SU-E-J-116: Validation of Deformable Image Registration Within the 4D Lung Using Two Commercial Algorithms and the User Guided Tool Reg Refine

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Abstract

Purpose:

To quantify the registration error between different phases of breathing using two commercially available deformable image registration algorithms. A secondary goal was to better understand the Reg Refine tool developed by MIM software and provide practical recommendations for its use.

Methods:

The 4D patient data sets of the POPI model were used as phantoms for this work. DIR was performed using MIM Maestro and the SmartAdapt module available within Eclipse. For each of six patients, 100 landmarks were deformed between inhalation and exhalation with their final location compared to the ground truth previously established by expert identification. The base rigid registration was varied to assess the effect on the DIR. For MIM, the Reg Refine tool was also used to try and improve the registration. The number of locked points was varied in addition to varying their location and the size of the window used for local box based alignment.

Results:

The average displacement for 5 of 6 patients was 6 – 8 mm. For these patients the mean error was low (1 – 1.5 mm) no matter which algorithm was used. SmartAdapt was less sensitive to the initial rigid registration, although both algorithms showed improvement when rigidly aligned to the base of the lung versus the apex. Reg Refine further improved accuracy albeit marginally. For the remaining patient the average displacement was larger (14 mm) and the Reg Refine tool showed marked improvement in reducing the mean and maximum errors from 5 and 35.5 mm to 1.1 and 4.8 mm using as little as ten points.

Conclusion:

The residual registration error using both SmartAdapt and MIM Maestro for 4D lung patients has been quantified on the order of 1 – 1.5 mm using the POPI model. Reg Refine was also shown effective at improving registrations within MIM Maestro.

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