A scale space theory based motion correction approach for dynamic PET brain imaging studies - Gutschmayer, Sebastian; Muzik, Otto; Chalampalakis, Zacharias; Ferrara, Daria; Yu, Josef; Kluge, Kilian; Rausch, Ivo; Boellaard, Ronald; Golla, Sandeep S.V.; Zuehlsdorff, Sven; Newiger, Hartwig; Beyer, Thomas; et al. (Frontiers in Physics, 2022)
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. 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 x 10s, 15 x 30s, 10 x 300s) of the dynamic PET sequence. 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 characterized by a sufficiently similar tracer distribution with the reference (last) frame. Validation of the approach was performed based on a simulated F18-fluorodeoxyglucose (FDG) dynamic sequence derived from a digital Zubal phantom. In addition, clinical utility was assessed based on clinically acquired FDG; [18F]-fluoroethyl-L-tyrosine (FET) and [11C]-alpha-methyl-tryptophan (AMT) dynamic studies. Results: Sub-millimetre accuracy (0.4mm + 0.2mm) was achieved in the Zubal phantom for all frames after 5min p.i., with early frames (30s – 180s) displaying a higher residual displacement of 3mm (3.2mm + 0.6mm) 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, 2.0min and 2.5min p.i. at the coarse, medium and fine resolution levels, respectively. For the clinical images, a significant correlation between the identified (and corrected) displacement as well as improvement in Dice scores 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 artifacts in a variety of clinical applications of extended PET imaging of the brain without the need for fiducial markers.