Program Information

The Importance of Image Processing for Simulated Deformations

N Kirby

N Kirby*, O Morin , K Nie , J Chen , J Pouliot , UC San Francisco, San Francisco, CA.

Presentations

SU-C-18A-7 Sunday 1:00PM - 1:55PM Room: 18A

Purpose: Deformations can be digitally applied to patient images to evaluate the accuracy of deformable image registration (DIR). However, this can also deform image noise and artifacts, leaving a fingerprint of the underlying deformation that could skew accuracy determination. Image processing can be used to erase this fingerprint and create a more realistic DIR test scenario. The importance of image processing to simulated deformations is tested here.

Methods: These tests utilize a virtual pelvic phantom, made from a patient CT image, and a pelvic-shaped water phantom to acquire noise signatures. Two image-filtering techniques are tested here: a spatial convolution with a Gaussian (SCG) and an edge-preserving filter (EPF) that preferentially removes the Fourier components associated with noise. Four different processing scenarios are evaluated here. The first is no processing (NP). For the second, noise from the water phantom is added to the set of test images without filtering (NF). The third and fourth scenarios add noise after applying the SCG and EPF filtering methods. EPF provides the most realistic test scenario. These processing scenarios are tested for their effect on the spatial accuracy of the DIR algorithms from MIM Software and Velocity Medical Solution.

Results: For NP, NF, SCG, and EPF, the mean errors from MIM were 0.78, 1.11, 1.44, and 1.24 mm, respectively. The corresponding maximum errors for MIM were 21.7, 18.8, 26.1, and 21.1 mm, respectively. Velocity was relatively insensitive to these different processing scenarios, where the mean errors ranged from 1.66 to 1.74 mm and the maximum errors from 12.1 to 12.4 mm.

Conclusion: Velocity creates globally smooth deformations, whereas MIM exhibits much more local pliability. This local pliability makes MIM sensitive to differences in image processing. Thus, an objective evaluation of its accuracy with simulated deformations must utilize realistic noise scenarios, with carefully balanced image processing.



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