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Biomedical Imaging: From Methods to Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 14406

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Special Issue Editors

Prof. Dr. Joaquin Lopez Herraiz

E-Mail Website
Guest Editor

Faculty of Physical Sciences, University Complutense of Madrid, CEI Moncloa, 28040 Madrid, Spain
Interests: image reconstruction; PET; CT; US; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Gonzalo Vegas Sánchez-Ferrero

E-Mail Website
Guest Editor

Brigham and women's hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: image reconstruction; CT; MRI; image harmonization; quantitative imaging

Special Issue Information

Dear Colleagues,

Biomedical imaging covers all the processes involved in the acquisition, processing, visualization, and analysis of structural or functional images of living specimens or systems. It includes both biological and medical applications. Examples of biomedical image modalities include X-ray, CT, MRI and fMRI, PET, SPECT, MEG, microscope/fluorescence imaging, and photoacoustic, among others. It is an exciting topic which is continuously evolving.

The purpose of this Special Issue is to provide an overview of the new developments in Biomedical Imaging. Potential topics include, but are not limited to, new biomedical image modalities, multimodality imaging, new developments in image processing methods such as machine learning and deep learning techniques, as well as new algorithms and computational methods applied to biomedical imaging.

New methodological approaches, as well as in vitro, in silico or in vivo studies, that challenge current thinking in biomedical imaging research are warmly welcomed topics. Review studies, including those that use conceptual frameworks for any of the aforementioned topics, will also be welcomed.

Prof. Dr. Joaquin Lopez Herraiz
Dr. Gonzalo Vegas Sánchez-Ferrero
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. Applied Sciences 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 Image Denoising
Biomedical Image Reconstruction
Biomedical Image Segmentation
Biomedical Registration Methods
Feature Recognition
Biomedical Image Classification
Machine Learning
Deep Learning (DL)
Neural Network (NN)
Convolutional Neural Network (CNN)
Kinetic Modeling
Super-Resolution
X-ray Imaging
Computer Tomography (CT)
Magnetic Resonance Imaging (MRI and fMRI)
Positron Emission Tomography (PET)
Single-Photon Emission Computer Tomography
Magnetoencephalography (MEG)
Fluorescence Imaging
Photoacoustic Imaging
Quantitative Imaging

Published Papers (7 papers)

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Research

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8 pages, 3744 KiB

Open AccessCommunication

Development and Evaluation of Deep Learning-Based Reconstruction Using Preclinical 7T Magnetic Resonance Imaging

by Naoki Tsuji, Takuma Kobayashi, Junpei Ueda and Shigeyoshi Saito

Appl. Sci. 2023, 13(11), 6567; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116567 - 29 May 2023

Viewed by 1206

Abstract

This study investigated a method for improving the quality of images with a low number of excitations (NEXs) based on deep learning using T₂-weighted magnetic resonance imaging (MRI) of the heads of normal Wistar rats to achieve higher image quality and a shorter acquisition time. A 7T MRI was used to acquire T₂-weighted images of the whole brain with NEXs = 2, 4, 8, and 12. As a preprocessing step, non-rigid registration of the acquired low NEX images (NEXs = 2, 4, 8) and NEXs = 12 images was performed. A residual dense network (RDN) was used for training. A low NEX image was used as the input to the RDN, and the NEX12 image was used as the correct image. For quantitative evaluation, we measured the signal-to-noise ratio (SNR), peak SNR, and structural similarity index measure of the original image and the image obtained by RDN. The NEX2 results are presented as an example. The SNR of the cortex was 10.4 for NEX2, whereas the SNR of the image reconstructed with RDN for NEX2 was 32.1. (The SNR NEX12 was 19.6) In addition, the PSNR in NEX2 was significantly increased to 35.4 ± 2.0 compared to the input image and to 37.6 ± 2.9 compared to the reconstructed image (p = 0.05). The SSIM in NEX2 was 0.78 ± 0.05 compared to the input image and 0.91 ± 0.05 compared to the reconstructed image (p = 0.0003). Furthermore, NEX2 succeeded in reducing the shooting time by 83%. Therefore, in preclinical 7T MRI, supervised learning between the NEXs using RDNs can potentially improve the image quality of low NEX images and shorten the acquisition time. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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11 pages, 1846 KiB

Open AccessFeature PaperArticle

Radiological Features of B3 Lesions in Mutation Carrier Patients: A Single-Center Retrospective Analysis

by Claudia Lucia Piccolo, Carlo Augusto Mallio, Laura Messina, Manuela Tommasiello, Paolo Orsaria, Vittorio Altomare, Matteo Sammarra and Bruno Beomonte Zobel

Appl. Sci. 2023, 13(8), 4994; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084994 - 16 Apr 2023

