BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients

[68Ga]Ga-FAPI PET/CT in brain tumors: comparison with [18F]F-FDG PET/CT

Purpose

To investigate the feasibility of [68Ga]Ga-FAPI PET/CT in brain tumor imaging and to compare it with [18F]F-FDG PET/CT.

Methods

25 patients with MRI-suspected brain tumors were included in the study. They underwent whole body [18F]F-FDG PET/CT and [68Ga]Ga-FAPI PET/CT and brain scans. The target-to-background ratio (TBR) of brain tumors was calculated with the background of surrounding normal brain tissues uptake. The SUVmax and TBR of [18F]F-FDG PET/CT and [68Ga]Ga-FAPI PET/CT were compared. Additionally, the correlation between the uptake of the tracer by lesions with the greatest diameter of the lesion, the breadth of the oedema band, and the enhancement scores of the MRI enhancement scans was analyzed.

Result

[68Ga]Ga-FAPI PET/CT was superior to [18F]F-FDG PET/CT for lesion detection, especially for brain metastases. Among gliomas, only high-grade gliomas uptake [68Ga]Ga-FAPI. Compared with [18F]F-FDG PET/CT, [68Ga]Ga-FAPI PET/CT had a lower SUVmax but a significantly better TBR. On [68Ga]Ga-FAPI PET/CT, the TBR may be associated with brain tumor blood-brain barrier disruption.

Conclusions

[68Ga]Ga-FAPI PET/CT is a promising imaging tool for the assessment of brain tumors. Lack of physiological uptake of [68Ga]Ga-FAPI in normal brain parenchyma results in high TBR values, leading to better visualization of lesions and contributing to subsequent targeted therapy studies.

Advances in knowledge

Clinical utility of [68Ga]Ga-FAPI PET/CT in brain tumors remains unclear, and there aren't many similar studies in the literature. We evaluated the role of [68Ga]Ga-FAPI PET/CT in diagnosing brain tumors.

The Monocle Sign on 18 F-FDG PET Indicates Contralateral Peripheral Facial Nerve Palsy

BACKGROUND

The aim of our study was to retrospectively analyze FDG PET/CT data in patients with facial nerve palsy (FNP) for the presence of the monocle sign.

PATIENTS AND METHODS

A total of 85 patients with unilateral FNP were included into our study, thereof 73 with peripheral FNP and 12 with central FNP. FDG uptake (SUV max , SUV mean , total lesion glycolysis) was measured in both orbicularis oculi muscles (OOMs). FDG uptake of paretic and nonparetic muscles was compared in patients with FNP (Wilcoxon test and Mann-Whitney U test) and was also compared with FDG uptake in 33 patients without FNP (Mann-Whitney U test). SUV max ratios of OOM were compared. A receiver operating characteristic curve and Youden Index were used to determine the optimal cutoff SUV max ratio for the prevalence of contralateral peripheral FNP.

RESULTS

The SUV max ratio of OOM was significantly higher in patients with peripheral FNP compared with patients with central FNP and those without FNP (1.70 ± 0.94 vs 1.16 ± 0.09 vs 1.18 ± 0.21, respectively; P < 0.001). The SUV max ratio of OOM yielded an area under the curve (AUC) of 0.719 (95% confidence interval, 0.630-0.809), with an optimal cutoff of 1.41, yielding a specificity of 94.4% and a sensitivity of 44.1% for identifying contralateral peripheral FNP. One hundred percent specificity is achieved using a cutoff of 1.91 (sensitivity, 29.4%).

CONCLUSIONS

Asymmetrically increased FDG uptake of the OOM (the "monocle sign") indicates contralateral peripheral FNP. A nearly 2-fold higher SUV max represents a practically useful cutoff.

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm

BACKGROUND

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality, decreased administered activity and/or acquisition times.

OBJECTIVE

To evaluate the impact of reducing counts (simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images.

MATERIALS AND METHODS

Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90 s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60 s/bed, β=800; 50 s/bed, β=900; 40 s/bed, β=1000; and 30 s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60 s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions.

RESULTS

The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5 kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90 s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90 s/bed, β=700 only at a simulated acquisition time of 30 s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40 s/bed, β=1000 images were generally preferred over 60 s/bed TOF OSEM images.

CONCLUSION

In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90 s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the [Formula: see text] increased by 59.0% for a sphere with a diameter of 10 mm and 26.4% for a sphere with a diameter of 37 mm. Conversely, [Formula: see text] increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ[Formula: see text] and Δ[Formula: see text] were - 41.1% and - 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively.

