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A Novel Objective Approach to Identify Scan Outliers in Deformable Image Registration for Longitudinal Datasets

Z Saleh

Z Saleh1*, M Thor2 , G Sharp3 , X Tang4 , T Volpe5 , R Margiasso6 , H Veeraraghavan7 , L Muren8 , J Deasy9 , (1) Memorial-Sloan Kettering West Harrison, West Harrison, New York, (2) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Massachusetts General Hospital, Boston, MA, (4) Memorial Sloan Kettering Cancer Center, West Harrison, NY, (5) ,,,(6) Memorial Sloan Kettering West Harrison, West Harrison, New York, (7) Memorial Sloan-Kettering Cancer Center, New York, NY, (8) Aarhus University, Aarhus, Aarhus, (9) Memorial Sloan Kettering Cancer Center, New York, NY


SU-E-J-95 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

The current practice for evaluating deformable image registration (DIR) is commonly subjective as it involves input from an observer due to lack of an absolute ground truth. We have therefore developed an automated objective method to evaluate DIR in order to identify scan outliers for longitudinal datasets.

The imaging dataset consisted of nine repeated CT scans (CT1-9) from four prostate patients. Six “similar” CT scans (CT1-6) from one patient (PT1) were used as ground truth meanwhile three other CT scans (CT7-9) from PT2-4 served as scan outliers. Voxel-by-voxel DIR-related uncertainties (Distance Discordance Metric, DDM) were evaluated on groups of five CT scans for the following ten scenarios: Scenario 1-5: five out of six CT scans (CT1-6) from PT1 using leave-one-out technique were used; Scenario 6-8: single scan from PT1 was replaced by another CT scan from PT2-4; and Scenario 9-10: two to three CT scans from PT1 were replaced by CT scans from PT2-4. For each scenario, the DDM map was superimposed on CT-1 and two-sample t-test was performed to compare the uncertainty distributions for all 10 scenarios.

The mean DDM values were 3.8-4.2 mm for the first five scenarios, 5.1-6.7 mm for scenario 6-8, and 8.3 mm and 8.9 mm for scenario 9 and scenario 10, respectively. The two-sample t-test showed that the DDM distributions for scenarios 1-5 have a similar mean while the distributions for scenarios 6-10 have statistically different means compared to scenario 1-5.

The DIR uncertainty distributions as estimated from our method on a set of images from the same patient are of similar magnitude. When images for a patient are replaced by images from other patients, the distribution of the uncertainties changes considerably, motivating the use of DDM as a metric to automatically detect scan outliers.

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