Patient-Specific Method of Generating Parametric Maps of Patlak K(i) without Blood Sampling or Metabolite Correction: A Feasibility Study.

Currently, kinetic analyses using dynamic positron emission tomography (PET) experience very limited use despite their potential for improving quantitative accuracy in several clinical and research applications. For targeted volume applications, such as radiation treatment planning, treatment monitoring, and cerebral metabolic studies, the key to implementation of these methods is the determination of an arterial input function, which can include time-consuming analysis of blood samples for metabolite correction. Targeted kinetic applications would become practical for the clinic if blood sampling and metabolite correction could be avoided. To this end, we developed a novel method (Patlak-P) of generating parametric maps that is identical to Patlak K(i) (within a global scalar multiple) but does not require the determination of the arterial input function or metabolite correction. In this initial study, we show that Patlak-P (a) mimics Patlak K(i) images in terms of visual assessment and target-to-background (TB) ratios of regions of elevated uptake, (b) has higher visual contrast and (generally) better image quality than SUV, and (c) may have an important role in improving radiotherapy planning, therapy monitoring, and neurometabolism studies

Direct estimation of kinetic parametric images for dynamic PET.

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed

P-glycoprotein at the blood-brain barrier: kinetic modeling of 11C-desmethylloperamide in mice using a 18F-FDG μPET scan to determine the input function

Purpose: The objective of this study is the implementation of a kinetic model for 11C-desmethylloperamide (11CdLop) and the determination of a typical parameter for P-glycoprotein (P-gp) functionality in mice. Since arterial blood sampling in mice is difficult, an alternative method to obtain the arterial plasma input curve used in the kinetic model is proposed. Methods: Wild-type (WT) mice (pre-injected with saline or cyclosporine) and P-gp knock-out (KO) mice were injected with 20 MBq of 11C-dLop, and a dynamic μPET scan was initiated. Afterwards, 18.5 MBq of 18F-FDG was injected, and a static μPET scan was started. An arterial input and brain tissue curve was obtained by delineation of an ROI on the left heart ventricle and the brain, respectively based on the 18F-FDG scan. Results: A comparison between the arterial input curves obtained by the alternative and the blood sampling method showed an acceptable agreement. The one-tissue compartment model gives the best results for the brain. In WT mice, the K1/k2 ratio was 0.4 ± 0.1, while in KO mice and cyclosporine-pretreated mice the ratio was much higher (2.0 ± 0.4 and 1.9 ± 0.2, respectively). K1 can be considered as a pseudo value K1, representing a combination of passive influx of 11C-desmethylloperamide and a rapid washout by P-glycoprotein, while k2 corresponds to slow passive efflux out of the brain. Conclusions: An easy to implement kinetic modeling for imaging P-glycoprotein function is presented in mice without arterial blood sampling. The ratio of K1/k2 obtained from a one-tissue compartment model can be considered as a good value for P-glycoprotein functionality

A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements : theory and simulation study

Author name used in this publication: Dagan FengVersion of RecordPublishe

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner.

BACKGROUND Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling. METHODS Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65, and 55-65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed. RESULTS There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55-65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved  0.99 and peak signal-to-noise ratio > 55 dB. CONCLUSION We demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner

Information technology applications in biomedical functional imaging

Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringVersion of RecordPublishe

Simultaneous estimation of physiological parameters and the input function : in vivo PET data

Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Dynamic imaging and tracer kinetic modeling for emission tomography using rotating detectors

Author name used in this publication: Dagan FengAuthor name used in this publication: Daniel Pak-Kong LunCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringVersion of RecordPublishe