Investigating PET Image Quality vs. Patient Size and Administered Activity for Different Scanner Models, Using the NEC Metric and a Dead-Time Model

Problem: PET system performance, particularly the count rate-related effects, depends on a variety of effects including the patient size and the amount and distribution of radioactivity in the patient. The performance also depends on the particular PET system. This is primarily due to differences in detector material and detector size. This leads to a difference in image quality for the same activity level for different detectors. The current activity dosing protocol in Duke University’s Cancer Center is weight-based and system-independent, even though the systems vary greatly in count rate capability. This protocol might not be the most optimal protocol given that patients of the same weight are given the same dose but would produce different image qualities depending on the system they were scanned on. The work done in this thesis explores the components of the dosing protocol in an effort to reconsider the patient- and system- specific dosing needs for optimal image quality. This study uses Noise Equivalent Count (NEC) curves to simulate image quality for data that has been acquired using different systems, body sizes and shapes, and activity levels.Methods: This study investigates the behavior of three different hybrid PET/CT systems: the GE Discovery 690 (D690), the GE Discovery IQ (DIQ), and the GE Discovery MI (DMI). Phantom data were used to understand the performance of the three systems, and existing patient data were used to further evaluate the effects that different body characteristics have on each system. Two phantoms were used in this study: a whole-body phantom that simulates a medium- large patient and a smaller cylindrical phantom that simulates an extreme case of a small object. Both phantoms were filled with a large amount of activity (about 18 mCi for the whole-body phantom and about 12 mCi for the smaller cylindrical phantom) and thoroughly mixed before being scanned repeatedly for a long duration on all three systems to test each system’s behavior with different-sized phantoms. A Bash script was run to collect information from the phantoms’ DICOM headers so that NEC formulas could be calculated, and NEC curves could be analyzed. The dead-time model was adjusted to best fit the simulated data to the actual data to potentially improve accuracy with patient data. The phantoms were used to analyze the systems’ general behaviors without any human factors such as different uptakes for different organs, a larger variety of shapes and sizes, and different compositions. Once the general behaviors were understood and the models were adjusted, a large selection of patient data (500 for the DIQ and 500 for the DMI) was obtained. This was accomplished through the creation of multiple Bash and Python scripts that ran through patient data, retrieved the desired patients and patient scans based on specific criteria determined by the scripts, and collected anonymized data used to form NEC curves and experiment with body metrics. A few anonymized CT and PET images were saved for each patient as well so that body diameter measurements could be made. Results: It was determined that the NEC curves produced by the two different detector materials (BGO and LYSO) peaked at different activity levels for the same phantom. Also, to obtain the same NEC rate, the smaller cylindrical phantom required less activity than the whole- body phantom for each of the three systems. Dead-time data found in the image header was analyzed using Stearns’ NEC model, and his model appeared to consistently deviate from actual measurements. To improve the model, adjustments were made to parameters in the dead-time model to create a best fit to the phantom data, considering the three different systems and two phantom sizes. It was determined that a single dosing protocol may not be optimal for all systems since the NEC curves peaked at very different activities for each system and peaked at different counts per second for each system. Furthermore, the dosing protocol may not be benefiting patients of all sizes, as heavier patients may be receiving higher doses than needed for good image quality. Various body metrics were tested to compare which is the best to implement into an improved dosing protocol. These included body weight, BMI, and a pseudodiameter calculated from a cylindrical body approximation. This pseudodiameter was formed as an effort to approximate body diameters from patient weights and heights. The relationship between optimal dose (the dose at which peak image quality occurs) and the three body metrics was tested to determine whether a new dosing protocol can be formed based off of optimal doses depending on a certain body metric. It was determined that there is no correlation between optimal dose and the three body metrics. Conclusion: Body weight was concluded to be the most meaningful metric for calculating patient doses due to the ease of the measurement and the consistent relationship between image quality and patient weight for each system. Since patients with similar weights tend to produce similar image qualities, body weight can be used as a fairly reliable predictor of image quality when injected with a specific dose. Due to the differences in detection between the Discovery IQ and Discovery MI, the NEC curves produced by either system are very different, so the current dosing protocol would work best if it were system-dependent. Patients scanned on the DIQ could especially be receiving lower doses while still producing near-optimal image quality. If the goal of scanning patients is to produce the same image quality for every patient, then the dose for some patients could be significantly decreased. This can be concluded due to the NEC curves of patients at different body weights peaking at different count rates (where the lightest patients peak at higher count rates, and the heaviest patients peak at lower count rates). In this case, the current system-independent dosing calculation may not be optimal. A new dosing protocol was proposed. For the DIQ, patients would all be injected with 5.84 mCi. For the DMI, patients would be injected with a dose calculated by multiplying patients’ body weights by 0.06 mCi/kg, with a maximum injected dose of 11 mCi.