MotionFree® (AMF) is a data-driven respiratory gating (DDG) algorithm for image processing that has recently been introduced into clinical practice. The present study aimed to verify the accuracy of respiratory waveform and the effects of normal and irregular respiratory motions using AMF with the DDG algorithm.
We used a NEMA IEC body phantom comprising six spheres (37-, 28-, 22-, 17-, 13-, and 10 mm diameter) containing 18F. The sphere-to-background ratio was 4:1 (21.2 and 5.3 kBq/mL). We acquired PET/CT images from a stationary or moving phantom placed on a custom-designed motion platform. Respiratory motions were reproduced based on normal (sinusoidal or expiratory-paused waveforms) and irregular (changed amplitude or shifted baseline waveforms) movements. The “width” parameters in AMF were set at 10–60% and extracted data during the expiratory phases of each waveform. We verified the accuracy of the derived waveforms by comparing those input from the motion platform and output determined using AMF. Quantitative accuracy was evaluated as recovery coefficients (RCs), improvement rate, and %change that were calculated based on sphere diameter or width. We evaluated statistical differences in activity concentrations of each sphere between normal and irregular waveforms.
Respiratory waveforms derived from AMF were almost identical to the input waveforms on the motion platform. Although the RCs in each sphere for expiratory-paused and ideal stationary waveforms were almost identical, RCs except the expiratory-paused waveform were lower than those for the stationary waveform. The improvement rate decreased more for the irregular, than the normal waveforms with AMF in smaller spheres. The %change was improved by decreasing the width of waveforms with a shifted baseline. Activity concentrations significantly differed between normal waveforms and those with a shifted baseline in spheres < 28 mm.
The PET images using AMF with the DDG algorithm provided the precise waveform of respiratory motions and the improvement of quantitative accuracy in the four types of respiratory waveforms. The improvement rate was the most obvious in expiratory-paused waveforms, and the most subtle in those with a shifted baseline. Optimizing the width parameter in irregular waveform will benefit patients who breathe like the waveform with the shifted baseline.
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The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R, van Dalen JA, et al. Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur J Nucl Med Mol Imaging. 2017;44:4–16.
Pepin A, Daouk J, Bailly P, Hapdey S, Meyer ME. Management of respiratory motion in PET/computed tomography: the state of the art. Nucl Med Commun. 2014;35:113–22.
Aide N, Lasnon C, Kesner A, Levin CS, Buvat I, Iagaru A, et al. New PET technologies - embracing progress and pushing the limits. Eur J Nucl Med Mol Imaging. 2021;48:2711–26.
Kesner AL, Schleyer PJ, Büther F, Walter MA, Schäfers KP, Koo PJ. On transcending the impasse of respiratory motion correction applications in routine clinical imaging—a consideration of a fully automated data driven motion control framework. EJNMMI Phys. 2014;1:8.
Thielemans K, Rathore S, Engbrant F, Razifar P. Device-less gating for PET/CT using PCA. In: 2011 IEEE Nuclear Science Symposium Conference Record; 2011. p. 3904–10.
Thielemans K, Schleyer P, Marsden PK, Manjeshwar RM, Wollenweber SD, Ganin A. Comparison of different methods for data-driven respiratory gating of PET data. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC); 2013. p. 1–4.
Schleyer P, Hong I, Jones J, Hamill J, Panin V, Fuerst S. Data-driven respiratory gating whole body PET using continuous bed motion. In: 2018 IEEE nuclear science symposium and medical imaging conference proceedings (NSS/MIC); 2018. p. 1–5.
Schleyer PJ, O’Doherty MJ, Barrington SF, Marsden PK. Retrospective data-driven respiratory gating for PET/CT. Phys Med Biol. 2009;54:1935–50.
Bundschuh RA, Martinez-Moeller A, Essler M, Martinez MJ, Nekolla SG, Ziegler SI, et al. Postacquisition detection of tumor motion in the lung and upper abdomen using list-mode PET data: a feasibility study. J Nucl Med. 2007;48:758–63.
Feng T, Wang J, Sun Y, Zhu W, Dong Y, Li H. Self-gating: an adaptive center-of-mass approach for respiratory gating in PET. IEEE Trans Med Imaging. 2018;37:1140–8.
KhamisH WS. MotionFree:Device-less digital respiratory gating technique, seamlessly integrated in PET imaging routine. In: Co. GE, editor.
Sebastian Fuerst JH, Inki Hong, Judson Jones, Paul Schleyer. OncoFreeze: Deviceless motion management for PET imaging. In: Siemens Medical Solutions USA I, editor.
Feng T, Yang G, Liu H, Ding Y, Lv Y, Li H, et al. Data-driven phase-matched PET/CT: a solution for axial location-dependent respiratory phase in CT. J Nucl Med. 2021;62:1420.
Morley NC, McGowan DR, Gleeson FV, Bradley KM. Software respiratory gating of positron emission tomography-computed tomography improves pulmonary nodule detection. Am J Respir Crit Care Med. 2017;195:261–2.
Liberini V, Kotasidis F, Treyer V, Messerli M, Orita E, Engel-Bicik I, et al. Impact of PET data driven respiratory motion correction and BSREM reconstruction of (68)Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS). Sci Rep. 2021;11:2273.
Buther F, Jones J, Seifert R, Stegger L, Schleyer P, Schafers M. Clinical evaluation of a data-driven respiratory gating algorithm for whole-body PET with continuous bed motion. J Nucl Med. 2020;61:1520–7.
