ArticleDiagnostics (Basel, Switzerland)2022
Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.Zeitschrift fur medizinische Physik · 2024Article
- Microglial Positron Emission Tomography Imaging In Vivo : Positron Emission Tomography Radioligands: Utility in Research and Clinical Practice.Advances in neurobiology · 2024Review
- [European journal of nuclear medicine and molecular imaging · 2023Article
- Brain and Systemic Inflammation in De Novo Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2023Article
- PET Imaging of Neuro-Inflammation with Tracers Targeting the Translocator Protein (TSPO), a Systematic Review: From Bench to Bedside.Diagnostics (Basel, Switzerland) · 2023Review
- Textural properties of microglial activation in Alzheimer's disease as measured by (R)-[Brain communications · 2023Article
- Editorial on Special Issue "Quantitative PET and SPECT".Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.
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