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![Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F58491.jpg&w=3840&q=50)
Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain
Published on: August 8, 2019
Quantifying capillary transit time distribution: a novel tracer kinetic model accounting for leakiness, back
Henrik B W Larsson1,2, Stig P Cramer3, Edis D Tireli3
1Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital - Rigshospitalet, Valdemar Hansens Vej 13, 2600, Glostrup, Copenhagen, Denmark. Henrik.bo.wiberg.larsson@regionh.dk.
Background:
Brain blood perfusion is typically characterized through regional cerebral blood flow metrics. However, recent research demonstrates that the distribution of blood transit times through the capillary network also plays a significant role in physiological processes. Here, we introduce a novel kinetic model, called the Outflow model, to quantify this distribution and calculate capillary transit time heterogeneity. The model is tailored to account for transport across the blood‒brain barrier and tissue-binding and is applicable for both for Magnetic Resonance Imaging and Positron emission Tomography scenarios. The model was employed across three methodologies: (1) contrast-enhanced T1-weighted Magnetic Resonance Imaging; (2) dynamic long axial field-of-view Positron Emission Tomography imaging using the radiotracers O-(2-[18F]fluoroethyl)-L-tyrosine and (3) 2-deoxy-2-[18F]fluoroglucose. The temporal resolution was optimized (1-2 s) to capture the bolus passages. Tissue curves were derived from the putamen, thalamus, frontal white matter, and regions of malignancy for comparative analysis. For the PET data the Outflow model was compared with a conventional 3-compartment model for validation.
Results:
For Magnetic Resonance Imaging, the capillary transit time heterogeneity ranged from 0.5 to 1 s, the extraction fraction ranged from 0.03 to 0.07%, and the unidirectional influx constant ranged from 0.01 to 0.2 ml/min/100 ml. For O-(2-[18F]fluoroethyl)-L-tyrosine, the capillary transit time heterogeneity ranged from 1.5 to 3 s, with an extraction fraction of 5.4% to 7.8% and the unidirectional influx constant ranging from 1.6 to 3.2 ml/min/100 ml, aligning closely with the anticipated parameters. For 2-deoxy-2-[18F]fluoroglucose obtain from the basal ganglia, capillary transit time heterogeneity was around 2 s, extraction fraction 40.2%, and cerebral metabolic rate of glucose consumption was 44 µmol/100 ml/min. Both the outflow model and the 3-compartment model gave excellent fit to data and for the comparable metrics the two models gave results in reasonable agreement.
Conclusion:
This proof-of-concept study demonstrated that calculating capillary transit time heterogeneity in the brain via the proposed new tracer kinetic model is feasible for both MRI and PET data. The model adeptly addresses scenarios where tracers or contrast agents undergo bidirectional transport across the BBB.
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