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Diagnostics
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Intelligent Imaging in Nuclear Medicine
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Intelligent Imaging in Nuclear Medicine

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 (30 June 2023) | Viewed by 9813

Special Issue Editors

Dr. Dilber Uzun Ozsahin

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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
Prof. Dr. Georges El Fakhri
Dr. Ilker Ozsahin
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 (5 papers)

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11 pages, 4899 KiB  
Article
Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network
by Tomoyuki Fujioka, Yoko Satoh, Tomoki Imokawa, Mio Mori, Emi Yamaga, Kanae Takahashi, Kazunori Kubota, Hiroshi Onishi and Ukihide Tateishi
Diagnostics 2022, 12(12), 3114; https://doi.org/10.3390/diagnostics12123114 - 09 Dec 2022
Cited by 3 | Viewed by 1132
Abstract
This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model [...] Read more.
This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1–5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine)
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22 pages, 3535 KiB  
Article
Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities
by Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Nikolaos I. Papandrianos, Elpiniki I. Papageorgiou and George S. Panayiotakis
Diagnostics 2022, 12(10), 2333; https://doi.org/10.3390/diagnostics12102333 - 27 Sep 2022
Cited by 1 | Viewed by 1643
Abstract
Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods [...] Read more.
Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine)
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13 pages, 2344 KiB  
Article
Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model
by Takaaki Yoshimura, Atsushi Hasegawa, Shoki Kogame, Keiichi Magota, Rina Kimura, Shiro Watanabe, Kenji Hirata and Hiroyuki Sugimori
Diagnostics 2022, 12(4), 872; https://doi.org/10.3390/diagnostics12040872 - 31 Mar 2022
Cited by 4 | Viewed by 1819
Abstract
In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, [...] Read more.
In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine)
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9 pages, 659 KiB  
Article
Identification of the Optimal Cut-Off Value of PSA for Assessing Severity of Disease in [68Ga]Ga-PSMA-11 PET/CT Study in Prostate Cancer Patients after Radical Prostatectomy
by Paulina Cegla, Marta Wojewódzka, Izabela Gorczewska, Wioletta Chalewska, Grażyna Łapińska, Paweł Ochman, Agata Sackiewicz and Marek Dedecjus
Diagnostics 2022, 12(2), 349; https://doi.org/10.3390/diagnostics12020349 - 29 Jan 2022
Cited by 1 | Viewed by 1711
Abstract
Objective: The objective of this study was to identify the optimal cut-off value of prostate specific antigen (PSA) to assess the extent of the disease in [68Ga]Ga-PSMA-11 PET/CT study in patients after radical prostatectomy. Materials and Methods: Retrospective analysis was performed [...] Read more.
Objective: The objective of this study was to identify the optimal cut-off value of prostate specific antigen (PSA) to assess the extent of the disease in [68Ga]Ga-PSMA-11 PET/CT study in patients after radical prostatectomy. Materials and Methods: Retrospective analysis was performed on a group of 215 patients who underwent a [68Ga]Ga-PSMA-11 PET/CT examination because of suspected recurrence after radical prostatectomy. Patients were divided into four groups: 1, no active lesions suggesting recurrence (n = 92); 2, suspected isolated local recurrence (n = 19); 3, oligometastatic disease (n = 82); and 4, polymetastatic disease (n = 22). Results: In group 1, the mean PSA level was 0.962 ng/mL (median: 0.376; min: 0.004; max: 25 ng/mL); in group 2, it was 4.970 ng/mL (median 1.320; min: 0.003; max: 40.350 ng/mL); in group 3, it was 2.802 ng/mL (median: 1.270; min: 0.020; max: 59.670 ng/mL); and in group 4, it was 4.997 ng/mL (median: 3.795; min: 0.007; max 21.110 ng/mL). Statistically significant differences were shown in PSA levels when comparing groups 1 and 2 (p = 0.0025) and groups 3 and 4 (p = 0.0474). The PSA cut-off point for discriminating groups 1 and 2 was 0.831 (sensitivity: 0.684; specificity: 0.772; area under the curve (AUC): 0.775), and for groups 3 and 4, it was 2.51 (sensitivity: 0.682; specificity: 0.780; AUC: 0.720). Conclusions: Our preliminary data suggested that the PSA level has an essential influence on determining the extent of disease in a [68Ga]Ga-PSMA-11 PET/CT study in patients after radical prostatectomy. Identification of the optimal cut-off values for the oligo- and polymetastatic diseases might be helpful in stratifying these patients. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine)
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13 pages, 1140 KiB  
Systematic Review
Systematic Review on Diagnostic Reference Levels for Computed Tomography Examinations in Radiation Therapy Planning
by Shreekripa Rao, Krishna Sharan, Suresh Sukumar, Srinidhi Gururajarao Chandraguthi, Rechal Nisha Dsouza, Leena R. David, Sneha Ravichandran, Berna Uzun, Rajagopal Kadavigere and Dilber Uzun Ozsahin
Diagnostics 2023, 13(6), 1072; https://doi.org/10.3390/diagnostics13061072 - 11 Mar 2023
Cited by 1 | Viewed by 1992
Abstract
Background: In August 2017, the European Commission awarded the “European Study on Clinical Diagnostic Reference Levels (DRL) for X-ray Medical Imaging” project to the European Society of Radiology to provide up-to-date Diagnostic Reference Levels based on clinical indications. This work aimed to [...] Read more.
Background: In August 2017, the European Commission awarded the “European Study on Clinical Diagnostic Reference Levels (DRL) for X-ray Medical Imaging” project to the European Society of Radiology to provide up-to-date Diagnostic Reference Levels based on clinical indications. This work aimed to conduct an extensive literature review by analyzing the most recent studies published and the data provided by the National Competent Authorities to understand the current situation regarding Diagnostic Reference Levels based on clinical indications for Radiation Therapy Computed Tomography. Objective: To review the literature on established DRLs and methodologies for establishing Diagnostic reference levels in radiation therapy planning computed tomography (RTCT). Methods: Eligibility criteria: A cohort study (observational design) reporting DRLs in adult patients undergoing computed tomography (CT) for radiation therapy for the region head and neck or pelvis were included. The comprehensive literature searches for the relevant studies published between 2000 and 2021 were performed using PubMed, Scopus, CINHAL, Web of Science, and ProQuest. Results: Three hundred fifty-six articles were identified through an extensive literature search. Sixty-eight duplicate reports were removed. The title and abstract of 288 studies were assessed and excluded if they did not meet the inclusion criteria. Sixteen of 288 articles were selected for full-text screening (studies conducted between 2000 and 2021). Five articles were included in the review after the full-text screening. Conclusions: A globally approved standard protocol that includes scanning techniques, dose measurement method, and DRL percentile needs to be established to make a valuable and accurate comparison with international DRLs. Full article
(This article belongs to the Special Issue Intelligent Imaging in Nuclear Medicine)
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