Performance characteristic evaluation of a bismuth germanate-based high-sensitivity 5-ring discovery image quality positron emission tomography/computed tomography system as per National Electrical Manufacturers Association NU 2-2012

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

BACKGROUND

The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies.

METHODS

18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100-500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM.

RESULTS

OSEM had 11.5-18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3-7.7% for a sphere with a diameter of 10 mm and 2.5-5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions.

CONCLUSIONS

Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

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.

Optimization of injection dose in 18F-FDG PET/CT based on the 2020 national diagnostic reference levels for nuclear medicine in Japan

OBJECTIVE

Recently, the national diagnostic reference levels (DRLs) in Japan were revised as the DRLs 2020, wherein the body weight-based injection dose optimization in positron emission tomography/computed tomography using 18F-fluoro-2-deoxy-D-glucose (18F-FDG PET/CT) was first proposed. We retrospectively investigated the usefulness of this optimization method in improving image quality and reducing radiation dose.

METHODS

A total of 1,231 patients were enrolled in this study. A fixed injection dose of 240 MBq was administered to 624 patients, and a dose adjusted to 3.7 MBq/kg body weight was given to 607 patients. The patients with body weight-based injection doses were further divided according to body weight: group 1 (≤ 49 kg), group 2 (50-59 kg), group 3 (60-69 kg), and group 4 (≥ 70 kg). The effective radiation dose of FDG PET was calculated using the conversion factor of 0.019 mSv/MBq, per the International Commission on Radiological Protection publication 106. Image quality was assessed using noise equivalent count density (NECdensity), which was calculated by excluding the counts of the brain and bladder. The usefulness of the injection dose optimization in terms of radiation dose and image quality was analyzed.

RESULTS

The body weight-based injection dose optimization significantly decreased the effective dose by 11%, from 4.54 ± 0.1 mSv to 4.05 ± 0.8 mSv (p < 0.001). Image quality evaluated by NECdensity was also significantly improved by 10%, from 0.39 ± 0.1 to 0.43 ± 0.2 (p < 0.001). In no case did NECdensity deteriorate when the effective dose was decreased. In group 1, the dose decreased by 32%, while there was no significant deterioration in NECdensity (p = 0.054). In group 2, the dose decreased by 17%, and the NECdensity increased significantly (p < 0.01). In group 3, the dose decreased by 3%, and the NECdensity increased significantly (p < 0.01). In group 4, the dose increased by 14%, but there was no significant change in the NECdensity (p = 0.766).

CONCLUSION

Body weight-based FDG injection dose optimization contributed to not only the reduction of effective dose but also the improvement of image quality in patients weighing between 50 and 69 kg.

Importance of Understanding and Analyzing Daily Quality Assurance Test of Positron Emission Tomography/Computed Tomography Equipment in Minimizing the Downtime of Equipment in Remote Places

This article briefly describes the event of a defective detector block in a daily quality assurance scan/blank scan and insists on implementing guidelines to scan or not to scan in such a scenario. The nuclear medicine physicist should have a clear understanding of the blank scan graph, which shall help rectify the right cause of problem and give confidence to the physician in reporting the acquired study. A routine blank scan in positron emission tomography signifies various parameters of the crystal (coincidence count rate, single count rate, dead time, and coincidence time along with energy response) and in some respect is analogous to the daily uniformity flood image for gamma cameras, providing an overall assessment of detector response. We encountered a bad detector block in our routine quality assurance scan/blank scan and analyzed the root cause behind such an error which was finally restored to normalcy by replacing the defected part with a new one and an error-free blank scan was established. The analysis was carried out by performing various possible checks and discussing the issue with service engineer to help identify the defects much before service engineer actually arrived in our department. This allowed us to take the correct decision and enabled us to get the scanner repaired faster. Hence, a good understanding of the daily quality control test and proper analysis of the same may result in swift decision-making and faster repair of equipment leading to minimal disruption in the clinical workflow as well as avoidance of suboptimal scanning leading to the wrong diagnosis.

Impact of High Temperature and Humidity on the Performance of Positron Emission Tomography Scanner

Positron emission tomography/computed tomography (PET/CT) scanner is a state-of-art imaging device. Susceptibility of PET scanner in fluctuation environmental condition is known. Hence, every vendor prescribes the optimal conditions such as temperature and humidity to maintain the equipment in its best condition. In a hot summer day, we faced an unexpected long duration power failure in our department after administration of F-18 fluorodeoxyglucose to one of our patients. As air condition was not working in our department, temperature in the machine room went far beyond the prescribed level. As we had already injected the patient, we decided to perform PET scan of that patient in the existing condition in the machine room. When we reviewed the scan, we identified significant count loss in the image, which raised doubt in our mind. We discussed with our colleague and decided to perform a daily quality assurance (DQA) test to assess the condition of the equipment in high temperature. On DQA scan, we spotted several changes in the uniformity plot as well as energy plot. Following to that, the system was shut down completely till the main supply was restored successfully, and room temperature and humidity was restored to normal in machine room and console room. After several hours of restoration of normal condition in console and machine room, PET/CT equipment was restarted, and the DQA was repeated. On review, we found the restoration of normal DQA graph. We conclude that the sudden increase in temperature and humidity in PET/CT equipment room affects the performance of scanner which reflects as count deficit in the image. This impairment in the image quality may be because of bismuth germanate crystal, photomultiplier tubes, and associated electronics.

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test–retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC > 0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

PURPOSE

To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning.

METHODS

We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images.

RESULTS

LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%.

CONCLUSION

LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.