Evaluation of the use of a standard input function for compartment analysis of [123I]iomazenil data: factors influencing the quantitative results

Standardized Input Function for 18F-FDG PET Studies in Mice: A Cautionary Study

Aim of the Study

The aim of this study was to assess the accuracy of a standardized arterial input function (SAIF) for positron emission tomography ¹⁸F-FDG studies in mice. In particular, we tested whether the same SAIF could be applied to populations of mice whose fasting conditions differed.

Methods

The SAIF was first created from a population of fasting mice (n = 11) and validated within this group using a correlation analysis and a leave-one-out procedure. Then, the SAIF was prospectively applied to a population of non-fasting mice (n = 16). The SAIFs were scaled using a single individual blood sample taken 25 min after injection. The metabolic rates of glucose (CMRglc) calculated with the SAIFs were compared with the reference values obtained by full arterial sampling (AIF).

Results

In both populations of mice, CMRglc values showed a very small bias but an important variability. The SAIF/AIF CMRglc ratio in the fasting mice was 0.97 ± 0.22 (after excluding a major outlier). The SAIF/AIF CMRglc ratio in the non-fasting mice was 1.04 ± 0.22. This variability was due to the presence of cases in which the SAIF poorly estimated the shape of the input function based on full arterial sampling.

Conclusion

Although SAIF allows the estimation of the ¹⁸F-FDG mice input function with negligible bias and independently from the fasting state, errors in individual mice (as high as 30–50%) cause an important variability. Alternative techniques, such as image-derived input function, might be a better option for mice PET studies.

Population-based input function modeling for [(18)F]FMPEP-d 2, an inverse agonist radioligand for cannabinoid CB1 receptors: validation in clinical studies

Background

Population-based input function (PBIF) may be a valid alternative to full blood sampling for quantitative PET imaging. PBIF is typically validated by comparing its quantification results with those obtained via arterial sampling. However, for PBIF to be employed in actual clinical research studies, its ability to faithfully capture the whole spectrum of results must be assessed. The present study validated a PBIF for [¹⁸F]FMPEP-d₂, a cannabinoid CB₁ receptor radioligand, in healthy volunteers, and also attempted to utilize PBIF to replicate three previously published clinical studies in which the input function was acquired with arterial sampling.

Methods

The PBIF was first created and validated with data from 42 healthy volunteers. This PBIF was used to assess the retest variability of [¹⁸F]FMPEP-d₂, and then to quantify CB₁ receptors in alcoholic patients (n = 18) and chronic daily cannabis smokers (n = 29). Both groups were scanned at baseline and after 2–4 weeks of monitored drug abstinence.

Results

PBIF yielded accurate results in the 42 healthy subjects (average Logan-distribution volume (V_T) was 13.3±3.8 mL/cm³ for full sampling and 13.2±3.8 mL/cm³ for PBIF; R² = 0.8765, p<0.0001) and test-retest results were comparable to those obtained with full sampling (variability: 16%; intraclass correlation coefficient: 0.89). PBIF accurately replicated the alcoholism study, showing a widespread ∼20% reduction of CB₁ receptors in alcoholic subjects, without significant change after abstinence. However, a small PBIF-V_T bias of −9% was unexpectedly observed in cannabis smokers. This bias led to substantial errors, including a V_T decrease in regions that had shown no downregulation in the full input function. Simulated data showed that the original findings could only have been replicated with a PBIF bias between −6% and +4%.

Conclusions

Despite being initially well validated in healthy subjects, PBIF may misrepresent clinical protocol results and be a source of variability between different studies and institutions.

Population-based input function and image-derived input function for [¹¹C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder

Quantitative PET studies of neuroreceptor tracers typically require that arterial input function be measured. The aim of this study was to explore the use of a population-based input function (PBIF) and an image-derived input function (IDIF) for [(11)C](R)-rolipram kinetic analysis, with the goal of reducing - and possibly eliminating - the number of arterial blood samples needed to measure parent radioligand concentrations.

METHODS

A PBIF was first generated using [(11)C](R)-rolipram parent time-activity curves from 12 healthy volunteers (Group 1). Both invasive (blood samples) and non-invasive (body weight, body surface area, and lean body mass) scaling methods for PBIF were tested. The scaling method that gave the best estimate of the Logan-V(T) values was then used to determine the test-retest variability of PBIF in Group 1 and then prospectively applied to another population of 25 healthy subjects (Group 2), as well as to a population of 26 patients with major depressive disorder (Group 3). Results were also compared to those obtained with an image-derived input function (IDIF) from the internal carotid artery. In some subjects, we measured arteriovenous differences in [(11)C](R)-rolipram concentration to see whether venous samples could be used instead of arterial samples. Finally, we assessed the ability of IDIF and PBIF to discriminate depressed patients (MDD) and healthy subjects.

