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Bioengineering
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Recent Advances in Biomedical Imaging: 2nd Edition
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Recent Advances in Biomedical Imaging: 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 8430

Special Issue Editor

Prof. Dr. Cuneyt M. Alper

E-Mail Website
Guest Editor
1. Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
2. Department of Pediatric Otolaryngology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
Interests: imaging; biomedical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging has arguably demonstrated the most rapid advancements in the entire biomedical field in the past decade. Besides the expansion of established imaging instrumentation into broader applications in tissue, cellular, and molecular diagnostic imaging, there have been substantial modifications in the imaging protocols that have advanced the capabilities of these existing imaging modalities. Technological advancements are stimulating further novel approaches in diagnosis and measuring as well as monitoring the outcomes of treatments. The adaptation of innovations in imaging technologies, methods, and protocols for broader applications is often limited by the inability to share an innovation with investigators outside the likely narrow field in which it originated. Therefore, it is crucial to facilitate the sharing of such advances in biomedical imaging occurring in one field with other fields. A broader vision with which to explore the full potential of an innovation often requires adding a new, perhaps outside, perspective. This Special Issue of Bioengineering aims to serve as a medium for such interdisciplinary exchange and the stimulation of the expansion of applications of innovations, perhaps by facilitating new collaborations between various fields and investigators. The next big breakthrough in biomedical imaging may come from diverse areas of expertise coming together and finding new ways forward.

This is the second volume of our Special Issue, "Recent Advances in Biomedical Imaging". Please feel free to download and read it freely via the following link:
https://www.mdpi.com/journal/bioengineering/special_issues/24F01PJ541

Dr. Cuneyt M. Alper
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.

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Published Papers (5 papers)

