Dataset Information

Dual time point based quantification of metabolic uptake rates in 18F-FDG PET.

ABSTRACT:

Background

Assessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.

Methods

Starting from the Patlak equation, it is shown that Km?mt/ca0+V?r/?a, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t?=?t0. ca0=ca(t0) denotes arterial tracer concentration, V?r is an estimate of the Patlak intercept, and ?a is the time constant of the ca(t) decrease. We compared the theoretical predictions with the observed relation between Ks=mt/ca0 and Km in a group of nine patients with liver metastases of colorectal cancer for which dynamic scans were available, and Km was derived from conventional Patlak analysis. Twenty-two lesion regions of interest (ROIs) were evaluated. ca(t) was determined from a three-dimensional ROI in the aorta. Furthermore, the correlation between Km and late standard uptake value (SUV) as well as retention index was investigated. Additionally, feasibility of the approach was demonstrated in a whole-body investigation.

Results

Patlak analysis yielded a mean Vr of V?r=0.53±0.08 ml/ml. The patient averaged ?a was 99?±?23 min. Linear regression between Patlak-derived Km and DTP-derived Ks according to Ks?=?b?·?Km?+?a yielded b?=?0.98?±?0.05 and a?=?-0.0054?±?0.0013 ml/min/ml (r?=?0.98) in full accordance with the theoretical predictions b?=?1 and a?-V?r/?a. Ks exhibits better correlation with Km than late SUV and retention index, respectively. Ks(c)=Ks+V?r/?a is proposed as a quantitative estimator of Km which is independent of patient weight, scan time, and scanner calibration.

Conclusion

Quantification of Km from dual time point measurements compatible with clinical routine is feasible. The proposed approach eliminates the issues of static SUV and conventional DTP imaging regarding influence of chosen scanning times and inter-study variability of the input function. Ks and Ks(c) exhibit improved stability and better correlation with the true Km. These properties might prove especially relevant in the context of radiation treatment planning and therapy response control.

SUBMITTER: den Hoff Jv

PROVIDER: S-EPMC3717002 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dual time point based quantification of metabolic uptake rates in 18F-FDG PET.

den Hoff Jörg van Jv Hofheinz Frank F Oehme Liane L Schramm Georg G Langner Jens J Beuthien-Baumann Bettina B Steinbach Jörg J Kotzerke Jörg J

EJNMMI research 20130313 1

<h4>Background</h4>Assessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.<h4>Methods</h4>Starting from the Patlak equation, it is shown that Km≈mt/ca0+V̄r/τa, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t = t0. ca0=ca(t0) denotes arterial tracer concentration, V ...[more]

PMID: 23497553

Similar Datasets

Project description:IntroductionMetabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer's disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities.MethodFirst, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD.ResultsOur results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters.ConclusionWe proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

Project description:We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Project description:BACKGROUND:A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [18F]flutemetamol and [18F]florbetaben studies. METHODS:Rate constants for subjects across the Alzheimer's disease spectrum (i.e., non-displaceable binding potential (BPND) in the range 0.02-0.77 and 0.02-1.04 for [18F]flutemetamol and [18F]florbetaben, respectively) were established based on clinical [18F]flutemetamol (N?=?6) and [18F]florbetaben (N?=?20) data, and used to simulate tissue time-activity curves (TACs) of 110?min using a reference tissue and plasma input model. Next, noise was added (N?=?50) and data points corresponding to different intervals were removed from the TACs, ranging from 0 (i.e., 90-90?=?full-kinetic curve) to 80 (i.e., 10-90) minutes, creating a dual-time-window. Resulting TACs were fitted using the simplified reference tissue method (SRTM) to estimate the BPND, outliers (??1.5?×?BPND max) were removed and the bias was assessed using the distribution volume ratio (DVR?=?BPND?+?1). To this end, acceptability curves, which display the fraction of data below a certain bias threshold, were generated and the area under those curves were calculated. RESULTS:[18F]Flutemetamol and [18F]florbetaben data demonstrated an increased bias in amyloid estimate for larger intervals and higher noise levels. An acceptable bias (??3.1%) in DVR could be obtained with all except the 10-90 and 20-90-min intervals. Furthermore, a reduced fraction of acceptable data and most outliers were present for these two largest intervals (maximum percentage outliers 48 and 32 for [18F]flutemetamol and [18F]florbetaben, respectively). CONCLUSIONS:The length of the interval inversely correlates with the accuracy of the BPND estimates. Consequently, a dual-time-window protocol of 0-30 and 90-110?min (=maximum of 60?min interval) allows for accurate estimation of BPND values for both tracers. [18F]flutemetamol: EudraCT 2007-000784-19, registered 8 February 2007, [18F]florbetaben: EudraCT 2006-003882-15, registered 2006.

Dataset Information

Dual time point based quantification of metabolic uptake rates in 18F-FDG PET.

Background

Methods

Results

Conclusion

Publications

Dual time point based quantification of metabolic uptake rates in 18F-FDG PET.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets