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Bioengineering
Special Issues
Radiological Imaging and Its Applications
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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: closed (30 September 2024) | Viewed by 5015

Special Issue Editor

Prof. Dr. Youngjin Lee

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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. Bioengineering 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 2700 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

  • 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

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

Published Papers (3 papers)

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14 pages, 3781 KiB  
Article
The Diagnostic Value of bpMRI in Prostate Cancer: Benefits and Limitations Compared to mpMRI
by Roxana Iacob, Diana Manolescu, Emil Robert Stoicescu, Simona Cerbu, Răzvan Bardan, Laura Andreea Ghenciu and Alin Cumpănaș
Bioengineering 2024, 11(10), 1006; https://doi.org/10.3390/bioengineering11101006 - 9 Oct 2024
Cited by 1 | Viewed by 1487
Abstract
Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and [...] Read more.
Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and mpMRI are essential for prostate cancer management. The PI-RADS system helps assess malignancy risk based on imaging. While mpMRI, which includes T1, T2, DWI, and dynamic contrast-enhanced imaging (DCE), is the standard, bpMRI offers a contrast-free alternative using only T2 and DWI. This reduces costs, acquisition time, and the risk of contrast-related side effects but has limitations in detecting higher-risk PI-RADS 3 and 4 lesions. This study compared bpMRI’s diagnostic accuracy to mpMRI, focusing on prostate volume and PI-RADS scoring. Both methods showed strong inter-rater agreement for prostate volume (ICC 0.9963), confirming bpMRI’s reliability in this aspect. However, mpMRI detected more complex conditions, such as periprostatic fat infiltration and iliac lymphadenopathy, which bpMRI missed. While bpMRI offers advantages like reduced cost and no contrast use, it is less effective for higher-risk lesions, making mpMRI more comprehensive. Full article
(This article belongs to the Special Issue Radiological Imaging and Its Applications)
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11 pages, 11945 KiB  
Article
Evaluation of Denoising Performance of ResNet Deep Learning Model for Ultrasound Images Corresponding to Two Frequency Parameters
by Hyekyoung Kang, Chanrok Park and Hyungjin Yang
Bioengineering 2024, 11(7), 723; https://doi.org/10.3390/bioengineering11070723 - 16 Jul 2024
Cited by 3 | Viewed by 1485
Abstract
Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are [...] Read more.
Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are typically used in the spatial domain. Recently, deep learning models have been increasingly applied in the field of medical imaging. In this study, we evaluated the effectiveness of a convolutional neural network-based residual network (ResNet) deep learning model for noise reduction when Gaussian and speckle noises were present. We compared the results with those obtained from conventional filtering techniques. A dataset of 500 images was prepared, and Gaussian and speckle noises were added to create noisy input images. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The ResNet deep learning model, comprising 16 residual blocks, was trained using optimized hyperparameters, including the learning rate, optimization function, and loss function. For quantitative analysis, we calculated the normalized noise power spectrum, peak signal-to-noise ratio, and root mean square error. Our findings showed that the ResNet deep learning model exhibited superior noise reduction performance to median, Wiener, and median-modified Wiener filter algorithms. Full article
(This article belongs to the Special Issue Radiological Imaging and Its Applications)
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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
Cited by 1 | Viewed by 1370
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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