Unlabelled: The authors' goal was to show the importance of starting scanning at a uniform time after F-18 fluorodeoxyglucose injection in positron emission tomography (PET) brain study.
Method: Fifteen healthy normal subjects underwent FDG-PET to obtain glucose metabolic images starting 60 min and 70 min after FDG injection, respectively. The two sets of images were compared in a voxel-by-voxel analysis.
Results: In the bilateral posterior cingulate gyrus, parietal and frontal association cortices, the FDG uptakes were larger on the 70 min scan images than on the 60 min scan images; the 60 min scans resembled Alzheimer's metabolic reduction area. Similarly the FDG uptakes were larger in the pons and vermis on the 60 min scan image than on the 70 min scan image.
Conclusions: Regional FDG uptake is different depending on the time scanning starts after FDG injection, even with a 10 minute difference in start time and different scanning time may lead to misdiagnosis. It is important to standardize the start time of FDG PET after FDG injection in brain PET.
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http://dx.doi.org/10.1007/BF02984652 | DOI Listing |
Theranostics
January 2025
Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
The role of oxidative stress metabolism during hepatocellular carcinoma (HCC) formation potentially allows for positron emission tomography (PET) imaging of oxidative stress activity for early and precise HCC detection. However, there is currently limited data available on oxidative-stress-related PET imaging for longitudinal monitoring of the pathophysiological changes during HCC formation. This work aimed to explore PET-based longitudinal monitoring of oxidative stress metabolism and determine the sensitivity of [18F]-5-fluoroaminosuberic acid ([18F]FASu) for assessing pathophysiological processes in diethylnitrosamine (DEN) induced rat HCC.
View Article and Find Full Text PDFAsia Ocean J Nucl Med Biol
January 2025
Department of Radiology, Faculty of Medicine, Shimane University, Izumo, Japan.
Objectives: We investigated image quality and standardized uptake values (SUVs) for different lesion sizes using clinical data generated by F-FDG-prone breast silicon photomultiplier (SiPM)-based positron emission tomography/computed tomography (PET/CT).
Methods: We evaluated the effect of point-spread function (PSF) modeling and Gaussian filtering (Gau) and determined the optimal reconstruction conditions. We compared the signal-to-noise ratio (SNR), contrast, %coefficient of variation (%CV), SUV, and Likert scale score between ordered-subset expectation maximization (OSEM) time-of-flight (TOF) and OSEM+TOF+PSF in phantom and clinical studies.
Bioorg Chem
December 2024
Department of Nuclear Medicine, Institute of Clinical Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 210000, China. Electronic address:
The non-specificity of F-FDG, coupled with high false-positive rates in pancreatitis, underscores an unmet clinical need for using specific positron emission tomography (PET) radiopharmaceuticals in noninvasive pancreatic cancer detection. ST14, a trypsin-like protease and a member of the type II transmembrane serine protease family, is overexpressed in various solid malignancies, including pancreatic cancer. This study aimed to develop a Ga-labeled PET radiopharmaceutical targeting ST14 for pancreatic cancer detection.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
December 2024
Neurosurgery Artificial Intelligence Lab, Stanford University School of Medicine, Stanford, California.
Background: The inability to localize pain generators often results in failed back surgery syndrome (FBSS). Structural imaging can identify multiple and/or noncausative abnormalities. Molecular imaging of glucose transporters offers the opportunity to localize metabolically active sites.
View Article and Find Full Text PDFQuant Imaging Med Surg
December 2024
Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China.
Background: Deep-learning-based denoising improves image quality and quantification accuracy for low count (LC) positron emission tomography (PET). Conventional deep-learning-based denoising methods only require single LC PET image input. This study aims to propose a deep-learning-based LC PET denoising method incorporating computed tomography (CT) priors to further reduce the dose level.
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