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Bioengineering
Special Issues
Modern Medical Imaging in Disease Diagnosis Applications
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Modern Medical Imaging in Disease Diagnosis Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 3740

Special Issue Editors

Prof. Dr. Carlo Cosimo Quattrocchi

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Guest Editor
Centre for Medical Sciences-CISMed, University of Trento, 38122 Trento, Italy
Interests: radiology; neuroradiology; contrast media; management in radiology
Dr. Marco Parillo

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Guest Editor
Radiology, Multizonal Unit of Rovereto and Arco, APSS Provincia Autonoma Di Trento, 38123 Trento, Italy
Interests: radiology; diagnostic imaging; contrast media; guidelines in radiology

Special Issue Information

Dear Colleagues,

Radiology has emerged as one of the most dynamic and rapidly evolving medical fields in recent years. Thanks to improvements in both hardware and software, techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound allow for non-invasive and highly precise exploration of nearly all anatomical regions, becoming fundamental in the diagnostic and therapeutic pathways of most pathologies. This Special Issue on “Modern Medical Imaging in Disease Diagnosis Applications”, therefore, will focus on original research papers and comprehensive reviews that explore the integration of engineering principles with medical imaging to advance disease diagnosis and patient care. Topics of interest for this Special Issue include, but are not limited to, the following:

  • High-resolution imaging: advanced MRI and CT techniques that provide unprecedented detail of anatomical structures, pathological and physiological processes, enabling earlier and more accurate diagnosis.
  • Contrast media: recent advancements in the development and application of contrast agents, such as the introduction of high-relaxivity agents that have opened up numerous improvements in magnetic resonance imaging diagnostics.
  • Artificial intelligence (AI) in imaging: exploring the role of AI algorithms in image analysis, including large language models, for the automated detection of abnormalities, improved diagnostic accuracy of radiologists, and personalized treatment planning.
  • Radiomics: Exploring the role of high-dimensional quantitative features extracted from medical images to uncover patterns beyond visual interpretation.

This Special Issue seeks to showcase the interplay between engineering innovation and medical imaging, offering insights into how emerging technologies can redefine diagnostic pathways and improve patient outcomes. We welcome contributions from researchers, engineers, and healthcare professionals working at the forefront of this interdisciplinary field.

Prof. Dr. Carlo Cosimo Quattrocchi
Dr. Marco Parillo
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. 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

  • radiology
  • diagnostic imaging
  • contrast media
  • computed tomography
  • magnetic resonance imaging
  • ultrasound
  • artificial intelligence in medical imaging
  • radiomics

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

Published Papers (2 papers)

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Research

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16 pages, 8673 KB  
Article
PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI
by Tianci Liu, Yao Zheng, Chengwei Chen, Jie Wei, Dong Huang, Yuefei Feng and Yang Liu
Bioengineering 2025, 12(9), 978; https://doi.org/10.3390/bioengineering12090978 - 15 Sep 2025
Viewed by 81
Abstract
Background: Glioblastoma (GBM) is a highly aggressive and heterogeneous primary malignancy of the central nervous system, with a median overall survival (OS) of approximately 15 months. Achieving accurate and generalizable OS prediction across multi-center settings is essential for clinical application. Methods: We propose [...] Read more.
Background: Glioblastoma (GBM) is a highly aggressive and heterogeneous primary malignancy of the central nervous system, with a median overall survival (OS) of approximately 15 months. Achieving accurate and generalizable OS prediction across multi-center settings is essential for clinical application. Methods: We propose a Personalized Habitat-aware Survival Prediction Network (PHSP-Net) that integrates multiparametric MRI with an adaptive habitat partitioning strategy. The network combines deep convolutional feature extraction and interpretable visualization modules to perform patient-specific subregional segmentation and survival prediction. A total of 1084 patients with histologically confirmed WHO grade IV GBM from four centers (UPENN-GBM, UCSF-PDGM, LUMIERE and TCGA-GBM) were included. PHSP-Net was compared with conventional radiomics, habitat imaging models and ResNet10, with independent validation on two external cohorts. Results: PHSP-Net achieved an AUROC of 0.795 (95% CI: 0.731–0.852) in the internal validation set, and 0.707 and 0.726 in the LUMIERE and TCGA-GBM external test sets, respectively—outperforming both comparison models. Kaplan–Meier analysis revealed significant OS differences between predicted high- and low-risk groups (log-rank p < 0.05). Visualization analysis indicated that necrotic-region habitats were key prognostic indicators. Conclusions: PHSP-Net demonstrates high predictive accuracy, robust cross-center generalization and improved interpretability in multi-center GBM cohorts. By enabling personalized habitat visualization, it offers a promising non-invasive tool for prognostic assessment and individualized clinical decision making in GBM. Full article
(This article belongs to the Special Issue Modern Medical Imaging in Disease Diagnosis Applications)
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Review

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19 pages, 6545 KB  
Review
Susceptibility-Weighted Imaging (SWI): Technical Aspects and Applications in Brain MRI for Neurodegenerative Disorders
by Federica Vaccarino, Carlo Cosimo Quattrocchi and Marco Parillo
Bioengineering 2025, 12(5), 473; https://doi.org/10.3390/bioengineering12050473 - 29 Apr 2025
Cited by 2 | Viewed by 3389
Abstract
Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence sensitive to substances that alter the local magnetic field, such as calcium and iron, allowing phase information to distinguish between them. SWI is a 3D gradient–echo sequence with high spatial resolution that leverages [...] Read more.
Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence sensitive to substances that alter the local magnetic field, such as calcium and iron, allowing phase information to distinguish between them. SWI is a 3D gradient–echo sequence with high spatial resolution that leverages both phase and magnitude effects. The interaction of paramagnetic (such as hemosiderin and deoxyhemoglobin), diamagnetic (including calcifications and minerals), and ferromagnetic substances with the local magnetic field distorts it, leading to signal changes. Neurodegenerative diseases are typically characterized by the progressive loss of neurons and their supporting cells within the neurovascular unit. This cellular decline is associated with a corresponding deterioration of both cognitive and motor abilities. Many neurodegenerative disorders are associated with increased iron accumulation or microhemorrhages in various brain regions, making SWI a valuable diagnostic tool in clinical practice. Suggestive SWI findings are known in Parkinson’s disease, Lewy body dementia, atypical parkinsonian syndromes, multiple sclerosis, cerebral amyloid angiopathy, amyotrophic lateral sclerosis, hereditary ataxias, Huntington’s disease, neurodegeneration with brain iron accumulation, and chronic traumatic encephalopathy. This review will assist radiologists in understanding the technical framework of SWI sequences for a correct interpretation of currently established MRI findings and for its potential future clinical applications. Full article
(This article belongs to the Special Issue Modern Medical Imaging in Disease Diagnosis Applications)
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