Toward noninvasive quantification of brain radioligand binding by combining electronic health records and dynamic PET imaging data

Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model. Phys Med Biol 2024;69:165008. [PMID: 38830366 PMCID: PMC11288174 DOI: 10.1088/1361-6560/ad539e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Objective.In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically.Approach.We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example.Main results.We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels.Significance.The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis.

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (V_T, DVR, or BP_ND for reversible tracers; K_i or CMR_glu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R² = 0.59-1.00, R² = 0.71-1.00, R² = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R² = 0.71-1.00 and R² = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.

Quantification of Positron Emission Tomography Data Using Simultaneous Estimation of the Input Function: Validation with Venous Blood and Replication of Clinical Studies

PURPOSE

To determine if one venous blood sample can substitute full arterial sampling in quantitative modeling for multiple positron emission tomography (PET) radiotracers using simultaneous estimation of the input function (SIME).

PROCEDURES

Participants underwent PET imaging with [¹¹C]ABP688, [¹¹C]CUMI-101, and [¹¹C]DASB. Full arterial sampling and additional venous blood draws were performed for quantification with the arterial input function (AIF) and SIME using one arterial or venous (vSIME) sample.

RESULTS

Venous and arterial metabolite-corrected plasma activities were within 6 % of each other at varying time points. vSIME- and AIF-derived outcome measures were in good agreement, with optimal sampling times of 12 min ([¹¹C]ABP688), 90 min ([¹¹C]CUMI-101), and 100 min ([¹¹C]DASB). Simulation-based power analyses revealed that SIME required fewer subjects than the AIF method to achieve statistical power, with significant reductions for [¹¹C]CUMI-101 and [¹¹C]DASB with vSIME. Replication of previous findings and test-retest analyses bolstered the simulation analyses.

CONCLUSIONS

We demonstrate the feasibility of AIF recovery using SIME with one venous sample for [¹¹C]ABP688, [¹¹C]CUMI-101, and [¹¹C]DASB. This method simplifies PET acquisition while allowing for fully quantitative modeling, although some variability and bias are present with respect to AIF-based quantification, which may depend on the accuracy of the single venous blood measurement.

Big data for health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.