Abstract
Purpose
The purpose of the study is to investigate the feasibility of an event driven motion correction method for neurological microPET imaging of small laboratory animals in the fully awake state.
Procedures
A motion tracking technique was developed using an optical motion tracking system and light (<1g) printed targets. This was interfaced to a microPET scanner. Recorded spatial transformations were applied in software to list mode events to create a motion-corrected sinogram. Motion correction was evaluated in microPET studies, in which a conscious animal was simulated by a phantom that was moved during data acquisition.
Results
The motion-affected scan was severely distorted compared with a reference scan of the stationary phantom. Motion correction yielded a nearly distortion-free reconstruction and a marked reduction in mean squared error.
Conclusions
This work is an important step towards motion tracking and motion correction in neurological studies of awake animals in the small animal PET imaging environment.
Similar content being viewed by others
References
Lindauer U, Villringer A, Dirnagl U (1993) Characterization of CBF response to somatosensory stimulation: model and influence of anesthetics. Am J Physiol 264(4):1223–1228
Jones SC, Williams JL, Shea M, Easley KA, Wei D (1995) Cortical cerebral blood flow cycling: anesthesia and arterial blood pressure. Am J Physiol 268:569–575
Fueger BJ, Czernin J, Hildebrandt I, Tran C, Halpern BS, Stout D et al (2006) Impact of animal handling on the results of 18F-FDG PET studies in mice. J Nucl Med 47:999–1006
Arnsten AF (2000) Stress impairs prefrontal cortical function in rats and monkeys: role of dopamine D1 and norepinephrine alpha-1 receptor mechanisms. Prog Brain Res 126:183–192
Picard Y, Thompson CJ (1997) Motion correction of PET images using multiple acquisition frames. IEEE Trans Med Imag 16:137–144
Fulton RR, Meikle SR, Eberl S, Pfeiffer J, Constable CJ (2002) Correction for head movements in positron emission tomography using an optical motion tracking system. IEEE Trans Nucl Sci 49:116–123
Bloomfield PM, Spinks TJ, Reed J, Schnorr L, Westrip AM, Livieratos L et al (2003) The design and implementation of a motion correction scheme for neurological PET. Phys Med Biol 48:959–978
Fulton R, Nickel I, Tellmann L, Meikle S, Pietrzyk U, Herzog H (2003) Event-by-event motion compensation in 3D PET. Proc. 2003 IEEE Nuclear Science Symposium and Medical Imaging Conference, 5:3286–3289, Portland, Oregon
Bühler P, Just U, Will E, Kotzerke J, van den Hoff J (2004) An accurate method for correction of head movement in PET. IEEE Trans Med Imag 23:1176–1185
Fulton R, Tellmann L, Pietrzyk U, Winz O, Stangier I, Nickel I et al (2004) Accuracy of motion correction methods for PET brain imaging. Proc. 2004 IEEE Nuclear Science Symposium and Medical Imaging Conference, 7:4226–4230, Lyon, France
Menke M, Atkins MS, Buckley KR (2002) Compensation methods for head motion detected during PET imaging. IEEE Trans Nucl Sci 49:116–123
Angel A, Linkens DC, Ting CH (1999) Estimation of latency changes and relative amplitudes in somatosensory evoked potentials using wavelets and regression. Comput Biomed Res 32(3):209–251
Nakao Y, Itoh Y, Kuang TY, Cook M, Jehle J, Sokoloff L (2001) Effects of anesthesia on functional activation of cerebral blood flow and metabolism. Proc Natl Acad Sci 98(13):7593–7598
Vaska P, Woody C, Schlyer D, Pratte J-F, Junnarkar S, Southekal S et al (2007) The design and performance of the 2nd-generation RatCAP awake brain PET system. Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference, Honolulu, 4181–4184
Hudson HM, Larkin RS (1994) Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imag 13:601–609
Defrise M (1995) A factorization method for the 3D X-ray transform. Inverse Probl 11:983–994
Acknowledgments
We thank Danny Newport, Stefan Siegel, Aaron McFarland, and Anne Smith of Siemens Medical Solutions for kindly providing details of the global dead time correction and the microPET list mode and sinogram formats. This work was supported by Australian Research Council Discovery Project 0663519.
Author information
Authors and Affiliations
Corresponding author
Appendix 1: Focus 220 List Mode Data Format
Appendix 1: Focus 220 List Mode Data Format
This appendix describes the microPET Focus 220 list mode data packets that were used in this work. Data are transported from the tomograph to the host PC via a Firewire interface and stored on disk in 48-bit list mode packets. The most significant byte and the lower four bits of the fifth byte of each packet are always 0 and 4, respectively, to provide a synchronization pattern in the list mode stream. Figure 9 shows the generalized list mode data packet format.
There are two classes of list mode data packets, event and tag packets, identified by bit 39 (0 for event and 1 for tag). The event packets cater for coincidence, singles, and double singles events as identified by bits 36–37. The format of a coincidence event packet is shown in Table 3.
Tag packets consist of time mark packets, prompt/delay count packets, block singles count packets and user-defined pose packets. Time mark packets contain elapsed time (in bits 0–28 as shown in Table 4) and are inserted into the list mode data stream every millisecond. Users can also insert their own time mark packets into the list mode data stream via DOS commands. In the present work, bit 28 of the time mark packet is dedicated to distinguishing between system and user-inserted time mark packets. This leaves bits 0–27 available in a system time mark to represent elapsed time in milliseconds or the identification number of a pose measurement in a user-inserted time mark (see Table 4).
Prompt/delay count packets report the number of prompt or delayed events detected by the coincidence controller every 40ms as shown in Table 5. Bits 0–23 report the number of coincidence events (prompt or delayed as indicated by the sense of bit 26) in the preceding 40ms period.
Block singles count packets, in bits 0–17, report the number of singles counts recorded in a block in the preceding 200ms, as shown in Table 6. Bit 26 is the valid bit (0 for valid singles counts and 1 for CFD singles counts). CFD singles counts are singles counts received by the constant fraction discriminator (CFD). Valid singles counts are singles counts that have been validated in terms of energy and position.
In the post-processing of the list mode data, synchronized pose data (R x (yaw), R v (pitch), R z (roll), x, y and z) are inserted into the list mode data in user-defined pose packets (Table 7).
Rights and permissions
About this article
Cite this article
Zhou, V.W., Kyme, A.Z., Meikle, S.R. et al. An Event-Driven Motion Correction Method for Neurological PET Studies of Awake Laboratory Animals. Mol Imaging Biol 10, 315–324 (2008). https://doi.org/10.1007/s11307-008-0157-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11307-008-0157-0