Kang SY, Moon BS, Kim HO, Yoon HJ, Kim BS. The impact of data-driven respiratory gating in clinical F-18 FDG PET/CT: comparison of free breathing and deep-expiration breath-hold CT protocol. Ann Nucl Med. 2021;35:328–37.
Kesner AL, Chung JH, Lind KE, Kwak JJ, Lynch D, Burckhardt D, et al. Validation of software gating: a practical technology for respiratory motion correction in PET. Radiology. 2016;281:239–48.
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Evaluation of data-driven respiratory gating waveforms for clinical PET imaging. EJNMMI Res. 2019;9:1.
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Data-driven respiratory gating outperforms device-based gating for clinical (18)F-FDG PET/CT. J Nucl Med. 2020;61:1678–83.
Walker MD, Bradley KM, McGowan DR. Evaluation of principal component analysis-based data-driven respiratory gating for positron emission tomography. Br J Radiol. 2018;91:20170793.
Buther F, Ernst I, Frohwein LJ, Pouw J, Schafers KP, Stegger L. Data-driven gating in PET: Influence of respiratory signal noise on motion resolution. Med Phys. 2018;45:3205–13.
Reynes-Llompart G, Gamez-Cenzano C, Romero-Zayas I, Rodriguez-Bel L, Vercher-Conejero JL, Marti-Climent JM. Performance characteristics of the whole-body discovery IQ PET/CT System. J Nucl Med. 2017;58:1155–61.
Tachibana H, Kitamura N, Ito Y, Kawai D, Nakajima M, Tsuda A, et al. Management of the baseline shift using a new and simple method for respiratory-gated radiation therapy: detectability and effectiveness of a flexible monitoring system. Med Phys. 2011;38:3971–80.
Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV, et al. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys. 2002;53:822–34.
Liu C, Alessio A, Pierce L, Thielemans K, Wollenweber S, Ganin A, et al. Quiescent period respiratory gating for PET/CT. Med Phys. 2010;37:5037–43.
Shirato H, Seppenwoolde Y, Kitamura K, Onimura R, Shimizu S. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol. 2004;14:10–8.
Sigfridsson J, Lindstrom E, Iyer V, Holstensson M, Velikyan I, Sundin A, et al. Prospective data-driven respiratory gating of [(68)Ga]Ga-DOTATOC PET/CT. EJNMMI Res. 2021;11:33.
Kim DH, Yoo EH, Hong US, Kim JH, Ko YH, Moon SC, et al. Image Registration of (18)F-FDG PET/CT Using the MotionFree Algorithm and CT Protocols through Phantom Study and Clinical Evaluation. Healthcare (Basel). 2021;9.
Yamashita K, Miyaji N, Motegi K, Ito S. Terauchi T [Effects of CT-based attenuation correction on pet images using data-driven respiratory gating]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2021;77:1317–24.
Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48(6):932–45.
Daou D. Respiratory motion handling is mandatory to accomplish the high-resolution PET destiny. Eur J Nucl Med Mol Imaging. 2008;35:1961–70.
Okubo M, Nishimura Y, Nakamatsu K, Okumura M, Shibata T, Kanamori S, et al. Static and moving phantom studies for radiation treatment planning in a positron emission tomography and computed tomography (PET/CT) system. Ann Nucl Med. 2008;22:579–86.
Teo BK, Saboury B, Munbodh R, Scheuermann J, Torigian DA, Zaidi H, et al. The effect of breathing irregularities on quantitative accuracy of respiratory gated PET/CT. Med Phys. 2012;39:7390–7.
Alessio AM, Kinahan PE. Improved quantitation for PET/CT image reconstruction with system modeling and anatomical priors. Med Phys. 2006;33:4095–103.
Frood R, McDermott G, Scarsbrook A. Respiratory-gated PET/CT for pulmonary lesion characterisation-promises and problems. Br J Radiol. 2018;91:20170640.
Tsutsui Y, Kidera D, Taniguchi T, Akamatsu G, Komiya I, Umezu Y, et al. Accuracy of amplitude-based respiratory gating for PET/CT in irregular respirations. Ann Nucl Med. 2014;28:770–9.
van Elmpt W, Hamill J, Jones J, De Ruysscher D, Lambin P, Ollers M. Optimal gating compared to 3D and 4D PET reconstruction for characterization of lung tumours. Eur J Nucl Med Mol Imaging. 2011;38:843–55.
Kim JS, Park CR, Yoon SH, Lee JA, Kim TY, Yang HJ. Improvement of image quality using amplitude-based respiratory gating in PET-computed tomography scanning. Nucl Med Commun. 2021;42:553–65.
Kesner AL, Meier JG, Burckhardt DD, Schwartz J, Lynch DA. Data-driven optimal binning for respiratory motion management in PET. Med Phys. 2018;45:277–86.
Chen S, Hu P, Gu Y, Yu H, Shi H. Performance characteristics of the digital uMI550 PET/CT system according to the NEMA NU2-2018 standard. EJNMMI Phys. 2020;7:43.
We thank the staff at the Diagnostic Imaging Center at Cancer Institute Hospital of JFCR which contributed to the study design and phantom data acquisition. We are also grateful to Mr. Miyachi (GE Healthcare Co., Ltd.) and Mr. Saito for technical support with the AMF. This study was supported in part by the National Cancer Center Research and Development Fund (2020-J-3) and by a KAKENHI Grant-in-Aid for Young Scientists (No. 22K18234) and from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japanese Government.
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Miyaji, N., Miwa, K., Yamashita, K. et al. Impact of irregular waveforms on data-driven respiratory gated PET/CT images processed using MotionFree algorithm. Ann Nucl Med 37, 665–674 (2023). https://doi.org/10.1007/s12149-023-01870-9