Varsha Viswanath1, Hasan Sari2,3, Austin R Pantel1, Maurizio Conti4, Margaret E Daube-Witherspoon5, Clemens Mingels3, Ian Alberts3, Lars Eriksson4,6, Kuangyu Shi3, Axel Rominger3, Joel S Karp1. 1. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 2. Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland. 3. Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 18, 3010, Bern, Switzerland. 4. Siemens Medical Solutions, USA Inc., Knoxville, TN, USA. 5. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. daubewit@pennmedicine.upenn.edu. 6. Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden.
Abstract
PURPOSE: Kinetic parameters from dynamic 18F-fluorodeoxyglucose (FDG) imaging offer complementary insights to the study of disease compared to static clinical imaging. However, dynamic imaging protocols are cumbersome due to the long acquisition time. Long axial field-of-view (LAFOV) PET scanners (> 70 cm) have two advantages for dynamic imaging over clinical PET scanners with a standard axial field-of-view (SAFOV; 16-30 cm). The large axial coverage enables multi-organ dynamic imaging in a single bed position, and the high sensitivity may enable clinically routine abbreviated dynamic imaging protocols. METHODS: In this work, we studied two abbreviated protocols using data from a 65-min dynamic 18F-FDG scan: (A) dynamic imaging immediately post-injection (p.i.) for variable durations, and (B) dynamic imaging immediately p.i. for variable durations plus a 1-h p.i. (5-min-long) datapoint. Nine cancer patients were imaged on the Biograph Vision Quadra (Siemens Healthineers). Time-activity curves over the lesions (N = 39) were fitted using the Patlak graphical analysis and a 2-tissue-compartment (2C, k4 = 0) model for variable scan durations (5-60 min). Kinetic parameters from the complete dataset served as the reference. Lesions from all cancers were grouped into low, medium, and high flux groups, and bias and precision of Ki (Patlak) and Ki, K1, k2, and k3 (2C) were calculated for each group. RESULTS: Using only early dynamic data with the 2C (or Patlak) model, accurate quantification of Ki required at least 50 (or 55) min of dynamic data for low flux lesions, at least 30 (or 40) min for medium flux lesions, and at least 15 (or 20) min for high flux lesions to achieve both 10% bias and precision. The addition of the final (5-min) datapoint allowed for accurate quantification of Ki with a bias and precision of 10% using only 10-15 min of early dynamic data for either model. CONCLUSION: Dynamic imaging for 10-15 min immediately p.i. followed by a 5-min scan at 1-h p.i can accurately and precisely quantify 18F-FDG on a long axial FOV scanner, potentially allowing for more widespread use of dynamic 18F-FDG imaging.
PURPOSE: Kinetic parameters from dynamic 18F-fluorodeoxyglucose (FDG) imaging offer complementary insights to the study of disease compared to static clinical imaging. However, dynamic imaging protocols are cumbersome due to the long acquisition time. Long axial field-of-view (LAFOV) PET scanners (> 70 cm) have two advantages for dynamic imaging over clinical PET scanners with a standard axial field-of-view (SAFOV; 16-30 cm). The large axial coverage enables multi-organ dynamic imaging in a single bed position, and the high sensitivity may enable clinically routine abbreviated dynamic imaging protocols. METHODS: In this work, we studied two abbreviated protocols using data from a 65-min dynamic 18F-FDG scan: (A) dynamic imaging immediately post-injection (p.i.) for variable durations, and (B) dynamic imaging immediately p.i. for variable durations plus a 1-h p.i. (5-min-long) datapoint. Nine cancer patients were imaged on the Biograph Vision Quadra (Siemens Healthineers). Time-activity curves over the lesions (N = 39) were fitted using the Patlak graphical analysis and a 2-tissue-compartment (2C, k4 = 0) model for variable scan durations (5-60 min). Kinetic parameters from the complete dataset served as the reference. Lesions from all cancers were grouped into low, medium, and high flux groups, and bias and precision of Ki (Patlak) and Ki, K1, k2, and k3 (2C) were calculated for each group. RESULTS: Using only early dynamic data with the 2C (or Patlak) model, accurate quantification of Ki required at least 50 (or 55) min of dynamic data for low flux lesions, at least 30 (or 40) min for medium flux lesions, and at least 15 (or 20) min for high flux lesions to achieve both 10% bias and precision. The addition of the final (5-min) datapoint allowed for accurate quantification of Ki with a bias and precision of 10% using only 10-15 min of early dynamic data for either model. CONCLUSION: Dynamic imaging for 10-15 min immediately p.i. followed by a 5-min scan at 1-h p.i can accurately and precisely quantify 18F-FDG on a long axial FOV scanner, potentially allowing for more widespread use of dynamic 18F-FDG imaging.
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