For bench-top X-ray fluorescence computed tomography (XFCT), the X-ray tube source will bring extreme Compton background noise, resulting in a low signal-to-noise ratio and low contrast detection limit. In this paper, a noise2noise denoising algorithm based on the UNet deep learning network is [...] Read more.
For bench-top X-ray fluorescence computed tomography (XFCT), the X-ray tube source will bring extreme Compton background noise, resulting in a low signal-to-noise ratio and low contrast detection limit. In this paper, a noise2noise denoising algorithm based on the UNet deep learning network is proposed. The network can use noise image learning to convert the noise image into a clean image. Two sets of phantoms (high concentration Gd phantom and low concentration Bi phantom) are used for scanning to simulate the imaging process under different noise levels and generate the required data set. Additionally, the data set is generated by Geant4 simulation. In the training process, the L1 loss function is used for its good convergence. The image quality is evaluated according to CNR and pixel profile, which shows that our algorithm is better than BM3D, both visually and quantitatively. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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Article
Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction
Photonics 2022, 9(1), 35; https://doi.org/10.3390/photonics9010035 - 08 Jan 2022
Viewed by 695
Abstract
Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based [...] Read more.
Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the L0-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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Article
Research on X-ray Fluorescence Enhanced Fluoroscopy Imaging Technology
Photonics 2021, 8(10), 441; https://doi.org/10.3390/photonics8100441 - 14 Oct 2021
Cited by 2 | Viewed by 1306
Abstract
Chest X-ray fluoroscopy is a commonly used medical imaging method, which has a wide range of applications in the diagnosis of lung diseases and other fields. However, due to low contrast and relatively close linear attenuation coefficients, some early small lesions are difficult [...] Read more.
Chest X-ray fluoroscopy is a commonly used medical imaging method, which has a wide range of applications in the diagnosis of lung diseases and other fields. However, due to low contrast and relatively close linear attenuation coefficients, some early small lesions are difficult to detect in time. Using the X-ray fluorescent effect of high atomic number metal elements and metal atom-containing agents that can be enriched in the lesion, the fluoroscopy signal and the fluorescent signal emitted by the metal atoms can be detected at the same time during the fluoroscopy, and the images of the two can be integrated, which can theoretically enhance the contrast between the lesion and the surrounding tissue. Based on GEANT4, this paper conducts Monte Carlo simulations to explore the feasibility and enhancement effects of three enhancement schemes: the pencil beam spot scanning method, cone-beam collimation method, and slit scanning method, and discusses the specific geometric structure and material selection. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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Article
X-ray Fluorescence Computed Tomography (XFCT) Imaging with a Superfine Pencil Beam X-ray Source
Photonics 2021, 8(7), 236; https://doi.org/10.3390/photonics8070236 - 25 Jun 2021
Cited by 3 | Viewed by 1481
Abstract
X-ray fluorescence computed tomography (XFCT) is a molecular imaging technique that can be used to sense different elements or nanoparticle (NP) agents inside deep samples or tissues. However, XFCT has not been a popular molecular imaging tool because it has limited molecular sensitivity [...] Read more.
X-ray fluorescence computed tomography (XFCT) is a molecular imaging technique that can be used to sense different elements or nanoparticle (NP) agents inside deep samples or tissues. However, XFCT has not been a popular molecular imaging tool because it has limited molecular sensitivity and spatial resolution. We present a benchtop XFCT imaging system in which a superfine pencil-beam X-ray source and a ring of X-ray spectrometers were simulated using GATE (Geant4 Application for Tomographic Emission) Monte Carlo software. An accelerated majorization minimization (MM) algorithm with an L1 regularization scheme was used to reconstruct the XFCT image of molybdenum (Mo) NP targets. Good target localization was achieved with a DICE coefficient of 88.737%. The reconstructed signal of the targets was found to be proportional to the target concentrations if detector number, detector placement, and angular projection number are optimized. The MM algorithm performance was compared with the maximum likelihood expectation maximization (ML-EM) and filtered back projection (FBP) algorithms. Our results indicate that the MM algorithm is superior to the ML-EM and FBP algorithms. We found that the MM algorithm was able to reconstruct XFCT targets as small as 0.25 mm in diameter. We also found that measurements with three angular projections and a 20-detector ring are enough to reconstruct the XFCT images. Full article
(This article belongs to the Special Issue X-ray Luminescence and Fluorescence)
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