Cited by 2 | Viewed by 1071

Abstract

Background. To evaluate the radiological features of B3 lesions in patients with genetic mutations to establish an anatomo-radiological correlation. Methods. A total of 227 women with a histological diagnosis of B3 breast lesion were enrolled. Breast images of 21 patients with genetic test positivity for mutations in genes associated with breast cancer were analyzed. Results. BRCA1 was the most frequent mutation (n = 12) followed by ATM (n = 6) and BRCA2 (n = 3). The histological findings showed nine atypical ductal hyperplasia (ADH), six lobular neoplasia (LN) including lobular carcinoma in situ (LCIS), three flat epithelial atypia (FEA) and three radial scar (RS) lesions. The results showed a significance difference between B3 lesion distribution in the three subgroups of mutations. LN and FEA showed the highest malignancy correlation. Patient age and risk anamnesis were factors that significantly influenced the malignancy rate. By mammography, 90.5% of lesions appeared as microcalcifications. By ultrasound, 13 lesions were observed as hypoechoic lesions. On breast MRI, 16 lesions were detected as a mass enhancement in all groups. DWI and kinetic curves significantly correlated with the risk of cancer. Conclusions. The radiological features of B3 lesions may help in the diagnosis of breast cancer malignancy. The high malignancy rate of cancer in our sample suggests they should always be surgically excised. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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20 pages, 4642 KiB

Open AccessArticle

Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning

by Dimple Nagpal, Najah Alsubaie, Ben Othman Soufiene, Mohammed S. Alqahtani, Mohamed Abbas and Hussain M. Almohiy

Appl. Sci. 2023, 13(8), 4695; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084695 - 07 Apr 2023

Cited by 5 | Viewed by 2109

Abstract

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes. It occurs when high blood sugar levels damage the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Therefore, there is a need to detect DR in the early stages to reduce the risk of blindness. Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task. Transfer learning has been applied to diabetic retinopathy classification with promising results. Pre-trained models, such as convolutional neural networks (CNNs), can be fine-tuned on a new dataset of retinal images to classify diabetic retinopathy. This manuscript aims at developing an automated scheme for diagnosing and grading DR and HR. The retinal image classification has been performed using three phases that include preprocessing, segmentation and feature extraction techniques. The pre-processing methodology has been proposed for reducing the noise in retinal images. A-CLAHE, DNCNN and Wiener filter techniques have been applied for the enhancement of images. After pre-processing, blood vessel segmentation in retinal images has been performed utilizing OTSU thresholding and mathematical morphology. Feature extraction and classification have been performed using transfer learning models. The segmented images were then classified using Modified ResNet 101 architecture. The performance for enhanced images has been evaluated on PSNR and shows better results as compared to the existing literature. The network is trained on more than 6000 images from MESSIDOR and ODIR datasets and achieves the classification accuracy of 98.72%. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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23 pages, 2876 KiB

Open AccessArticle

Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study

by Bolin Pan, Paul K. Marsden and Andrew J. Reader

Appl. Sci. 2023, 13(7), 4089; https://0-doi-org.brum.beds.ac.uk/10.3390/app13074089 - 23 Mar 2023

Cited by 5 | Viewed by 1951

Abstract

Multiplexed positron emission tomography (PET) imaging provides perfectly registered simultaneous functional and molecular imaging of more than one biomarker. However, the separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that all PET tracers emit positrons, which, after annihilating with a nearby electron, give rise to 511 keV photon pairs that are detected in coincidence. Compartment modelling can separate single-tracer PET signals from multiplexed signals based on the differences in bio-distribution kinetics and radioactive decay. However, the compartment-modelling-based method requires staggered injections and assumes that each tracer’s input function is known. In this paper, we propose a deep-learning-based method to simultaneously separate dual-tracer PET signals without explicitly knowing the input functions. We evaluate the proposed deep-learning-based separation method on dual-tracer [

^{18}

F]FDG and [

^{11} C

]MET PET simulations and compare its separation performance to that of the compartment-modelling-based method, assessing performance dependence on the time interval between tracer injections as well as on the amount of training data. It is shown that the proposed method implicitly denoises the separated images and offers reduced variance in the separated images compared to compartment modelling. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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12 pages, 12620 KiB

Open AccessArticle

Inter-Rater Variability in the Evaluation of Lung Ultrasound in Videos Acquired from COVID-19 Patients

by Joaquin L. Herraiz, Clara Freijo, Jorge Camacho, Mario Muñoz, Ricardo González, Rafael Alonso-Roca, Jorge Álvarez-Troncoso, Luis Matías Beltrán-Romero, Máximo Bernabeu-Wittel, Rafael Blancas, Antonio Calvo-Cebrián, Ricardo Campo-Linares, Jaldún Chehayeb-Morán, Jose Chorda-Ribelles, Samuel García-Rubio, Gonzalo García-de-Casasola, Adriana Gil-Rodrigo, César Henríquez-Camacho, Alba Hernandez-Píriz, Carlos Hernandez-Quiles, Rafael Llamas-Fuentes, Davide Luordo, Raquel Marín-Baselga, María Cristina Martínez-Díaz, María Mateos-González, Manuel Mendez-Bailon, Francisco Miralles-Aguiar, Ramón Nogue, Marta Nogué, Borja Ortiz de Urbina-Antia, Alberto Ángel Oviedo-García, José M. Porcel, Santiago Rodriguez, Diego Aníbal Rodríguez-Serrano, Talía Sainz, Ignacio Manuel Sánchez-Barrancos, Marta Torres-Arrese, Juan Torres-Macho, Angela Trueba Vicente, Tomas Villén-Villegas, Juan José Zafra-Sánchez and Yale Tung-Chen Show full author list Hide full author list

