Underestimation of 68Ga PET/CT SUV caused by activity overestimation using default calibrator settings

Sanderson, Tom; Solomon, James; Nottage, Chris; Dickson, John

doi:10.1016/j.ejmp.2019.03.006

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The first factor that may affect the quantification accuracy is the radionuclide calibrator. Calibrator accuracy should be part of the PET centre's QC program, especially when radionuclides other than 18 F are used [28,29]. In addition, to obtain reliable and comparable intra and inter-equipment data, cross-calibration between the PET scanner and the radionuclide calibrator has to be performed on a regular basis.…”

Section: Discussionmentioning

confidence: 99%

Quality control in PET/CT and PET/MRI: Results of a survey amongst European countries

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Quality control in PET/CT and PET/MRI: Results of a survey amongst European countries

et al. 2022

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“…SPECT and PET images are evaluated by quantitative/qualitative approaches that are grouped into three categories; straightforward qualitative method based on expert observation, semi‐quantitative methods based on standardised uptake value (SUV) and its variants and kinetic modelling analysis [66, 67]. SUV is defined as the ratio of the specific radioactivity in the measured zone and introduced radioactivity per body mass [68, 69]. Nevertheless, SUV do not differentiate between specific and non‐specific radio‐ligand binding into the target, while kinetic modelling analysis approach allows assessing physiological parameters as a differential metrics.…”

Section: Ml/dl In Pd Diagnosismentioning

confidence: 99%

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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“…In most PET/CT systems, cross-calibration between the dose calibrator and a PET system is performed using 18 F, which may result in inaccurate quantification with radioisotopes other than 18 F. Accurate quantification for non- 18 F radioisotopes requires proper correction of such physical properties as branching ratio, half-life, and prompt γ rays. Bailey et al [ 125 ] and Sanderson et al [ 126 ] noted most PET systems underestimated 68 Ga SUVs which was caused by overestimation of 68 Ga radioactivity using dose calibrators with a default calibration factor setting. Appropriate 68 Ga calibration factor setting is important to provide accurate quantitative values.…”

Section: Introductionmentioning

confidence: 99%

A review of harmonization strategies for quantitative PET

et al. 2023

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PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-18F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.

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Underestimation of 68Ga PET/CT SUV caused by activity overestimation using default calibrator settings

Cited by 7 publications

References 6 publications

Quality control in PET/CT and PET/MRI: Results of a survey amongst European countries

Quality control in PET/CT and PET/MRI: Results of a survey amongst European countries

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

A review of harmonization strategies for quantitative PET

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