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Diagnostics
Special Issues
Intelligent Imaging in Nuclear Medicine—2nd Edition
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Intelligent Imaging in Nuclear Medicine—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 451

Special Issue Editors

Dr. Dilber Uzun Ozsahin

E-Mail Website
Guest Editor
1. Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, United Arab Emirates
2. Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
3. Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: medical imaging; nuclear medicine; decision theory in healthcare; artificial intelligence in healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Ilker Ozsahin

E-Mail Website
Guest Editor
1. Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
2.Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
Interests: medical imaging; radiology; operational research; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Georges El Fakhri

E-Mail Website
Guest Editor
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
Interests: medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) makes use of algorithms that are networked together to function as a neural network. The AI is taught by trial and error, and over time it learns to be very precise and accurate. The application of AI in nuclear medicine varies from easy checkups of diseases in the body to helping to analyze patients’ treatment progress and capturing images more quickly. It can be agreed that the efficiency and accuracy of AI has helped many clinicians to easily identify and diagnose the diseases in their patients’ bodies. The use of AI in diagnosis is more accurate than human experts in some cases. The application of AI in nuclear medicine is increasing in the diagnosis of diseases such as Alzheimer’s disease, Parkinson’s disease, and cardiological disorders. This Special Issue will cover AI applications in nuclear medicine and its application in different areas.

Dr. Dilber Uzun Ozsahin
Dr. Ilker Ozsahin
Prof. Dr. Georges El Fakhri
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. 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

  • nuclear medicine
  • artificial intelligence
  • Alzheimer’s disease
  • Parkinson’s disease
  • cardiological disorders

Related Special Issue

Published Papers (1 paper)

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Research

14 pages, 261 KiB  
Article
Advanced Computational Methods for Radiation Dose Optimization in CT
by Shreekripa Rao, Krishna Sharan, Srinidhi Gururajarao Chandraguthi, Rechal Nisha Dsouza, Leena R. David, Sneha Ravichandran, Mubarak Taiwo Mustapha, Dilip Shettigar, Berna Uzun, Rajagopal Kadavigere, Suresh Sukumar and Dilber Uzun Ozsahin
Diagnostics 2024, 14(9), 921; https://doi.org/10.3390/diagnostics14090921 (registering DOI) - 29 Apr 2024
Abstract
Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept [...] Read more.
Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine—2nd Edition)
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