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Research

16 pages, 8610 KiB  
Article
Characterization of Normal and Degenerative Discovertebral Complexes Using Qualitative and Quantitative Magnetic Resonance Imaging at 4.7T: Longitudinal Evaluation of Immature and Mature Rats
by Benjamin Dallaudière, Emeline J. Ribot, Aurélien J. Trotier, Laurence Dallet, Olivier Thibaudeau, Sylvain Miraux and Olivier Hauger
Bioengineering 2025, 12(2), 141; https://doi.org/10.3390/bioengineering12020141 - 31 Jan 2025
Viewed by 333
Abstract
Purpose: We assessed the feasibility of qualitative, semiquantitative, and multiparametric quantitative magnetic resonance imaging (MRI) using a three-dimensional (3D) ultrashort echo time (3D-UTE) sequence together with 2D-T2 and 3D-T1 mapping sequences to evaluate normal and pathological discovertebral complexes (DVCs). We assessed the disc [...] Read more.
Purpose: We assessed the feasibility of qualitative, semiquantitative, and multiparametric quantitative magnetic resonance imaging (MRI) using a three-dimensional (3D) ultrashort echo time (3D-UTE) sequence together with 2D-T2 and 3D-T1 mapping sequences to evaluate normal and pathological discovertebral complexes (DVCs). We assessed the disc (nucleus pulposus [NP] and annulus fibrosus [AF]), vertebral endplate (cartilage endplate [CEP] and growth plate [GP]), and subchondral bone (SB) using a rat model of degenerative disc disease (DDD). We also assessed whether this complete MRI cartography can improve the monitoring of DDD. Methods: DDD was induced by percutaneous disc trituration and collagenase injection of the tail. Then, the animals were imaged at 4.7T. The adjacent disc served as the control. The MRI protocol was performed at baseline and each week (W) postoperatively for 2 weeks. Visual analysis and signal intensity measurements from the 3D-UTE images, as well as T2 and T1 measurements, were carried out in all DVC portions. Histological analysis with hematoxylin–eosin and Masson trichrome staining was performed following euthanization of the rats at 2 weeks and the results were compared to the MRI findings. Results: Complete qualitative identification of the normal zonal anatomy of the DVC, including the AF, CEP, and GP, was achieved using the 3D-UTE sequence. Quantitative measurements of the signal-to-noise ratio in the AF and NP enabled healthy DVCs to be distinguished from surgery-induced DDD, based on an increase in these values post-surgery. The 2D-T2 mapping results showed a significant increase in the T2 values of the AF and a decrease in the values of the NP between the baseline and W1 and W2 postoperatively (p < 0.001). In the 3D-T1 mapping, there was a significant decrease in the T1 values of the AF and NP between baseline and W1 and W2 postoperatively in immature rats (p < 0.01). This variation in T1 and T2 over time was consistent with the results of the 3D-UTE sequence. Conclusions: Use of the 3D-UTE sequence enabled a complete, robust, and reproducible visualization of DVC anatomy in both immature and mature rats under both normal and pathological conditions. The findings were supported quantitatively by the T2 and T1 mapping sequences and histologically. This sequence is therefore of prime interest in spinal imaging and should be regularly be performed. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)
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15 pages, 1570 KiB  
Article
Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects
by Carter Woods, Xin Xing, Subash Khanal and Ai-Ling Lin
Bioengineering 2024, 11(9), 943; https://doi.org/10.3390/bioengineering11090943 - 20 Sep 2024
Viewed by 2009
Abstract
Background: Alzheimer’s disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the [...] Read more.
Background: Alzheimer’s disease (AD) is a leading cause of dementia, and it is significantly influenced by the apolipoprotein E4 (APOE4) gene and gender. This study aimed to use machine learning (ML) algorithms to predict brain age and assess AD risk by considering the effects of the APOE4 genotype and gender. Methods: We collected brain volumetric MRI data and medical records from 1100 cognitively unimpaired individuals and 602 patients with AD. We applied three ML regression models—XGBoost, random forest (RF), and linear regression (LR)—to predict brain age. Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models’ performances and analyze the influences of the APOE4 genotype and gender on brain aging. Results: Patients with AD displayed significantly older brain ages compared to their chronological ages, with BADs ranging from 6.5 to 10 years. The RF model outperformed both XGBoost and LR in terms of accuracy, delivering higher ID values and more precise predictions. Comparing the APOE4 carriers with noncarriers, the models showed enhanced ID values and consistent brain age predictions, improving the overall performance. Gender-specific analyses indicated slight enhancements, with the models performing equally well for both genders. Conclusions: This study demonstrates that robust ML models for brain age prediction can play a crucial role in the early detection of AD risk through MRI brain structural imaging. The significant impact of the APOE4 genotype on brain aging and AD risk is also emphasized. These findings highlight the potential of ML models in assessing AD risk and suggest that utilizing AI for AD identification could enable earlier preventative interventions. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)
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17 pages, 5173 KiB  
Article
Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources
by Xi Yang, Chengpeng Chai, Hongzhi Zuo, Yun-Hsuan Chen, Junhui Shi, Cheng Ma and Mohamad Sawan
Bioengineering 2024, 11(3), 260; https://doi.org/10.3390/bioengineering11030260 - 7 Mar 2024
Cited by 5 | Viewed by 2116
Abstract
Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. [...] Read more.
Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)
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13 pages, 2773 KiB  
Article
Development and Utility of an Imaging System for Internal Dosimetry of Astatine-211 in Mice
by Atsushi Yagishita, Miho Katsuragawa, Shin’ichiro Takeda, Yoshifumi Shirakami, Kazuhiro Ooe, Atsushi Toyoshima, Tadayuki Takahashi and Tadashi Watabe
Bioengineering 2024, 11(1), 25; https://doi.org/10.3390/bioengineering11010025 - 26 Dec 2023
Cited by 1 | Viewed by 1743
Abstract
In targeted radionuclide therapy, determining the absorbed dose of the ligand distributed to the whole body is vital due to its direct influence on therapeutic and adverse effects. However, many targeted alpha therapy drugs present challenges for in vivo quantitative imaging. To address [...] Read more.
In targeted radionuclide therapy, determining the absorbed dose of the ligand distributed to the whole body is vital due to its direct influence on therapeutic and adverse effects. However, many targeted alpha therapy drugs present challenges for in vivo quantitative imaging. To address this issue, we developed a planar imaging system equipped with a cadmium telluride semiconductor detector that offers high energy resolution. This system also comprised a 3D-printed tungsten collimator optimized for high sensitivity to astatine-211, an alpha-emitting radionuclide, and adequate spatial resolution for mouse imaging. The imager revealed a spectrum with a distinct peak for X-rays from astatine-211 owing to the high energy resolution, clearly distinguishing these X-rays from the fluorescent X-rays of tungsten. High collimator efficiency (4.5 × 10−4) was achieved, with the maintenance of the spatial resolution required for discerning mouse tissues. Using this system, the activity of astatine-211 in thyroid cancer tumors with and without the expression of the sodium iodide symporter (K1-NIS/K1, respectively) was evaluated through in vivo imaging. The K1-NIS tumors had significantly higher astatine-211 activity (sign test, p = 0.031, n = 6) and significantly decreased post-treatment tumor volume (Student’s t-test, p = 0.005, n = 6). The concurrent examination of intratumor drug distribution and treatment outcome could be performed with the same mice. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)
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15 pages, 6318 KiB  
Article
Automated Segmentation and Measurements of Pulmonary Cysts in Lymphangioleiomyomatosis across Multiple CT Scanner Platforms over a Period of Two Decades
by Simone Lee, Alfredo Lebron, Brianna Matthew, Joel Moss and Han Wen
Bioengineering 2023, 10(11), 1255; https://doi.org/10.3390/bioengineering10111255 - 27 Oct 2023
Cited by 1 | Viewed by 1516
Abstract
(1) Background: Lymphangioleiomyomatosis is a genetic disease that affects mostly women of childbearing age. In the lungs, it manifests as the progressive formation of air-filled cysts and is associated with a decline in lung function. With a median survival of 29 years after [...] Read more.
(1) Background: Lymphangioleiomyomatosis is a genetic disease that affects mostly women of childbearing age. In the lungs, it manifests as the progressive formation of air-filled cysts and is associated with a decline in lung function. With a median survival of 29 years after the onset of symptoms, computed-tomographic monitoring of cystic changes in the lungs is a key part of the management of the disease. However, the current standard method to measure cyst burdens from CT is semi-automatic and requires manual adjustments from trained operators to obtain consistent results due to variabilities in CT technology and imaging conditions over the long course of the disease. This can be impractical for longitudinal studies involving large numbers of scans and is susceptible to subjective biases. (2) Methods: We developed an automated method of pulmonary cyst segmentation for chest CT images incorporating novel graphics processing algorithms. We assessed its performance against the gold-standard semi-automated method performed by experienced operators who were blinded to the results of the automated method. (3) Results: the automated method had the same consistency over time as the gold-standard method, but its cyst scores were more strongly correlated with concurrent pulmonary function results from the physiology laboratory than those of the gold-standard method. (4) Conclusions: The automated cyst segmentation is a competent replacement for the gold-standard semi-automated process. It is a solution for saving time and labor in clinical studies of lymphangioleiomyomatosis that may involve large numbers of chest CT scans from diverse scanner platforms and protocols. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging: 2nd Edition)
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