Assessment of different quantification metrics of [18F]-NaF PET/CT images of patients with abdominal aortic aneurysm

BACKGROUND

We aim to assess the spill-in effect and the benefit in quantitative accuracy for [18F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) using the background correction (BC) technique.

METHODS

Seventy-two datasets of patients diagnosed with AAA were reconstructed with ordered subset expectation maximization algorithm incorporating point spread function (PSF). Spill-in effect was investigated for the entire aneurysm (AAA), and part of the aneurysm excluding the region close to the bone (AAAexc). Quantifications of PSF and PSF+BC images using different thresholds (% of max. SUV in target regions-of-interest) to derive target-to-background (TBR) values (TBRmax, TBR90, TBR70 and TBR50) were compared at 3 and 10 iterations.

RESULTS

TBR differences were observed between AAA and AAAexc due to spill-in effect from the bone into the aneurysm. TBRmax showed the highest sensitivity to the spill-in effect while TBR50 showed the least. The spill-in effect was reduced at 10 iterations compared to 3 iterations, but at the expense of reduced contrast-to-noise ratio (CNR). TBR50 yielded the best trade-off between increased CNR and reduced spill-in effect. PSF+BC method reduced TBR sensitivity to spill-in effect, especially at 3 iterations, compared to PSF (P-value ≤ 0.05).

CONCLUSION

TBR50 is robust metric for reduced spill-in and increased CNR.

Iterative reconstruction incorporating background correction improves quantification of [18F]-NaF PET/CT images of patients with abdominal aortic aneurysm

BACKGROUND

A confounding issue in [18F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) is the spill in contamination from the bone into the aneurysm. This study investigates and corrects for this spill in contamination using the background correction (BC) technique without the need to manually exclude the part of the AAA region close to the bone.

METHODS

Seventy-two (72) datasets of patients with AAA were reconstructed with the standard ordered subset expectation maximization (OSEM) algorithm incorporating point spread function (PSF) modelling. The spill in effect in the aneurysm was investigated using two target regions of interest (ROIs): one covering the entire aneurysm (AAA), and the other covering the aneurysm but excluding the part close to the bone (AAAexc). ROI analysis was performed by comparing the maximum SUV in the target ROI (SUVmax(T)), the corrected cSUVmax (SUVmax(T) - SUVmean(B)) and the target-to-blood ratio (TBR = SUVmax(T)/SUVmean(B)) with respect to the mean SUV in the right atrium region.

RESULTS

There is a statistically significant higher [18F]-NaF uptake in the aneurysm than normal aorta and this is not correlated with the aneurysm size. There is also a significant difference in aneurysm uptake for OSEM and OSEM + PSF (but not OSEM + PSF + BC) when quantifying with AAA and AAAexc due to the spill in from the bone. This spill in effect depends on proximity of the aneurysms to the bone as close aneurysms suffer more from spill in than farther ones.

CONCLUSION

The background correction (OSEM + PSF + BC) technique provided more robust AAA quantitative assessments regardless of the AAA ROI delineation method, and thus it can be considered as an effective spill in correction method for [18F]-NaF AAA studies.

Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm

Abdominal aortic aneurysm (AAA) monitoring and risk of rupture is currently assumed to be correlated with the aneurysm diameter. Aneurysm growth, however, has been demonstrated to be unpredictable. Using PET to measure uptake of [¹⁸F]-NaF in calcified lesions of the abdominal aorta has been shown to be useful for identifying AAA and to predict its growth. The PET low spatial resolution, however, can affect the accuracy of the diagnosis. Advanced edge-preserving reconstruction algorithms can overcome this issue. The kernel method has been demonstrated to provide noise suppression while retaining emission and edge information. Nevertheless, these findings were obtained using simulations, phantoms and a limited amount of patient data. In this study, the authors aim to investigate the usefulness of the anatomically guided kernelized expectation maximization (KEM) and the hybrid KEM (HKEM) methods and to judge the statistical significance of the related improvements. Sixty-one datasets of patients with AAA and 11 from control patients were reconstructed with ordered subsets expectation maximization (OSEM), HKEM and KEM and the analysis was carried out using the target-to-blood-pool ratio, and a series of statistical tests. The results show that all algorithms have similar diagnostic power, but HKEM and KEM can significantly recover uptake of lesions and improve the accuracy of the diagnosis by up to 22% compared to OSEM. The same improvements are likely to be obtained in clinical applications based on the quantification of small lesions, like for example cancer. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

INTRODUCTION

Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF.

