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Electronics
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Advances in Biomedical Imaging and Processing
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Advances in Biomedical Imaging and Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 3025

Special Issue Editors

Dr. Byullee Park

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Guest Editor
1. Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology (Caltech), Pasadena, CA 91125, USA
2. Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
Interests: biomedical imaging system; photoacoustic imaging; optical imaging; ultrasound imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Cao

E-Mail Website
Guest Editor
Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology (Caltech), Pasadena, CA 91125, USA
Interests: biomedical imaging system; photoacoustic imaging; optical imaging; ultrasound imaging
Dr. Wonseok Choi

E-Mail Website
Guest Editor
1. Department of Radiology, Stanford University School of Medicine, Palo Alto, CA 94304, USA
2. Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
Interests: biomedical imaging system; photoacoustic imaging; ultrasound imaging; image processing

Special Issue Information

Dear Colleagues,

Biomedical imaging plays a key role in clinical diagnosis and treatment guiding/evaluation through the complete visualization from molecular scale to cell, tissue, organ, lesion, and whole organism scale. Moreover, in vivo imaging of animal models has served as a pivotal translational link between basic biomedical research and clinical applications for human diseases.

Over the past few decades, engineering science, like electronic and computer science, has been a driving force in advancing the biomedical imaging field via development/improvement/ miniaturization of systems, improvement of sensitivity and spatiotemporal resolution, acceleration of computational analysis, and development of methods that minimize the adverse effects of applied energies. In particular, the recent rapid development of artificial intelligence technology in computer science has been an opportunity to actively apply automated medical image analysis to the medical field.

The purpose of this Special Issue is to publish cutting-edge research contributions (original and review articles) covering advances in biomedical imaging. This Special Issue will contribute to improving diagnostic accuracy and maximizing treatment efficiency through biomedical imaging.

This scope includes advances in biomedical imaging systems (i.e., clinical or preclinical imaging modalities, tomographic or microscopic imaging systems) and associated signal or image processing (e.g., signal/image quality improvement, image reconstruction, computer-aided analysis). Furthermore, we welcome studies related to biomedical imaging using contrast agents and various preclinical/clinical applications. 

Dr. Byullee Park
Dr. Rui Cao
Dr. Wonseok Choi
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 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. Electronics 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 2400 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

  • biomedical imaging
  • microscopy
  • tomography
  • image processing
  • contrast imaging
  • clinical application

Published Papers (2 papers)

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15 pages, 2449 KiB  
Article
Sinogram Upsampling via Sub-Riemannian Diffusion with Adaptive Weighting
by JaKeoung Koo
Electronics 2023, 12(21), 4503; https://doi.org/10.3390/electronics12214503 - 01 Nov 2023
Viewed by 848
Abstract
Computed tomography is a versatile imaging technique used to enable seeing internal structures of objects without opening or destroying them. This is possible through a process called tomographic reconstruction, which reconstructs images from projections of the object that are obtained by penetrating the [...] Read more.
Computed tomography is a versatile imaging technique used to enable seeing internal structures of objects without opening or destroying them. This is possible through a process called tomographic reconstruction, which reconstructs images from projections of the object that are obtained by penetrating the object with beams of radiation, such as X-rays, from different angles. These projection data are often limited to low-resolution data in terms of projection angles. These limited or subsampled data make it difficult to obtain high-quality reconstruction results. Hence, upsampling projection data is necessary. In this paper, we propose a sinogram upsampling method via the sub-Riemannian diffusion process. We first lift the data into a feature space, and we fill in the missing angle parts by propagating information from the observed data to the missing parts. We observe that the sinogram with limited angle data has high directional dependency, and based on this observation, we suggest an adaptive weighting scheme to keep information propagating toward the missing regions. This adaptive weighting allows for diffusing toward the desired directions. The experimental results show the effectiveness of the proposed method in some scenarios regarding inpainting fine details, when compared to the existing model-based methods, such as Plug-and-Play and total generalized variation. Full article
(This article belongs to the Special Issue Advances in Biomedical Imaging and Processing)
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16 pages, 3337 KiB  
Article
Optimization of the Algorithm for the Implementation of Point Spread Function in the 3D-OSEM Reconstruction Algorithm Based on the List-Mode Micro PET Data
by Jie Zhao, Yunfeng Song, Qiong Liu, Shijie Chen and Jyh-Cheng Chen
Electronics 2023, 12(6), 1309; https://doi.org/10.3390/electronics12061309 - 09 Mar 2023
Viewed by 1304
Abstract
Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of [...] Read more.
Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of PET images to degrade. It is possible to reduce this PVE and achieve better image quality by measuring and modeling the point spread function (PSF) and then accounting for it inside the reconstruction algorithm. In this work, we examined the response characteristics of the MetisTM PET/CT system by acquiring 22Na point source at different locations in the field of view (FOV) of the scanner and reconstructing with small pixel size for images to obtain their radial, tangential, and axial full-width half maximum (FWHM). An image-based model of the PSF model was then obtained by fitting asymmetric two-dimensional Gaussians on the 22Na images. This PSF model determined by FWHM in three directions was integrated into a three-dimensional ordered subsets expectation maximization (3D-OSEM) algorithm based on a list-mode format to form a new PSF-OSEM algorithm. We used both algorithms to reconstruct point source, Derenzo phantom, and mouse PET images and performed qualitative and quantitative analyses. In the point source study, the PSF-OSEM algorithm reduced the FWHM of the point source PET image in three directions to about 0.67 mm, and in the phantom study, the PET image reconstructed by the PSF-OSEM algorithm had better visual effects. At the same time, the quantitative analysis results of the Derenzo phantom were better than the original 3D-OSEM algorithm. In the mouse experiment, the results of qualitative and quantitative analyses showed that the imaging quality of PSF-OSEM algorithm was better than that of 3D-OSEM algorithm. Our results show that adding the PSF model to the 3D-OSEM algorithm in the MetisTM PET/CT system helps to improve the resolution of the image and satisfy the qualitative and quantitative analysis criteria. Full article
(This article belongs to the Special Issue Advances in Biomedical Imaging and Processing)
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