AUTHOR=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 , Kumar Shiyam Sundar Lalith 

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

JOURNAL=Frontiers in Physics

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.1034783

DOI=10.3389/fphy.2022.1034783

ISSN=2296-424X

ABSTRACT=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.