Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

Advances and Applications of Medical Imaging Physics

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 9386

Share This Special Issue

Special Issue Editor

Prof. Dr. Gordana Žauhar

E-Mail Website
Guest Editor

1. Department of Medical Physics and Biophysics, Faculty of Medicine, University of Rijeka, Braće Branchetta 20, 51000 Rijeka, Croatia
2. Faculty of Physics, University of Rijeka, Radmile Matejčić 2, 51 000 Rijeka, Croatia
Interests: ultrasound; medical physics; nuclear medicine; medical imaging

Special Issue Information

Dear Colleagues,

Medical imaging has developed rapidly and is now a versatile tool with numerous possible applications. Imaging has experienced a quantum leap in technology and clinical applications over the last 30 years. This leap includes super-resolution ultrasound imaging, X-ray computed tomography (CT), emission computed tomography (SPECT and PET), magnetic resonance imaging (MRI), including functional MRI (fMRI), as well as combined, hybrid, or dual-imaging techniques. The application of the principles and methods of physics in medical imaging has contributed to an improvement in this field. Physics-based techniques have been progressively developed and optimized to help physicians make rapid diagnoses and establish effective treatments for various diseases. The development of medical imaging is the result of physicists collaborating with engineers and physicians. As medical imaging continues to evolve, researchers are finding ways to improve diagnosis and treatment planning. One of the most exciting areas currently being researched is the application of artificial intelligence to medical imaging, which can set new frontiers in both diagnosing disease and planning as well as monitoring the effectiveness of treatments.

The scope of this Special Issue of Applied Sciences, entitled “Advances and Applications of Medical Imaging Physics”, is to collect original research manuscripts describing cutting-edge medical imaging physics developments in medicine, as well as reviews providing updates on the latest progresses in this field.

Prof. Dr. Gordana Žauhar
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. 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

medical physics
medical imaging
imaging technology
ultrasound imaging
X-ray computed tomography (CT)
magnetic resonance imaging (MRI)
positron emission tomography (PET)
hybrid imaging
artificial intelligence

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

15 pages, 3571 KiB

Open AccessArticle

SPECT and MRI Evaluation of Cerebrovascular Reactivity with CO₂ Inhalation—A Preliminary Study

by Min-Gyu Song, Jeong-Min Shim, Young-Don Son, Yeong-Bae Lee and Chang-Ki Kang

Appl. Sci. 2025, 15(10), 5352; https://doi.org/10.3390/app15105352 - 10 May 2025

Viewed by 222

Abstract

Assessment of cerebrovascular function is crucial for managing neurological disorders, with cerebral blood flow (CBF) measurement being key. Single photon emission computed tomography (SPECT), a traditional method, uses radiation exposure. Blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) with carbon dioxide (CO₂) is a non-invasive cerebrovascular reactivity (CVR) alternative, but direct SPECT-MRI CO₂ comparisons for MRI’s replacement potential are limited. This study directly compared CVR from SPECT and MRI CO₂ in nine healthy participants. Delay-based MRI (tcMRI) with stimulus timing correction was analyzed alongside conventional MRI. Results showed no significant CVR differences between SPECT and tcMRI (p = 0.688) or SPECT and conventional MRI (p = 0.813), indicating comparable overall CVR. However, tcMRI significantly differed from conventional MRI (p = 0.016) and showed a greater similarity to SPECT. Regionally, the largest CVR differences were observed between tcMRI and conventional MRI, particularly in the cingulate cortex, frontal lobe, and basal ganglia. These discrepancies suggest that tcMRI may capture subtle CVR abnormalities not detected by conventional MRI. The findings support the clinical utility of CO₂-MRI, especially with stimulus timing correction, as a safe, repeatable, and radiation-free alternative to SPECT. In particular, tcMRI may offer advantages for repeated CVR assessments in long-term clinical monitoring. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

13 pages, 4955 KiB

Open AccessArticle

Retinitis Pigmentosa Classification with Deep Learning and Integrated Gradients Analysis

by Hélder Ferreira, Ana Marta, Jorge Machado, Inês Couto, João Pedro Marques, João Melo Beirão and António Cunha