MATERIALS AND METHODS

Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed.

RESULTS

Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%).

CONCLUSIONS

PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

Comparison of Correction Techniques for the Spill in Effect in Emission Tomography

In positron emission tomography (PET) imaging, accurate clinical assessment is often affected by the partial volume effect (PVE) leading to overestimation (spill-in) or underestimation (spill-out) of activity in various small regions. The spill-in correction, in particular, can be very challenging when the target region is close to a hot background region. Therefore, this study evaluates and compares the performance of various recently developed spill-in correction techniques, namely: background correction (BC), local projection (LP), and hybrid kernelized (HKEM) methods. We used a simulated digital phantom and [¹⁸F]-NaF PET data of three patients with abdominal aortic aneurysms (AAA) acquired with Siemens Biograph mMR™ and mCT™ scanners respectively. Region of Interest (ROI) analysis was performed and the extracted SUV _mean , SUV _max and target-to-background ratio (TBR) scores were compared. Results showed substantial spill-in effects from hot regions to targeted regions, which are more prominent in small structures. The phantom experiment demonstrated the feasibility of spill-in correction with all methods. For the patient data, large differences in SUV _mean , SUV _max and TBR _max scores were observed between the ROIs drawn over the entire aneurysm and ROIs excluding some regions close to the bone. Overall, BC yielded the best performance in spill-in correction in both phantom and patient studies.

Validation of the physiological background correction method for the suppression of the spill-in effect near highly radioactive regions in positron emission tomography

Background

Positron emission tomography (PET) imaging has a wide applicability in oncology, cardiology and neurology. However, a major drawback when imaging very active regions such as the bladder is the spill-in effect, leading to inaccurate quantification and obscured visualisation of nearby lesions. Therefore, this study aims at investigating and correcting for the spill-in effect from high-activity regions to the surroundings as a function of activity in the hot region, lesion size and location, system resolution and application of post-filtering using a recently proposed background correction technique. This study involves analytical simulations for the digital XCAT2 phantom and validation acquiring NEMA phantom and patient data with the GE Signa PET/MR scanner. Reconstructions were done using the ordered subset expectation maximisation (OSEM) algorithm. Dedicated point-spread function (OSEM+PSF) and a recently proposed background correction (OSEM+PSF+BC) were incorporated into the reconstruction for spill-in correction. The standardised uptake values (SUV) were compared for all reconstruction algorithms.

Results

The simulation study revealed that lesions within 15–20 mm from the hot region were predominantly affected by the spill-in effect, leading to an increased bias and impaired lesion visualisation within the region. For OSEM, lesion SUV_max converged to the true value at low bladder activity, but as activity increased, there was an overestimation as much as 19% for proximal lesions (distance around 15–20 mm from the bladder edge) and 2–4% for distant lesions (distance larger than 20 mm from the bladder edge). As bladder SUV increases, the % SUV change for proximal lesions is about 31% and 6% for SUV_max and SUV_mean, respectively, showing that the spill-in effect is more evident for the SUV_max than the SUV_mean. Also, the application of post-filtering resulted in up to 65% increment in the spill-in effect around the bladder edges. For proximal lesions, PSF has no major improvement over OSEM because of the spill-in effect, coupled with the blurring effect by post-filtering. Within two voxels around the bladder, the spill-in effect in OSEM is 42% (32%), while for OSEM+PSF, it is 31% (19%), with (and without) post-filtering, respectively. But with OSEM+PSF+BC, the spill-in contribution from the bladder was relatively low (below 5%, either with or without post-filtering). These results were further validated using the NEMA phantom and patient data for which OSEM+PSF+BC showed about 70–80% spill-in reduction around the bladder edges and increased contrast-to-noise ratio up to 36% compared to OSEM and OSEM+PSF reconstructions without post-filtering.

Conclusion

The spill-in effect is dependent on the activity in the hot region, lesion size and location, as well as post-filtering; and this is more evident in SUV_max than SUV_mean. However, the recently proposed background correction method facilitates stability in quantification and enhances the contrast in lesions with low uptake.

Electronic supplementary material

The online version of this article (10.1186/s40658-018-0233-8) contains supplementary material, which is available to authorized users.