Optimization of temporal sampling for 18F-choline uptake quantification in prostate cancer assessment

Optimization of time frame binning for FDOPA uptake quantification in glioma

Introduction

3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (FDOPA) uptake quantification in glioma assessment can be distorted using a non-optimal time frame binning of time-activity curves (TAC). Under-sampling or over-sampling dynamic PET images induces significant variations on kinetic parameters quantification. We aimed to optimize temporal time frame binning for dynamic FDOPA PET imaging.

Methods

Fourteen patients with 33 tumoral TAC with biopsy-proven gliomas were analysed. The mean SUVmax tumor-to-brain ratio (TBRmax) were compared at 20 min and 35 min post-injection (p.i). Five different time frame samplings within 20 min were compared: 11x10sec-6x15sec-5x20sec-3x300sec; 8x15sec– 2x30sec– 2x60sec– 3x300sec; 6x20sec– 8x60sec– 2x300sec; 10x30sec– 3x300sec and 4x45sec– 3x90sec– 5x150sec. The reversible single-tissue compartment model with blood volume parameter (VB) was selected using the Akaike information criterion. K1 values extracted from 1024 noisy simulated TAC using Monte Carlo method from the 5 different time samplings were compared to a target K1 value as the objective, which is the average of the K1 values extracted from the 33 lesions using an imaging-derived input function for each patient.

Results

The mean TBRmax was significantly higher at 20 min p.i. than at 35 min p.i (respectively 1.4 +/- 0.8 and 1.2 +/- 0.6; p <0.001). The target K1 value was 0.161 mL/ccm/min. The 8x15sec– 2x30sec– 2x60sec– 3x300sec time sampling was the optimal time frame binning. K1 values extracted using this optimal time frame binning were significantly different with K1 values extracted from the other time frame samplings, except with K1 values obtained using the 11x10sec– 6x15sec –5x20sec-3x300sec time frame binning.

Conclusions

This optimal sampling schedule design (8x15sec– 2x30sec– 2x60sec– 3x300sec) could be used to minimize bias in quantification of FDOPA uptake in glioma using kinetic analysis.

A simulation-based method to determine optimal sampling schedules for dosimetry in radioligand therapy

AIM

For dosimetry in radioligand therapy, the time-integrated activity coefficients (TIACs) for organs at risk and for tumour lesions have to be determined. The used sampling scheme affects the TIACs and therefore the calculated absorbed doses. The aim of this work was to develop a general and flexible method, which analyses numerous clinically applicable sampling schedules using true time-activity curves (TACs) of virtual patients.

METHOD

Nine virtual patients with true TACs of the tumours were created using a physiologically-based pharmacokinetic (PBPK) model and individual biokinetic data of five patients with neuroendocrine tumours and four with meningioma. ¹¹¹In-DOTATATE was used for pre-therapeutic dosimetry. In total, 15,120 sampling schemes, each consisting of 4 time points, were investigated. Gaussian noise of different levels was added to the corresponding true time-activity points. A bi-exponential function was used to fit the simulated time-activity data. For each investigated sampling schedule, 1000 replications were performed. Patient-specific and population-specific optimal sampling schedules were determined using the relative root-mean-square error (rRMSE). Furthermore, the fractions of TIACs a˜ deviating >5% (f_Δa˜>5%) and >10% (f_Δa˜>10%) from the true TIAC a˜_true were used for additional evaluations e.g. to investigate the effect of varying single time points.

RESULTS

Almost all patient-specific and all population-specific optimal sampling schedules have t₄≥96h for all noise levels. Changing the latest time point from the population-specific optimal time to e.g. 48h leads to a median increase of f_Δa˜>10% from 0.1% to 88% for the lowest investigated noise level. Using the determined population-specific optimal schedules, results in more accurate and precise results than established schedules from the literature.

CONCLUSION

A method of determining the optimal sampling schedule for dosimetry, which considers clinical working hours and measurement uncertainties, has been developed and applied. The simulation study shows that optimised sampling schedules result in high accuracy and precision of the determined TIACs.

Comparison of 18F-Choline PET/CT and MRI functional parameters in prostate cancer

AIM

¹⁸F-Choline (FCH) uptake parameters are strong indicators of aggressive disease in prostate cancer. Functional parameters derived by magnetic resonance imaging (MRI) are also correlated to aggressive disease. The aim of this work was to evaluate the relationship between metabolic parameters derived by FCH PET/CT and functional parameters derived by MRI.

MATERIALS AND METHODS

Fourteen patients with proven prostate cancer who underwent FCH PET/CT and multiparametric MRI were enrolled. FCH PET/CT consisted in a dual phase: early pelvic list-mode acquisition and late whole-body acquisition. FCH PET/CT and multiparametric MRI examinations were registered and tumoral volume-of-interest were drawn on the largest lesion visualized on the apparent diffusion coefficient (ADC) map and projected onto the different multiparametric MR images and FCH PET/CT images. Concerning the FCH uptake, kinetic parameters were extracted with the best model selected using the Akaike information criterion between the one- and two-tissue compartment models with an imaging-derived plasma input function. Other FCH uptake parameters (early SUVmean and late SUVmean) were extracted. Concerning functional parameters derived by MRI scan, cell density (ADC from diffusion weighting imaging) and vessel permeability (K_trans and V_e using the Tofts pharmakinetic model from dynamic contrast-enhanced imaging) parameters were extracted. Spearman's correlation coefficients were calculated to compare parameters.

RESULTS

The one-tissue compartment model for kinetic analysis of PET images was selected. Concerning correlation analysis between PET parameters, K₁ was highly correlated with early SUVmean (r = 0.83, p < 0.001) and moderately correlated with late SUVmean (r = 0.66, p = 0.010) and early SUVmean was highly correlated with late SUVmean (r = 0.90, p < 0.001). No significant correlation was found between functional MRI parameters. Concerning correlation analysis between PET and functional MRI parameters, K₁ (from FCH PET/CT imaging) was moderately correlated with K_trans (from perfusion MR imaging) (r = 0.55, p = 0.041).

CONCLUSIONS

No significant correlation was found between FCH PET/CT and multiparametric MRI metrics except FCH influx which is moderately linked to the vessel permeability in prostate cancer.