Noise equivalent count rate (NECR) and image noise are two different but related metrics that have been used to predict and assess image quality, respectively. The aim of this study is to investigate, using patient studies, the relationships between injected dose (ID), body mass index (BMI) and scanner type on NECR and image noise measurements in PET imaging. Two groups of 90 patients each were imaged on a GE DSTE and a DRX PET/CT scanner, respectively. The patients in each group were divided into nine subgroups according to three BMI (20-24.9, 25-29.9, 30-45 kg m(-2)) and three ID (296-444, 444-555, 555-740 MBq) ranges, resulting in ten patients/subgroup. All PET data were acquired in 3D mode and reconstructed using the VuePoint HD® fully 3D OSEM algorithm (2 iterations, 21(DRX) or 20 (DSTE) subsets). NECR and image noise measurements for bed positions covering the liver were calculated for each patient. NECR was calculated from the trues, randoms and scatter events recorded in the DICOM header of each patient study, while image noise was determined as the standard deviation of 50 non-neighboring voxels in the liver of each patient. A t-test compared the NECR and image noise for different scanners but with the same BMI and ID. An ANOVA test on the other hand was used to compare the results of patients with different BMI but the same ID and scanner type as well as different ID but the same BMI and scanner type. As expected the t-test showed a significant difference in NECR between the two scanners for all BMI and ID subgroups. However, contrary to what is expected no such findings were observed for image noise measurement. The ANOVA results showed a statistically significant difference in both NECR and image noise among the different BMI for each ID and scanner subgroup. However, there was no statistically significant difference in NECR and image noise across different ID for each BMI and scanner subgroup. Although the GE DRX PET/CT scanner has better count rate performance than the GE DSTE PET/CT scanner, this improvement does not translate to a lower image noise when using OSEM reconstruction. Our results show that patients with larger BMI consistently generate poorer image quality. Dose reduction from>555 to 296-444 MBq has minimal impact on image quality independent of the scanner used. A reduction in ID decreases patient and technologist exposure and can potentially reduce the overall cost of the study.
Amplitude gating techniques have recently been shown to be better at suppressing respiratory motion artifacts than phase gating. However, most commercial PET/CT scanners are equipped with phase gating capabilities only. The objective of this article was to propose and evaluate using patient studies an automated respiratory amplitude gating technique that could be implemented on current whole-body PET/CT scanners. A primary design feature of the proposed technique is to automatically match the respiratory amplitude captured during the CT scan with a corresponding amplitude during the PET scan. Methods The proposed amplitude gating technique consists of a CT scan, followed by a list-mode PET scan. The CT scan was acquired while the patient’s respiratory motion was recorded by a monitoring device that determined the respiratory motion amplitude captured during the CT scan. A program was designed to inject triggers into the PET list stream whenever the patient’s respiration crossed a preset amplitude range determined by the captured amplitude during CT. To implement this proposed amplitude gating technique in whole-body PET/CT, a PET-first protocol was necessary to minimize the respiratory baseline drift between the CT and PET scans. In this implementation, a regular PET scan was first acquired over the patient’s whole body but excluding the bed position that covered the lesion of interest. The whole-body CT scan was then acquired, followed by a list-mode PET acquisition over the bed position that covered the area of interest (lesion). The proposed amplitude gating technique was tested using 13 patients with 21 lung or thoracic tumors. Results In the patient studies, the gated images—when compared with the ungated images—showed statistically significant improvements, with an average 27% and 28% increase in maximum and mean standardized uptake value, respectively, for all lesions. Furthermore, the tumors in the gated images showed better contrast using visual inspection and line profiles. Conclusion The implementation of the proposed respiratory amplitude gating technique on current PET/ CT scanners is feasible, and amplitude-matched CT and PET data can be automatically generated using our proposed procedures without requiring patients to hold their breath or increase their radiation exposure.
For the scanners and reconstruction algorithm used in this study, our results suggest that the image SNR cannot be predicted by the NEC when using 3D-OSEM reconstruction particularly for those clinical applications requiring high activity concentration. Instead, our results suggest that image SNR varies with activity concentration and is dominated by the 3D-OSEM reconstruction algorithm and its associated parameters, while not being affected by the scanner type for the range of activity concentrations usually found in the clinic.
Purpose: Respiratory motion artifacts and partial volume effects ͑PVEs͒ are two degrading factors that affect the accuracy of image quantification in PET/CT imaging. In this article, the authors propose a joint motion and PVE correction approach ͑JMPC͒ to improve PET quantification by simultaneously correcting for respiratory motion artifacts and PVE in patients with lung/thoracic cancer. The objective of this article is to describe this approach and evaluate its performance using phantom and patient studies. Methods: The proposed joint correction approach incorporates a model of motion blurring, PVE, and object size/shape. A motion blurring kernel ͑MBK͒ is then estimated from the deconvolution of the joint model, while the activity concentration ͑AC͒ of the tumor is estimated from the normalization of the derived MBK. To evaluate the performance of this approach, two phantom studies and eight patient studies were performed. In the phantom studies, two motion waveforms-a linear sinusoidal and a circular motion-were used to control the motion of a sphere, while in the patient studies, all participants were instructed to breathe regularly. For the phantom studies, the resultant MBK was compared to the true MBK by measuring a correlation coefficient between the two kernels. The measured sphere AC derived from the proposed method was compared to the true AC as well as the ACs in images exhibiting PVE only and images exhibiting both PVE and motion blurring. For the patient studies, the resultant MBK was compared to the motion extent derived from a 4D-CT study, while the measured tumor AC was compared to the AC in images exhibiting both PVE and motion blurring. Results: For the phantom studies, the estimated MBK approximated the true MBK with an average correlation coefficient of 0.91. The tumor ACs following the joint correction technique were similar to the true AC with an average difference of 2%. Furthermore, the tumor ACs on the PVE only images and images with both motion blur and PVE effects were, on average, 75% and 47.5% ͑10%͒ of the true AC, respectively, for the linear ͑circular͒ motion phantom study. For the patient studies, the maximum and mean AC/SUV on the PET images following the joint correction are, on average, increased by 125.9% and 371.6%, respectively, when compared to the PET images with both PVE and motion. The motion extents measured from the derived MBK and 4D-CT exhibited an average difference of 1.9 mm. Conclusions: The proposed joint correction approach can improve the accuracy of PET quantification by simultaneously compensating for the respiratory motion artifacts and PVE in lung/ thoracic PET/CT imaging.
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