Appl. Sci. 2023, 13(3), 1321; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031321 - 18 Jan 2023

Cited by 6 | Viewed by 2909

Abstract

Lung ultrasound (LUS) allows for the detection of a series of manifestations of COVID-19, such as B-lines and consolidations. The objective of this work was to study the inter-rater reliability (IRR) when detecting signs associated with COVID-19 in the LUS, as well as the performance of the test in a longitudinal or transverse orientation. Thirty-three physicians with advanced experience in LUS independently evaluated ultrasound videos previously acquired using the ULTRACOV system on 20 patients with confirmed COVID-19. For each patient, 24 videos of 3 s were acquired (using 12 positions with the probe in longitudinal and transverse orientations). The physicians had no information about the patients or other previous evaluations. The score assigned to each acquisition followed the convention applied in previous studies. A substantial IRR was found in the cases of normal LUS (κ = 0.74), with only a fair IRR for the presence of individual B-lines (κ = 0.36) and for confluent B-lines occupying < 50% (κ = 0.26) and a moderate IRR in consolidations and B-lines > 50% (κ = 0.50). No statistically significant differences between the longitudinal and transverse scans were found. The IRR for LUS of COVID-19 patients may benefit from more standardized clinical protocols. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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21 pages, 5729 KiB

Open AccessArticle

A Deep Learning Approach to Upscaling “Low-Quality” MR Images: An In Silico Comparison Study Based on the UNet Framework

by Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G. Webb, Ioannis Seimenis, Constantinos Loukas, Ernst Leiss and Nikolaos V. Tsekos

Appl. Sci. 2022, 12(22), 11758; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211758 - 19 Nov 2022

Cited by 4 | Viewed by 2310

Abstract

MR scans of low-gamma X-nuclei, low-concentration metabolites, or standard imaging at very low field entail a challenging tradeoff between resolution, signal-to-noise, and acquisition duration. Deep learning (DL) techniques, such as UNets, can potentially be used to improve such “low-quality” (LQ) images. We investigate three UNets for upscaling LQ MRI: dense (DUNet), robust (RUNet), and anisotropic (AUNet). These were evaluated for two acquisition scenarios. In the same-subject High-Quality Complementary Priors (HQCP) scenario, an LQ and a high quality (HQ) image are collected and both LQ and HQ were inputs to the UNets. In the No Complementary Priors (NoCP) scenario, only the LQ images are collected and used as the sole input to the UNets. To address the lack of same-subject LQ and HQ images, we added data from the OASIS-1 database. The UNets were tested in upscaling 1/8, 1/4, and 1/2 undersampled images for both scenarios. As manifested by non-statically significant differences of matrices, also supported by subjective observation, the three UNets upscaled images equally well. This was in contrast to mixed effects statistics that clearly illustrated significant differences. Observations suggest that the detailed architecture of these UNets may not play a critical role. As expected, HQCP substantially improves upscaling with any of the UNets. The outcomes support the notion that DL methods may have merit as an integral part of integrated holistic approaches in advancing special MRI acquisitions; however, primary attention should be paid to the foundational step of such approaches, i.e., the actual data collected. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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Review

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22 pages, 11368 KiB

Open AccessReview

Magnetic Resonance Roadmap in Detecting and Staging Endometriosis: Usual and Unusual Localizations

by Claudia Lucia Piccolo, Laura Cea, Martina Sbarra, Anna Maria De Nicola, Carlo De Cicco Nardone, Eliodoro Faiella, Rosario Francesco Grasso and Bruno Beomonte Zobel

Appl. Sci. 2023, 13(18), 10509; https://0-doi-org.brum.beds.ac.uk/10.3390/app131810509 - 21 Sep 2023

Viewed by 1316

Abstract

Endometriosis is a chronic condition characterized by the presence of abnormal endometrial tissue outside the uterus. These misplaced cells are responsible for inflammation, symptoms, scar tissue and adhesions. Endometriosis manifests mainly in three patterns: superficial peritoneal lesions (SUP), ovarian endometriomas (OMA) and deep infiltrating endometriosis (DIE). It also exhibits atypical and extremely rare localization. The updated 2022 guidelines of the ESHRE recommend using both ultrasound and magnetic resonance imaging (MRI) as first-line diagnostic tests. Currently, MRI provides a more complete view of the pelvis anatomy. The aim of our review is to provide radiologists with a “map” that can help them in reporting pelvic MRI scans in patients with endometriosis. We will illustrate the usual and unusual localizations of endometriosis (categorized into compartments) using post-operative imaging, and we will focus on the role of MRI, the main sequences and the use of contrast agents. Full article

(This article belongs to the Special Issue Biomedical Imaging: From Methods to Applications)

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