Essentials of Imaging Diagnosis in Pulmonary Diseases: Techniques and Clinical Practice
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 30 July 2026 | Viewed by 26
Special Issue Editor
Special Issue Information
Dear Colleagues,
Pulmonary diseases remain one of the leading causes of morbidity and mortality worldwide. Diagnostic imaging plays an essential role in prevention, early detection, accurate diagnosis, and therapeutic monitoring. Recent developments in high-resolution CT, functional MRI, PET/CT, and AI-based image analysis have revolutionized pulmonary imaging, allowing quantitative and functional assessment of lung structure and physiology. These advances are paving the way toward precision imaging and personalized care. This Special Issue of Diagnostics, titled “Essentials of Imaging Diagnosis in Pulmonary Diseases: Techniques and Clinical Practice,” aims to collect original studies and comprehensive reviews focused on current imaging methodologies, quantitative approaches, and clinical applications in respiratory medicine.
This Special Issue will explore the technological and clinical aspects of imaging diagnosis in pulmonary diseases, promoting collaboration among radiologists, pulmonologists, nuclear medicine specialists, and data scientists. The goal is to bridge the gap between innovation and clinical practice, fostering the implementation of advanced diagnostic tools in daily care. The topic aligns with the scope of Diagnostics, which covers the latest advances in diagnostic techniques, image-based biomarkers, and translational applications across medical disciplines.
Potential topics include, but are not limited to, the following:
- Advances in CT, MRI, and PET/CT imaging for lung diseases;
- Quantitative imaging biomarkers and radiomics;
- Artificial intelligence and machine learning in pulmonary imaging;
- Functional and hybrid imaging of ventilation and perfusion;
- Imaging diagnosis and monitoring in COPD, ILD, pulmonary hypertension, and lung cancer;
- Imaging-guided therapy and response assessment;
- Standardization, harmonization, and quality assurance;
- Education, ethics, and clinical integration of new imaging technologies.
We welcome original research articles, reviews, short communications, and perspective papers.
We look forward to receiving your valuable contributions that will advance understanding and clinical practice in pulmonary imaging.
Dr. Chiara Romei
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 250 words) can be sent to the Editorial Office for assessment.
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. Diagnostics 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 2600 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
- pulmonary imaging
- CT, MRI, and PET/CT imaging for lung diseases
- quantitative imaging
- artificial intelligence and machine learning
- ventilation and perfusion imaging
- clinical application
- diagnosis
- personalized medicine
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