Image-derived input function with factor analysis and a-priori information

Simulation study of a coincidence detection system for non-invasive determination of arterial blood time-activity curve measurements

Background

Arterial sampling in PET studies for the purposes of kinetic modeling remains an invasive, time-intensive, and expensive procedure. Alternatives to derive the blood time-activity curve (BTAC) non-invasively are either reliant on large vessels in the field of view or are laborious to implement and analyze as well as being prone to many processing errors. An alternative method is proposed in this work by the simulation of a non-invasive coincidence detection unit.

Results

We utilized GATE simulations of a human forearm phantom with a blood flow model, as well as a model for dynamic radioactive bolus activity concentration based on clinical measurements. A fixed configuration of 14 and, also separately, 8 detectors were employed around the phantom, and simulations were performed to investigate signal detection parameters. Bismuth germanate (BGO) crystals proved to show the highest count rate capability and sensitivity to a simulated BTAC with a maximum coincidence rate of 575 cps. Repeatable location of the blood vessels in the forearm allowed a half-ring design with only 8 detectors. Using this configuration, maximum coincident rates of 250 cps and 42 cps were achieved with simulation of activity concentration determined from ¹⁵O and ¹⁸F arterial blood sampling. NECR simulated in a water phantom at 3 different vertical positions inside the 8-detector system (Y = − 1 cm, Y = − 2 cm, and Y = −3 cm) was 8360 cps, 13,041 cps, and 20,476 cps at an activity of 3.5 MBq. Addition of extra axial detection rings to the half-ring configuration provided increases in system sensitivity by a factor of approximately 10.

Conclusions

Initial simulations demonstrated that the configuration of a single half-ring 8 detector of monolithic BGO crystals could describe the simulated BTAC in a clinically relevant forearm phantom with good signal properties, and an increased number of axial detection rings can provide increased sensitivity of the system. The system would find use in the derivation of the BTAC for use in the application of kinetic models without physical arterial sampling or reliance on image-based techniques.

Longitudinal mouse-PET imaging: a reliable method for estimating binding parameters without a reference region or blood sampling

Abstract

Longitudinal mouse PET imaging is becoming increasingly popular due to the large number of transgenic and disease models available but faces challenges. These challenges are related to the small size of the mouse brain and the limited spatial resolution of microPET scanners, along with the small blood volume making arterial blood sampling challenging and impossible for longitudinal studies. The ability to extract an input function directly from the image would be useful for quantification in longitudinal small animal studies where there is no true reference region available such as TSPO imaging.

Methods

Using dynamic, whole-body ¹⁸F-DPA-714 PET scans (60 min) in a mouse model of hippocampal sclerosis, we applied a factor analysis (FA) approach to extract an image-derived input function (IDIF). This mouse-specific IDIF was then used for 4D-resolution recovery and denoising (4D-RRD) that outputs a dynamic image with better spatial resolution and noise properties, and a map of the total volume of distribution (V_T) was obtained using a basis function approach in a total of 9 mice with 4 longitudinal PET scans each. We also calculated percent injected dose (%ID) with and without 4D-RRD. The V_T and %ID parameters were compared to quantified ex vivo autoradiography using regional correlations of the specific binding from autoradiography against V_T and %ID parameters.

Results

The peaks of the IDIFs were strongly correlated with the injected dose (Pearson R = 0.79). The regional correlations between the %ID estimates and autoradiography were R = 0.53 without 4D-RRD and 0.72 with 4D-RRD over all mice and scans. The regional correlations between the V_T estimates and autoradiography were R = 0.66 without 4D-RRD and 0.79 with application of 4D-RRD over all mice and scans.

Conclusion

We present a FA approach for IDIF extraction which is robust, reproducible and can be used in quantification methods for resolution recovery, denoising and parameter estimation. We demonstrated that the proposed quantification method yields parameter estimates closer to ex vivo measurements than semi-quantitative methods such as %ID and is immune to tracer binding in tissue unlike reference tissue methods. This approach allows for accurate quantification in longitudinal PET studies in mice while avoiding repeated blood sampling.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04755-5) contains supplementary material, which is available to authorized users.

Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates

Absolute quantification of PET brain imaging requires the measurement of an arterial input function (AIF), typically obtained invasively via an arterial cannulation. We present an approach to automatically calculate an image-derived input function (IDIF) and cerebral metabolic rates of glucose (CMRGlc) from the [18F]FDG PET data using an integrated PET/MRI system. Ten healthy controls underwent test-retest dynamic [18F]FDG-PET/MRI examinations. The imaging protocol consisted of a 60-min PET list-mode acquisition together with a time-of-flight MR angiography scan for segmenting the carotid arteries and intermittent MR navigators to monitor subject movement. AIFs were collected as the reference standard. Attenuation correction was performed using a separate low-dose CT scan. Assessment of the percentage difference between area-under-the-curve of IDIF and AIF yielded values within ±5%. Similar test-retest variability was seen between AIFs (9 ± 8) % and the IDIFs (9 ± 7) %. Absolute percentage difference between CMRGlc values obtained from AIF and IDIF across all examinations and selected brain regions was 3.2% (interquartile range: (2.4-4.3) %, maximum < 10%). High test-retest intravariability was observed between CMRGlc values obtained from AIF (14%) and IDIF (17%). The proposed approach provides an IDIF, which can be effectively used in lieu of AIF.

Dynamic 18F-FLT PET imaging of spatiotemporal changes in tumor cell proliferation and vasculature reveals the mechanistic actions of anti-angiogenic therapy

Anti-angiogenic therapies target tumor vasculature and tumor cells, thus a concurrent assessment of these targets would lead to a greater understanding of therapeutic resistance and facilitate development of improved therapeutic strategies. We utilize dynamic 3'-deoxy-3'-¹⁸F-fluorothymidine positron emission tomography (¹⁸F-FLT PET) scanning to concurrently assess changes in tumor cell proliferation and vasculature during anti-angiogenic therapy, providing insight into how these therapies may be used effectively with combination chemotherapy. Thirty-three patients with advanced solid malignancies underwent treatment with vascular endothelial growth factor receptor inhibitor (VEGFR-TKI) axitinib on an intermittent schedule (two-weeks-on/one-week-off). Patients had up to three dynamic ¹⁸F-FLT PET/CT scans: at baseline, after two weeks of continuous VEGFR-TKI treatment, and following a one week treatment break. ¹⁸F-FLT kinetics were analyzed using a two-tissue compartment kinetic model. Kinetic parameters V _b and K ₁ were extracted to quantify changes in tumor vasculature and the ¹⁸F-FLT flux constant K _i was calculated to quantify changes in tumor cell proliferation. Two weeks of continuous axitinib exposure led to decreases in V _b (median -21%, P  =  0.07), K ₁ (median -39%, P  <  0.01), and K _i (median -37%, P  <  0.01), corresponding to diminished tumor vasculature and cell proliferation that may antagonize treatment with concurrent chemotherapy. Axitinib treatment breaks led to significant increases in V _b (median  +42%, P  <  0.01), K ₁ (median  +46%, P  <  0.01), and K _i (median  +39%, P  <  0.01) that is suggestive of an optimal time to schedule synergistic chemotherapy. Significant negative correlations (rho  ⩽  -0.70, P  <  0.01) were found between changes in tumor vasculature during axitinib exposure weeks and changes in tumor vasculature during treatment breaks. Imaging with dynamic ¹⁸F-FLT PET revealed new insights relating to the interplay of vascular and proliferative pharmacodynamics of axitinib therapy, facilitating a greater understanding of the mechanistic actions of VEGFR-TKIs. Increases in tumor vasculature and cell proliferation during VEGFR-TKI treatment breaks, suggests this period is an optimal time to schedule synergistic chemotherapy and warrants further investigation.

Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping

15O-H2O PET imaging is an accurate method to measure cerebral blood flow (CBF) but it requires an arterial input function (AIF). Historically, image-derived AIF estimation suffers from low temporal resolution, spill-in, and spill-over problems. Here, we optimized tracer dose on a time-of-flight PET/MR according to the acquisition-specific noise-equivalent count rate curve. An optimized dose of 850 MBq of 15O-H2O was determined, which allowed sufficient counts to reconstruct a short time-frame PET angiogram (PETA) during the arterial phase. This PETA enabled the measurement of the extent of spill-over, while an MR angiogram was used to measure the true arterial volume for AIF estimation. A segment of the high cervical arteries outside the brain was chosen, where the measured spill-in effects were minimal. CBF studies were performed twice with separate [15O]-H2O injections in 10 healthy subjects, yielding values of 88 ± 16, 44 ± 9, and 58 ± 11 mL/min/100 g for gray matter, white matter, and whole brain, with intra-subject CBF differences of 5.0 ± 4.0%, 4.1 ± 3.3%, and 4.5 ± 3.7%, respectively. A third CBF measurement after the administration of 1 g of acetazolamide showed 35 ± 23%, 29 ± 20%, and 33 ± 22% increase in gray matter, white matter, and whole brain, respectively. Based on these findings, the proposed noninvasive AIF method provides robust CBF measurement with 15O-H2O PET.

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.

Fully automated calculation of image-derived input function in simultaneous PET/MRI in a sheep model

Background

Obtaining the arterial input function (AIF) from image data in dynamic positron emission tomography (PET) examinations is a non-invasive alternative to arterial blood sampling. In simultaneous PET/magnetic resonance imaging (PET/MRI), high-resolution MRI angiographies can be used to define major arteries for correction of partial-volume effects (PVE) and point spread function (PSF) response in the PET data. The present study describes a fully automated method to obtain the image-derived input function (IDIF) in PET/MRI. Results are compared to those obtained by arterial blood sampling.

Methods

To segment the trunk of the major arteries in the neck, a high-resolution time-of-flight MRI angiography was postprocessed by a vessel-enhancement filter based on the inertia tensor. Together with the measured PSF of the PET subsystem, the arterial mask was used for geometrical deconvolution, yielding the time-resolved activity concentration averaged over a major artery. The method was compared to manual arterial blood sampling at the hind leg of 21 sheep (animal stroke model) during measurement of blood flow with O15-water. Absolute quantification of activity concentration was compared after bolus passage during steady state, i.e., between 2.5- and 5-min post injection. Cerebral blood flow (CBF) values from blood sampling and IDIF were also compared.

Results

The cross-calibration factor obtained by comparing activity concentrations in blood samples and IDIF during steady state is 0.98 ± 0.10. In all examinations, the IDIF provided a much earlier and sharper bolus peak than in the time course of activity concentration obtained by arterial blood sampling. CBF using the IDIF was 22 % higher than CBF obtained by using the AIF yielded by blood sampling.

Conclusions

The small deviation between arterial blood sampling and IDIF during steady state indicates that correction of PVE and PSF is possible with the method presented. The differences in bolus dynamics and, hence, CBF values can be explained by the different sampling locations (hind leg vs. major neck arteries) with differences in delay/dispersion. It will be the topic of further work to test the method on humans with the perspective of replacing invasive blood sampling by an IDIF using simultaneous PET/MRI.

Image-derived input function with factor analysis and a-priori information

BACKGROUND

Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies.

MATERIALS AND METHODS

IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT).

RESULTS

The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan.

CONCLUSION

A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.