A standardized blood sampling scheme in quantitative FDG-PET studies

Image-derived input functions from dynamic 15O-water PET scans using penalised reconstruction

BACKGROUND

Quantitative positron emission tomography (PET) scans of the brain typically require arterial blood sampling but this is complicated and logistically challenging. One solution to remove the need for arterial blood sampling is the use of image-derived input functions (IDIFs). Obtaining accurate IDIFs, however, has proved to be challenging, mainly due to the limited resolution of PET. Here, we employ penalised reconstruction alongside iterative thresholding methods and simple partial volume correction methods to produce IDIFs from a single PET scan, and subsequently, compare these to blood-sampled input curves (BSIFs) as ground truth. Retrospectively we used data from sixteen subjects with two dynamic ¹⁵O-labelled water PET scans and continuous arterial blood sampling: one baseline scan and another post-administration of acetazolamide.

RESULTS

IDIFs and BSIFs agreed well in terms of the area under the curve of input curves when comparing peaks, tails and peak-to-tail ratios with R² values of 0.95, 0.70 and 0.76, respectively. Grey matter cerebral blood flow (CBF) values showed good agreement with an average difference between the BSIF and IDIF CBF values of 2% ± and a coefficient of variation (CoV) of 7.3%.

CONCLUSION

Our results show promising results that a robust IDIF can be produced for dynamic ¹⁵O-water PET scans using only the dynamic PET scan images with no need for a corresponding MRI or complex analytical techniques and thereby making routine clinical use of quantitative CBF measurements with ¹⁵O-water feasible.

Noninvasive quantification of nonhuman primate dynamic 18F-FDG PET imaging

¹⁸F-FDG uptake rate constant K_i is the main physiology parameter measured in dynamic PET studies. A model-independent graphical analysis using Patlak plot with plasma input function (PIF) is a standard approach used to estimate K_i . The PIF is the ¹⁸F-FDG time activity curve (TAC) in plasma that is obtained by serial arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach with reduced blood samples for noninvasive quantification of dynamic ¹⁸F-FDG PET imaging. Eight 60 min rhesus monkey brain dynamic ¹⁸F-FDG PET scans with arterial blood samples were collected. The measured PIF (mPIF) was determined by arterial blood samples. TACs of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the population-based PIF (pPIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal sampling scheme with given blood sample size was determined by maximizing the correlations between the K_i estimated from pPIF and those obtained by mPIF. A leave-two-out cross-validation method was used for evaluation. The linear correlations between the K_i estimates from pPIF with optimal sampling schemes and those from mPIF were: K_i (pPIF 1 sample at 40 min) = 1.015 K_i (mPIF) - 0.000, R ² = 0.974; K_i (pPIF 2 samples at 35 and 50 min) = 1.052 K_i (mPIF) - 0.001, R ² = 0.976; K_i (pPIF 3 samples at 12, 40, and 50 min) = 1.030 K_i (mPIF) - 0.000, R ² = 0.985; and K_i (pPIF 4 samples at 10, 20, 40, and 50 min) = 1.016 K_i (mPIF)- 0.000, R ² = 0.993. As the sample size became greater or equal to 4, the K_i estimates from pPIF with the optimal protocol were almost identical to those from mPIF. The Patlak plot-based optimization approach is a reliable method to estimate PIF for noninvasive quantification of non-human primate dynamic ¹⁸F-FDG PET imaging and is potentially extendable to further translational human studies.

Analysis of hypoxia in human glioblastoma tumors with dynamic 18F-FMISO PET imaging

Gliomas are the most common type of primary brain tumors and are classified as grade IV. Necrosis and hypoxia are essential diagnostic features which result in poor prognosis of gliomas. The aim of this study was to report quantitative temporal analyses aiming at determining the hypoxic regions in glioblastoma multiforme and to suggest an optimal time for the clinical single scan of hypoxia. Nine subjects were imaged with PET and 18F-FMISO in dynamic mode for 15 min followed with static scans at 2, 3 and 4 h post-injection. Spectral analysis, tumor-to-blood ratio (TBR) and tumor-to-normal tissue ratio (TNR) were used to delimit perfused and hypoxic tumor regions. TBR and TNR images were further scaled by thresholding at 1.2, 1.4, 2 and 2.5 levels. The images showed a varying tumor volume with time. TBR produced broader images of the tumor than TNR considering the same thresholds on intensity. Spectral analysis reliably determined hypoxia with different degrees of perfusion. By comparing TBR and TNR with spectral analysis images, weak to moderate correlation coefficients were found for most thresholding values and imaging times (range: 0 to 0.69). Hypoxic volume (HV) estimated from the net uptake rate (K_i) were changing among imaging times. The minimum HV changes were found between 3 h and 4 h, confirming that after 3 h, there was a very low exchange of 81F-FMISO between blood and tumor. On the other hand, hypoxia started to dominate the perfused tissue at 90 min, suggesting this time is suitable for a single scan acquisition irrespective of tumor status being highly hypoxic or perfused. At this time, TBR and TNR were respectively found in the nine subjects as 1.72 ± 0.22 and 1.74 ± 0.19.

Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based algorithm

We present a novel segmentation algorithm for dynamic PET studies that groups pixels according to the similarity of their time-activity curves.

METHODS

Sixteen mice bearing a human tumor cell line xenograft (CH-157MN) were imaged with three different (68)Ga-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC) using a small animal PET-CT scanner. Regional activities (input function and tumor) were obtained after manual delineation of regions of interest over the image. The algorithm was implemented under the jClustering framework and used to extract the same regional activities as in the manual approach. The volume of distribution in the tumor was computed using the Logan linear method. A Kruskal-Wallis test was used to investigate significant differences between the manually and automatically obtained volumes of distribution.

RESULTS

The algorithm successfully segmented all the studies. No significant differences were found for the same tracer across different segmentation methods. Manual delineation revealed significant differences between DOTANOC and the other two tracers (DOTANOC - DOTATATE, p=0.020; DOTANOC - DOTATOC, p=0.033). Similar differences were found using the leader-follower algorithm.

CONCLUSION

An open implementation of a novel segmentation method for dynamic PET studies is presented and validated in rodent studies. It successfully replicated the manual results obtained in small-animal studies, thus making it a reliable substitute for this task and, potentially, for other dynamic segmentation procedures.

Kinetic modeling of PET-FDG in the brain without blood sampling

The aim in this work is to report a new method to calculate parametric images from a single scan acquisition with positron emission tomography (PET) and fluorodeoxyglucose (FDG) in the human brain without blood sampling. It is usually practical for research or clinical purposes to inject the patient in an isolated room and to start the PET acquisition only for some 10-20 min, about 30 min after FDG injection. In order to calculate the cerebral metabolic rates for glucose (CMRG), usually several blood samples are required. The proposed method considers the relation between the uptake of the tracer in the cerebellum as a reference tissue and the population based input curve. Similar results were obtained for CMRG values with the present method in comparison to the usual autoradiographic and the non-linear least squares fitting of regions of interest.