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Improving Image Quality in 4D-CT Scans Using Deformable Registration and Selective Averaging

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E Aliotta

E Aliotta*, D Thomas, S Gaudio, B White, S Jani, P Lee, J Lamb, D Low, University of California, Los Angeles, Los Angeles, CA

TU-C-141-6 Tuesday 10:30AM - 12:30PM Room: 141

Purpose: To improve image quality in low-dose 4D-CT using a selective averaging algorithm that combines images acquired under free breathing and registered to a single breathing phase.

Methods: Five patients were imaged with a low-pitch helical protocol on a 64-slice scanner during free patient breathing, as part of an IRB-approved research protocol. 25 low dose scans were performed in order to image the lungs at varying breathing phases to generate a breathing motion model. The first scan was registered to the subsequent 24 using a b-spline registration algorithm to produce 25 representations of a single patient scan geometry. These images were averaged using k-means clustering (k=2 clusters) in conjunction with the arithmetic mean. A gradient mapping algorithm assigned the appropriate mean value to each voxel to produce a composite image. This algorithm calculates the image gradient at each voxel and assigns a cluster mean when the gradient magnitude is above a threshold, otherwise assigning the arithmetic mean. The composite image was compared with the first image in the set as well as the arithmetic mean of all 25 scans. Image noise was calculated in an axial region of the liver. Sharpness was measured as the sum of gradient magnitudes across the image.

Results: By combining gradient selective k-means clustering with the arithmetic mean, image noise was reduced by 78% while maintaining sharpness within 14.5% of the reference image. This improves on the arithmetic mean in sharpness by 14% with less than a 1% increase in noise.

Conclusion: Results indicate that 4D-CT image quality can be improved by co-registration followed by a gradient selective averaging algorithm. This method greatly reduces image noise while maintaining sharpness that is lost in a simple averaging algorithm.

Funding Support, Disclosures, and Conflict of Interest: This work supported in part by NIH R01CA096679

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