RESULTS

Arterial blood-scaled PBIF gave better results than any non-invasive scaling technique. Excellent results were obtained when the blood-scaled PBIF was prospectively applied to the subjects in Group 2 (V(T) ratio 1.02±0.05; mean±SD) and Group 3 (V(T) ratio 1.03±0.04). Equally accurate results were obtained for two subpopulations of subjects drawn from Groups 2 and 3 who had very differently shaped (i.e. "flatter" or "steeper") input functions compared to PBIF (V(T) ratio 1.07±0.04 and 0.99±0.04, respectively). Results obtained via PBIF were equivalent to those obtained via IDIF (V(T) ratio 0.99±0.05 and 1.00±0.04 for healthy subjects and MDD patients, respectively). Retest variability of PBIF was equivalent to that obtained with full input function and IDIF (14.5%, 15.2%, and 14.1%, respectively). Due to [(11)C](R)-rolipram arteriovenous differences, venous samples could not be substituted for arterial samples. With both IDIF and PBIF, depressed patients had a 20% reduction in [(11)C](R)-rolipram binding as compared to control (two-way ANOVA: p=0.008 and 0.005, respectively). These results were almost equivalent to those obtained using 23 arterial samples.

CONCLUSION

Although some arterial samples are still necessary, both PBIF and IDIF are accurate and precise alternatives to full arterial input function for [(11)C](R)-rolipram PET studies. Both techniques give accurate results with low variability, even for clinically different groups of subjects and those with very differently shaped input functions.

Minimally invasive input function for 2-18F-fluoro-A-85380 brain PET studies

PURPOSE

Quantitative neuroreceptor positron emission tomography (PET) studies often require arterial cannulation to measure input function. While population-based input function (PBIF) would be a less invasive alternative, it has only rarely been used in conjunction with neuroreceptor PET tracers. The aims of this study were (1) to validate the use of PBIF for 2-(18)F-fluoro-A-85380, a tracer for nicotinic receptors; (2) to compare the accuracy of measures obtained via PBIF to those obtained via blood-scaled image-derived input function (IDIF) from carotid arteries; and (3) to explore the possibility of using venous instead of arterial samples for both PBIF and IDIF.

METHODS

Ten healthy volunteers underwent a dynamic 2-(18)F-fluoro-A-85380 brain PET scan with arterial and, in seven subjects, concurrent venous serial blood sampling. PBIF was obtained by averaging the normalized metabolite-corrected arterial input function and subsequently scaling each curve with individual blood samples. IDIF was obtained from the carotid arteries using a blood-scaling method. Estimated Logan distribution volume (V(T)) values were compared to the reference values obtained from arterial cannulation.

RESULTS

For all subjects, PBIF curves scaled with arterial samples were similar in shape and magnitude to the reference arterial input function. The Logan V(T) ratio was 1.00 ± 0.05; all subjects had an estimation error <10%. IDIF gave slightly less accurate results (V(T) ratio 1.03 ± 0.07; eight of ten subjects had an error <10%). PBIF scaled with venous samples yielded inaccurate results (V(T) ratio 1.13 ± 0.13; only three of seven subjects had an error <10%). Due to arteriovenous differences at early time points, IDIF could not be calculated using venous samples.

CONCLUSION

PBIF scaled with arterial samples accurately estimates Logan V(T) for 2-(18)F-fluoro-A-85380. Results obtained with PBIF were slightly better than those obtained with IDIF. Due to arteriovenous concentration differences, venous samples cannot be substituted for arterial samples.

Dynamic single photon emission computed tomography--basic principles and cardiac applications

The very nature of nuclear medicine, the visual representation of injected radiopharmaceuticals, implies imaging of dynamic processes such as the uptake and wash-out of radiotracers from body organs. For years, nuclear medicine has been touted as the modality of choice for evaluating function in health and disease. This evaluation is greatly enhanced using single photon emission computed tomography (SPECT), which permits three-dimensional (3D) visualization of tracer distributions in the body. However, to fully realize the potential of the technique requires the imaging of in vivo dynamic processes of flow and metabolism. Tissue motion and deformation must also be addressed. Absolute quantification of these dynamic processes in the body has the potential to improve diagnosis. This paper presents a review of advancements toward the realization of the potential of dynamic SPECT imaging and a brief history of the development of the instrumentation. A major portion of the paper is devoted to the review of special data processing methods that have been developed for extracting kinetics from dynamic cardiac SPECT data acquired using rotating detector heads that move as radiopharmaceuticals exchange between biological compartments. Recent developments in multi-resolution spatiotemporal methods enable one to estimate kinetic parameters of compartment models of dynamic processes using data acquired from a single camera head with slow gantry rotation. The estimation of kinetic parameters directly from projection measurements improves bias and variance over the conventional method of first reconstructing 3D dynamic images, generating time-activity curves from selected regions of interest and then estimating the kinetic parameters from the generated time-activity curves. Although the potential applications of SPECT for imaging dynamic processes have not been fully realized in the clinic, it is hoped that this review illuminates the potential of SPECT for dynamic imaging, especially in light of new developments that enable measurement of dynamic processes directly from projection measurements.