A scale space theory based motion correction approach for dynamic PET brain imaging studies

Sebastian Gutschmayer, Otto Muzik, Zacharias Chalampalakis, Daria Ferrara, Josef Yu, Killian Kluge, Ivo Rausch, Ronald Boellaard, Sandeep S. V. Golla, Sven Zuehlsdorff, Hartwig Newiger, Thomas Beyer, Lalith Kumar Shiyam Sundar

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1 Citation (Scopus)


Aim/Introduction: Patient head motion poses a significant challenge when performing dynamic PET brain studies. In response, we developed a fast, robust, easily implementable and tracer-independent brain motion correction technique that facilitates accurate alignment of dynamic PET images. Materials and methods: Correction of head motion was performed using motion vectors derived by the application of Gaussian scale-space theory. A multiscale pyramid consisting of three different resolution levels (1/4x: coarse, 1/2x: medium, and 1x: fine) was applied to all image frames (37 frames, framing of 12 × 10s, 15 × 30s, 10 × 300s) of the dynamic PET sequence. Frame image alignment was initially performed at the coarse scale, which was subsequently used to initialise coregistration at the next finer scale, a process repeated until the finest possible scale, that is, the original resolution was reached. In addition, as tracer distribution changes during the dynamic frame sequence, a mutual information (MI) score was used to identify the starting frame for motion correction that is characterised by a sufficiently similar tracer distribution with the reference (last) frame. Validation of the approach was performed based on a simulated F18-fluoro-deoxy-glucose (FDG) dynamic sequence synthesised from the digital Zubal phantom. Inter-frame motion was added to each dynamic frame (except the reference frame). Total brain voxel displacement based on the added motion was constrained to 25 mm, which included both translation (0–15 mm in x, y and z) and rotation (0–0.3 rad for each Euler angle). Twenty repetitions were performed for each dataset with arbitrarily simulated motion, resulting in 20 synthetic datasets, each consisting of 36 dynamic frames (frame 37 was the reference frame). Assessment of motion correction accuracy across the dynamic sequence was performed based on the uncorrected/residual displacement remaining after the application of our algorithm. To investigate the clinical utility of the developed algorithm, three clinically cases that underwent list-mode PET imaging utilising different tracers ([18F]-fluoro-deoxy-glucose [18F]FDG [18F]-fluoroethyl-l-tyrosine [18F]FET [11C]-alpha-methyl-tryptophan [11C]AMT), each characterised by a different temporal tracer distribution were included in this study. Improvements in the Dice score coefficient (DSC) following frame alignment were evaluated as the correlation significance between the identified displacement for each frame of the clinical FDG, FET and AMT dynamic sequences. Results: Sub-millimetre accuracy (0.4 ± 0.2 mm) was achieved in the Zubal phantom for all frames after 5 min p. i., with early frames (30 s–180 s) displaying a higher residual displacement of ∼3 mm (3.2 ± 0.6 mm) due to differences in tracer distribution relative to the reference frame. The effect of these differences was also seen in MI scores; the MI plateau phase was reached at 35s p. i., 2.0 and 2.5 min p. i. At the coarse, medium and fine resolution levels, respectively. For the clinical images, a significant correlation between the identified (and corrected) displacement and the improvement in DSC score was seen in all dynamic studies (FET: R = 0.49, p < 0.001; FDG: R = 0.82, p < 0.001; AMT: R = 0.92, p < 0.001). Conclusion: The developed motion correction method is insensitive to any specific tracer distribution pattern, thus enabling improved correction of motion artefacts in a variety of clinical applications of extended PET imaging of the brain without the need for fiducial markers.

Original languageEnglish
Article number1034783
JournalFrontiers in Physics
Publication statusPublished - 28 Oct 2022


  • brain motion correction
  • dynamic PET imaging
  • frame-based algorithm
  • gaussian pyramid algorithms
  • scale-space

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