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One-day dual-tracer examination in neuroendocrine neoplasms: a real advantage of low activity LAFOV PET imaging.
Eur J Nucl Med Mol Imaging
January 2025
Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Otfried-Mueller-Str. 14, 72076, Tuebingen, Germany.
Purpose: Somatostatin receptor (SSTR)-PET is crucial for effective treatment stratification of neuroendocrine neoplasms (NENs). In highly proliferating or poorly differentiated NENs, dual-tracer approaches using additional [F]FDG PET can effectively identify SSTR-negative disease, usually requiring separate imaging sessions. We evaluated the feasibility of a one-day dual-tracer imaging protocol with a low activity [F]FDG PET followed by an SSTR-PET using the recently introduced [F]SiFAlin-TATE tracer in a long axial field-of-view (LAFOV) PET/CT scanner and its implications in patient management.
View Article and Find Full Text PDFDeep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.
Clin Nucl Med
January 2025
From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Purpose: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task.
View Article and Find Full Text PDFOptimizing Clinical Postgraduate Training: Perspectives of Postgraduate Alumni Regarding Their Residency Training at Mbarara University of Science and Technology, Uganda.
Adv Med Educ Pract
January 2025
Department of Obstetrics and Gynecology, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda.
Background: Understanding alumni satisfaction and the competencies gained during university training can guide improvements in education quality, particularly in higher education institutions. This study at Mbarara University of Science and Technology (MUST) explored the factors influencing alumni decisions, their residency experiences, and their levels of satisfaction. Additionally, it provided recommendations for improving graduate training programs.
View Article and Find Full Text PDFHow does the medicines retail sector ensure continued access to medicines during public health emergencies? Lessons from the COVID-19 pandemic in Uganda.
J Pharm Policy Pract
January 2025
Department of Community Health and Behavioral Sciences, Makerere University School of Public Health, Kampala, Uganda.
Background: The medicines retail sector (MRS) enables access to life-saving health commodities. Despite efforts to harness this market for public health goals, in low- and middle-income countries it is rarely incorporated into pandemic preparedness. This paper analyses the role of the MRS in the response to COVID-19 in Uganda, the extent to which it was incorporated into national planning and in the continuity of essential services.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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