Appl. Sci. 2025, 15(4), 2181; https://doi.org/10.3390/app15042181 - 18 Feb 2025

Viewed by 1663

Abstract

Inherited retinal diseases (IRDs) are genetic disorders affecting photoreceptors and the retinal pigment epithelium, leading to progressive vision loss. Retinitis pigmentosa (RP), the most common IRD, manifests as night blindness, peripheral vision loss, and eventually central vision decline. RP is genetically diverse and can be categorized into non-syndromic and syndromic. Advanced imaging technologies such as fundus autofluorescence (FAF) and spectral-domain optical coherence tomography (SD-OCT) facilitate diagnosing and managing these conditions. The integration of artificial intelligence in analyzing retinal images has shown promise in identifying genes associated with RP. This study used a dataset from Portuguese public hospitals, comprising 2798 FAF images labeled for syndromic and non-syndromic RP across 66 genes. Three pre-trained models, Inception-v3, ResNet-50, and VGG-19, were used to classify these images, obtaining an accuracy of over 80% in the training data and 54%, 56%, and 54% in the test data for all models. Data preprocessing included class balancing and boosting to address variability in gene representation. Model performance was evaluated using some main metrics. The findings demonstrate the effectiveness of deep learning in automatically classifying retinal images for different RP-associated genes, marking a significant advancement in the diagnostic capabilities of artificial intelligence and advanced imaging techniques in IRD. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

12 pages, 513 KiB

Open AccessArticle

Application of Artificial Intelligence as an Aid for the Correction of the Objective Structured Clinical Examination (OSCE)

by Davide Luordo, Marta Torres Arrese, Cristina Tristán Calvo, Kirti Dayal Shani Shani, Luis Miguel Rodríguez Cruz, Francisco Javier García Sánchez, Alfonso Lagares Gómez-Abascal, Rafael Rubio García, Juan Delgado Jiménez, Mercedes Pérez Carreras, Ramiro Diez Lobato, Juan José Granizo Martínez, Yale Tung-Chen and Mª Victoria Villena Garrido

Appl. Sci. 2025, 15(3), 1153; https://doi.org/10.3390/app15031153 - 23 Jan 2025

Viewed by 1461

Abstract

The assessment of clinical competencies is essential in medical training, and the Objective Structured Clinical Examination (OSCE) is an essential tool in this process. There are multiple studies exploring the usefulness of artificial intelligence (AI) in medical education. This study explored the use of the GPT-4 AI model to grade clinical reports written by students during the OSCE at the Teaching Unit of the 12 de Octubre and Infanta Cristina University Hospitals, part of the Faculty of Medicine at the Complutense University of Madrid, comparing its results with those of human graders. Ninety-six (96) students participated, and their reports were evaluated by two experts, an inexperienced grader, and the AI using a checklist designed during the OSCE planning by the teaching team. The results show a significant correlation between the AI and human graders (ICC = 0.77 for single measures and 0.91 for average measures). AI was more stringent, assigning scores on an average of 3.51 points lower (t = −15.358, p < 0.001); its correction was considerably faster, completing the analysis in only 24 min compared to the 2–4 h required by human graders. These results suggest that AI could be a promising tool to enhance efficiency and objectivity in OSCE grading. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

12 pages, 1069 KiB

Open AccessArticle

Fetal Radiation Dose in Common Diagnostic Radiology Procedures for Pregnant Patients: Findings from In-Phantom Measurements

by Anja Tomić, Hrvoje Brkić, Tajana Turk, Mladen Kasabašić, Ivana Bjelobrk, Ivana Kralik, Francesca De Monte, Nicola Zancopè, Riccardo Lombardi, Marija Majer, Željka Knežević, Mercedes Horvat, Matko Škarica, Zrinka Marić, Dario Faj and Vjekoslav Kopačin

Appl. Sci. 2025, 15(3), 1143; https://doi.org/10.3390/app15031143 - 23 Jan 2025

Viewed by 1249

Abstract

The diagnosis of emergent conditions during pregnancy can be delayed due to insufficient knowledge of fetal radiation doses in different imaging modalities. The aim of this article is to investigate the ranges of fetal doses in most common diagnostic and interventional radiology procedures. Procedures were carried out on an anthropomorphic phantom, Tena, representing a pregnant woman in the 18th week of pregnancy with the fetus in breech position. Different clinical scenarios using computer tomography (CT), radiography, fluoroscopy and digital subtraction angiography were selected in three teaching hospitals. Measurements were performed using radiophotoluminescent glass dosimeters placed in dedicated holes in the fetal head and fetal body. Measured fetal doses were below 1 mGy when the fetus was not in the primary beam. The highest fetal doses, up to 47 mGy, were measured after a CT scan for polytrauma and up to 24 mGy after a CT scan of the abdomen and pelvis. Significant variability in fetal doses for the same procedure was found between different hospitals but within the same hospital also. All obtained results are below the threshold for deterministic effects given by the International Commission for Radiation Protection but can be reached with two or more imaging procedures employed. The variability in fetal doses for the same procedures highlights the need for the improved optimization of imaging protocols. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

15 pages, 7377 KiB

Open AccessArticle

Application of Adaptive Search Window-Based Nonlocal Total Variation Filter in Low-Dose Computed Tomography Images: A Phantom Study

by Hajin Kim, Bo Kyung Cha, Kyuseok Kim and Youngjin Lee

Appl. Sci. 2024, 14(23), 10886; https://doi.org/10.3390/app142310886 - 24 Nov 2024