Optimization of Bayesian penalized likelihood reconstruction for 68 Ga-prostate-specific membrane antigen-11 PET/computed tomography

OBJECTIVE

The objective of this study is to determine the optimal β value for clinical use in digital 68 Ga-prostate-specific membrane antigen (PSMA-11) PET/computed tomography (CT) imaging.

METHODS

68 Ga PSMA PET/CT of 21 patients with prostate cancer were reconstructed using block-sequential regularized expectation maximization ( β value of 400-1600) and ordered subsets expectation maximization. Nine independent blinded readers evaluated each reconstruction for overall image quality, noise level and lesion detectability. Maximum standardized uptake value (SUVmax) of the most intense lesion, liver SUVmean and liver SUV SD were recorded. Lesions were then subdivided according to uptake and size; the SUVmax of these lesions were analyzed.

RESULTS

There is a statistically significant correlation between improvement in image quality and β value, with the best being β 1400. This trend was also seen in image noise ( P  < 0.001), with the least image noise reported with β 1400. Lesion detectability was not significantly different between the different β values ( P  =  0.6452). There was no statistically significant difference in SUVmax of the most intense lesion ( P  = 0.9966) and SUVmean of liver background between the different β values ( P  = 0.9999); however, the SUV SD of the liver background showed a clear trend, with the lowest with β 1400 ( P  = 0.0008). There was a decreasing trend observed in SUVmax when β values increased from 800 to 1400 for all four subgroups, and this decrease was greatest in small and low uptake lesions.

CONCLUSION

Bayesian penalized likelihood reconstruction algorithms improve image quality without affecting lesion detectability. A β value of 1400 is optimal.

Analysis of image quality by regulating beta function of BSREM reconstruction algorithm and comparison with conventional reconstructions in carcinoma breast studies of PET CT with BGO detector

BACKGROUND

The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions.

METHODS

Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable β factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated.

RESULTS

Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing β value of BSREM. In comparison with OSEM, liver noise was not significantly different from β200 and β250. SNR of OSEM was significantly lower than any other β factors and SBR of β factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from β400 and β350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. β350 scored highest under visual scoring with a moderate agreement.

CONCLUSION

The study quantitatively indicates the optimum beta range of β250-450 and the qualitative evaluation indicates that β350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

HYPR4D kernel method on TOF PET data with validations including image-derived input function

Background

Positron emission tomography (PET) images are typically noisy especially in dynamic imaging where the PET data are divided into a number of short temporal frames often with a low number of counts. As a result, image features such as contrast and time–activity curves are highly variable. Noise reduction in PET is thus essential. Typical noise reduction methods tend to not preserve image features/patterns (e.g. contrast and size dependent) accurately. In this work, we report the first application of our HYPR4D kernel method on time-of-flight (TOF) PET data (i.e. PSF-HYPR4D-K-TOFOSEM). The proposed HYPR4D kernel method makes use of the mean 4D high frequency features and inconsistent noise patterns over OSEM subsets as well as the low noise property of the early reconstruction updates to achieve prior-free de-noising. The method was implemented and tested on the GE SIGNA PET/MR and was compared to the TOF reconstructions with PSF resolution modeling available on the system, namely PSF-TOFOSEM with and without standard post filter and PSF-TOFBSREM (TOF Q.Clear) with various beta values (regularization strengths).

Results

Results from experimental contrast phantom and human subject data with various PET tracers showed that the proposed method provides more robust and accurate image features compared to other regularization methods. The preservation of contrast for the PSF-HYPR4D-K-TOFOSEM was observed to be better and less dependent on the contrast and size of the target structures as compared to TOF Q.Clear and PSF-TOFOSEM with filter. At the same contrast level, PSF-HYPR4D-K-TOFOSEM achieved better 4D noise suppression than other methods (e.g. >2 times lower noise than TOF Q.Clear at the highest contrast). We also present a novel voxel search method to obtain an image-derived input function (IDIF) and demonstrate that the obtained IDIF is the most quantitative w.r.t. the measured blood samples when the acquired data are reconstructed with PSF-HYPR4D-K-TOFOSEM.

Conclusions

The overall results support superior performance of the PSF-HYPR4D-K-TOFOSEM for TOF PET data and demonstrate that the proposed method is likely suitable for all imaging tasks including the generation of IDIF without requiring any prior information as well as further improving the effective sensitivity of the imaging system.