9 March 2017 Motion vector field upsampling for improved 4D cone-beam CT motion compensation of the thorax
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To improve the accuracy of motion vector fields (MVFs) required for respiratory motion compensated (MoCo) CT image reconstruction without increasing the computational complexity of the MVF estimation approach, we propose a MVF upsampling method that is able to reduce the motion blurring in reconstructed 4D images. While respiratory gating improves the temporal resolution, it leads to sparse view sampling artifacts. MoCo image reconstruction has the potential to remove all motion artifacts while simultaneously making use of 100% of the rawdata. However the MVF accuracy is still below the temporal resolution of the CBCT data acquisition. Increasing the number of motion bins would increase reconstruction time and amplify sparse view artifacts, but not necessarily the accuracy of MVF. Therefore we propose a new method to upsample estimated MVFs and use those for MoCo. To estimate the MVFs, a modified version of the Demons algorithm is used. Our proposed method is able to interpolate the original MVFs up to a factor that each projection has its own individual MVF. To validate the method we use an artificially deformed clinical CT scan, with a breathing pattern of a real patient, and patient data acquired with a TrueBeamTM4D CBCT system (Varian Medical Systems). We evaluate our method for different numbers of respiratory bins, each again with different upsampling factors. Employing our upsampling method, motion blurring in the reconstructed 4D images, induced by irregular breathing and the limited temporal resolution of phase–correlated images, is substantially reduced.
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Sebastian Sauppe, Sebastian Sauppe, Christopher M. Rank, Christopher M. Rank, Marcus Brehm, Marcus Brehm, Pascal Paysan, Pascal Paysan, Dieter Seghers, Dieter Seghers, Marc Kachelrieß, Marc Kachelrieß, } "Motion vector field upsampling for improved 4D cone-beam CT motion compensation of the thorax", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322E (9 March 2017); doi: 10.1117/12.2255603; https://doi.org/10.1117/12.2255603

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