Viewed by 795

Abstract

Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

14 pages, 5223 KiB

Open AccessArticle

Evaluation of Single-Photon Emission Computed Tomography Myocardial Perfusion Detection Capability through Physical Descriptors

by Dea Dundara Debeljuh, Roberta Matheoud, Ivan Pribanić, Marco Brambilla and Slaven Jurković

Appl. Sci. 2024, 14(12), 5283; https://doi.org/10.3390/app14125283 - 18 Jun 2024

Viewed by 1239

Abstract

A comprehensive validation of data acquired by different myocardial perfusion imaging (MPI) systems was performed to evaluate contrast, self-attenuation properties, and perfusion detection capability. An anthropomorphic phantom with a myocardial insert and perfusion defect was used to simulate ^99mTc-tetrofosmin distribution. Different MPI systems were evaluated: a SPECT system with iterative reconstruction algorithms and resolution recovery (IRR) with/without scatter correction (SPECT-IRR-SC and SPECT-IRR), and a cardio-centric IQ SPECT/CT system with IRR, with/without scatter and attenuation corrections (IQ-IRR-SC-AC and IQ-IRR). The image quality was assessed through physical descriptors: the contrast between the left ventricular (LV) wall and LV inner chamber (

C_{L V / L V I C}

), intrinsic contrast (IC), and net contrast (NC).

C_{L V / L V I C}

was found to be superior for IQ-IRR-SC-AC. The IC results showed non-uniformity of the signal intensity in the LV wall for the SPECT systems. The lowest IC values were obtained for IQ-IRR-SC-AC, except for septal position, where an underestimation of the signal intensity was revealed. The NC was found to be the highest for IQ-IRR-SC-AC and SPECT-IRR-SC. Additionally, for IQ-IRR-SC-AC, the NC increased in posterior and septal positions compared to IQ-IRR, enabling better perfusion detection capability over short-axis images. IQ-IRR showed performances comparable to SPECT-IRR. The characterization and evaluation perfusion detection capability of the MPI systems enabled the investigation of the systems’ performance and limitations. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Figure 1

Other

Jump to: Research

23 pages, 898 KiB

Open AccessSystematic Review

Artificial Intelligence and Statistical Models for the Prediction of Radiotherapy Toxicity in Prostate Cancer: A Systematic Review

by Antonio Piras, Rosario Corso, Viviana Benfante, Muhammad Ali, Riccardo Laudicella, Pierpaolo Alongi, Andrea D'Aviero, Davide Cusumano, Luca Boldrini, Giuseppe Salvaggio, Domenico Di Raimondo, Antonino Tuttolomondo and Albert Comelli

Appl. Sci. 2024, 14(23), 10947; https://doi.org/10.3390/app142310947 - 25 Nov 2024

Cited by 4 | Viewed by 1858

Abstract

Background: Prostate cancer (PCa) is the second most common cancer in men, and radiotherapy (RT) is one of the main treatment options. Although effective, RT can cause toxic side effects. The accurate prediction of dosimetric parameters, enhanced by advanced technologies and AI-based predictive models, is crucial to optimize treatments and reduce toxicity risks. This study aims to explore current methodologies for predictive dosimetric parameters associated with RT toxicity in PCa patients, analyzing both traditional techniques and recent innovations. Methods: A systematic review was conducted using the PubMed, Scopus, and Medline databases to identify dosimetric predictive parameters for RT in prostate cancer. Studies published from 1987 to April 2024 were included, focusing on predictive models, dosimetric data, and AI techniques. Data extraction covered study details, methodology, predictive models, and results, with an emphasis on identifying trends and gaps in the research. Results: After removing duplicate manuscripts, 354 articles were identified from three databases, with 49 shortlisted for in-depth analysis. Of these, 27 met the inclusion criteria. Most studies utilized logistic regression models to analyze correlations between dosimetric parameters and toxicity, with the accuracy assessed by the area under the curve (AUC). The dosimetric parameter studies included Vdose, Dmax, and Dmean for the rectum, anal canal, bowel, and bladder. The evaluated toxicities were genitourinary, hematological, and gastrointestinal. Conclusions: Understanding dosimetric parameters, such as DVH, Dmax, and Dmean, is crucial for optimizing RT and predicting toxicity. Enhanced predictive accuracy improves treatment effectiveness and reduces side effects, ultimately improving patients’ quality of life. Emerging artificial intelligence and machine learning technologies offer the potential to further refine RT in PCa by analyzing complex data, and enabling more personalized treatment approaches. Full article

(This article belongs to the Special Issue Advances and Applications of Medical Imaging Physics)

► Show Figures

Journal Menu

Journal Browser

Advances and Applications of Medical Imaging Physics

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI