Image-derived input function in PET brain studies: blood-based methods are resistant to motion artifacts

Assessment of [18F]PI-2620 Tau-PET Quantification via Non-Invasive Automatized Image Derived Input Function

PURPOSE

[18F]PI-2620 positron emission tomography (PET) detects misfolded tau in progressive supranuclear palsy (PSP) and Alzheimer's disease (AD). We questioned the feasibility and value of absolute [18F]PI-2620 PET quantification for assessing tau by regional distribution volumes (VT). Here, arterial input functions (AIF) represent the gold standard, but cannot be applied in routine clinical practice, whereas image-derived input functions (IDIF) represent a non-invasive alternative. We aimed to validate IDIF against AIF and we evaluated the potential to discriminate patients with PSP and AD from healthy controls by non-invasive quantification of [18F] PET.

METHODS

In the first part of the study, we validated AIF derived from radial artery whole blood against IDIF by investigating 20 subjects (ten controls and ten patients). IDIF were generated by manual extraction of the carotid artery using the average and the five highest (max5) voxel intensity values and by automated extraction of the carotid artery using the average and the maximum voxel intensity value. In the second part of the study, IDIF quantification using the IDIF with the closest match to the AIF was transferred to group comparison of a large independent cohort of 40 subjects (15 healthy controls, 15 PSP patients and 10 AD patients). We compared VT and VT ratios, both calculated by Logan plots, with distribution volume (DV) ratios using simplified reference tissue modelling and standardized uptake value (SUV) ratios.

RESULTS

AIF and IDIF showed highly correlated input curves for all applied IDIF extraction methods (0.78 < r < 0.83, all p < 0.0001; area under the curves (AUC): 0.73 < r ≤ 0.82, all p ≤ 0.0003). Regarding the VT values, correlations were mainly found between those generated by the AIF and by the IDIF methods using the maximum voxel intensity values. Lowest relative differences (RD) were observed by applying the manual method using the five highest voxel intensity values (max5) (AIF vs. IDIF manual, avg: RD = -82%; AIF vs. IDIF automated, avg: RD = -86%; AIF vs. IDIF manual, max5: RD = -6%; AIF vs. IDIF automated, max: RD = -26%). Regional VT values revealed considerable variance at group level, which was strongly reduced upon scaling by the inferior cerebellum. The resulting VT ratio values were adequate to detect group differences between patients with PSP or AD and healthy controls (HC) (PSP target region (globus pallidus): HC vs. PSP vs. AD: 1.18 vs. 1.32 vs. 1.16; AD target region (Braak region I): HC vs. PSP vs. AD: 1.00 vs. 1.00 vs. 1.22). VT ratios and DV ratios outperformed SUV ratios and VT in detecting differences between PSP and healthy controls, whereas all quantification approaches performed similarly in comparing AD and healthy controls.

CONCLUSION

Blood-free IDIF is a promising approach for quantification of [18F]PI-2620 PET, serving as correlating surrogate for invasive continuous arterial blood sampling. Regional [18F]PI-2620 VT show large variance, in contrast to regional [18F]PI-2620 VT ratios scaled with the inferior cerebellum, which are appropriate for discriminating PSP, AD and healthy controls. DV ratios obtained by simplified reference tissue modeling are similarly suitable for this purpose.

Evaluation of Non-Invasive Methods for (R)-[11C]PK11195 PET Image Quantification in Multiple Sclerosis

This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[11C]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

caliPER: A software for blood-free parametric Patlak mapping using PET/MRI input function

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modelling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (K_i) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [¹⁸F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The K_i and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

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.

Blood perfusion in osteomyelitis studied with [15O]water PET in a juvenile porcine model

Background

Osteomyelitis is a serious disease which can be difficult to treat despite properly instituted antibiotic therapy. This appears to be related at least partly to degraded vascularisation in the osteomyelitic (OM) lesions. Studies of perfusion in OM bones are, however, few and not quantitative. Quantitative assessment of perfusion could aid in the selection of therapy. A non-invasive, quantitative way to study perfusion is dynamic [¹⁵O]water positron emission tomography (PET). We aim to demonstrate that the method can be used for measuring perfusion in OM lesions and hypothesize that perfusion will be less elevated in OM lesions than in soft tissue (ST) infection. The study comprised 11 juvenile pigs with haematogenous osteomyelitis induced by injection of Staphylococcus aureus into the right femoral artery 1 week before scanning (in one pig, 2 weeks). The pigs were dynamically PET scanned with [¹⁵O]water to quantify blood perfusion. OM lesions (N = 17) in long bones were studied, using the left limb as reference. ST lesions (N = 8) were studied similarly.

Results

Perfusion was quantitatively determined. Perfusion was elevated by a factor 1.5 in OM lesions and by a factor 6 in ST lesions.

Conclusions

Blood perfusion was successfully determined in pathological subacute OM lesions; average perfusion was increased compared to that in a healthy bone, but as hypothesized, the increase was less than in ST lesions, indicating that the infected bone has less perfusion reserve than the infected soft tissue.

Electronic supplementary material

The online version of this article (doi:10.1186/s13550-016-0251-2) contains supplementary material, which is available to authorized users.

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.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [¹¹C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [¹¹C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (V_T/f_P) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V_T/f_P error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [¹¹C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-V_T/f_P in [¹¹C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

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.