Radiological Imaging and Its Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 730

Special Issue Editor


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Guest Editor
Department of Radiological Science, Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea
Interests: medical imaging; image processing; photon-counting detector technology; medical physics
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Special Issue Information

Dear Colleagues,

Radiological imaging plays a pivotal role in modern healthcare, enabling the non-invasive examination and diagnosis of various medical conditions. It encompasses a wide range of imaging techniques, including X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. The versatility and advancements in radiological imaging have revolutionized medical practices, serving as an indispensable tool for accurate disease detection, treatment planning, and monitoring patient responses.

This Special Issue aims to delve into the realm of radiological imaging and explore its vast applications across multiple disciplines. This Special Issue welcomes original research articles, reviews, and communications that focus on novel imaging techniques, advances in image analysis, and emerging applications in both clinical and preclinical settings. Furthermore, contributions addressing radiological imaging with a particular emphasis on artificial intelligence, deep learning, and computer-aided diagnosis are particularly welcome.

Topics of interest include, but are not limited to, the following:

  1. State-of-the-art imaging modalities and techniques
  2. Image reconstruction, enhancement, and denoising algorithms
  3. Quantitative imaging and radiomics
  4. Image-guided interventions and theranostics
  5. Innovative applications of radiological imaging in various diseases and medical conditions
  6. Challenges and future directions in radiological imaging research
  7. Sensor technology
  8. X-ray detector technology
  9. Measuring system and signal processing
  10. Radiation detector, system design, and applications
  11. Innovative applications of radiological imaging in various diseases and medical conditions
  12. Application of machine learning

By consolidating the latest advancements and innovative applications in radiological imaging, this Special Issue aspires to stimulate interdisciplinary collaborations, foster knowledge sharing, and provide a platform for researchers to showcase their cutting-edge findings in the field.

Prof. Dr. Youngjin Lee
Guest Editor

Manuscript Submission Information

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Keywords

  • radiological imaging
  • medical imaging
  • computed tomography (CT)
  • magnetic resonance imaging (MRI)
  • positron emission tomography (PET)
  • single-photon emission computed tomography (SPECT)
  • ultrasonography
  • radiomics
  • artificial intelligence
  • nuclear medicine
  • interventional radiology

Published Papers (1 paper)

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Research

15 pages, 20041 KiB  
Article
Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain
by Kyuseok Kim, Bo Kyung Cha, Hyun-Woo Jeong and Youngjin Lee
Bioengineering 2024, 11(4), 330; https://doi.org/10.3390/bioengineering11040330 - 28 Mar 2024
Viewed by 499
Abstract
In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. [...] Read more.
In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. The proposed method was used to predict the optimal point spread function (PSF) between thin and thick scintillation detectors by considering image quality assessment (IQA). The process of identifying the optimal PSF was performed on each sub-band in the wavelet domain to improve restoration accuracy. In the experiments, the edge preservation index (EPI) values of the non-blind deblurred image with a blurring sigma of σ = 5.13 pixels and the image obtained with optimal parameters from the thick scintillator using the proposed method were approximately 0.62 and 0.76, respectively. The coefficient of variation (COV) values for the two images were approximately 1.02 and 0.63, respectively. The proposed method was validated through simulations and experimental results, and its viability is expected to be verified on various radiological imaging systems. Full article
(This article belongs to the Special Issue Radiological Imaging and Its Applications)
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