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SU‐E‐J‐66: Effects of Noise in 4D‐CT On Deformable Image Registration and Derived Ventilation Data
Author(s) -
Latifi K,
Huang T,
Feygelman V,
Budzevich M,
Stevens C,
Dilling T,
Moros E,
Van Elmpt W,
Dekker A,
Zhang G
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4814278
Subject(s) - noise (video) , image registration , image noise , standard deviation , mathematics , nuclear medicine , artificial intelligence , computer science , medicine , image (mathematics) , statistics
Purpose: Deformable image registration (DIR) and 4D‐CT have been proposed to generate ventilation images. Quantum noise is common in CT images. This study focuses on the effects of noise in 4D‐CT on DIR and the derived ventilation data. Methods: Diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B‐Spline were used to register the end‐inspiration phase to the end‐expiration phase of 6 4D‐CT sets with landmarks delineated on different phases, called point‐validated pixel‐based breathing thorax models (POPI). Landmarks at expiration were mapped to inspiration using DIR deformation matrices (DIRDM) for each POPI model. Target registration errors (TRE) were calculated as the distances between the delineated and the mapped landmarks. Gaussian noise with different standard deviations (SD) of the amplitude was added to the POPI models to simulate different levels of quantum noise. Ventilation estimations were performed by calculating the volume change geometrically, based on the DIRDM. Ventilation images with different CT noise levels were compared using Dice similarity coefficient (DSC). Results: The root mean square (RMS) values of the landmark TRE over the 6 POPI models for the 4 DIR algorithms were stable when the noise level was below SD=150 Hounsfield Units (HU), and increased with the noise level. The most accurate DIR was DD with mean RMS of 1.5±0.5 and 1.8+−0.5 mm at the added noise SD=0 and 200 HU respectively. The DSC values between the ventilation images with and without added noise decreased with the noise level. The most consistent DIR was DM with mean DSC=0.89+− 0.01 and 0.66+−0.02 for the top 50% ventilation volumes with the added noise of 0 and 30, 0 and 200 HU respectively. Conclusion: While the landmark TRE was stable with noise level for low noise, the differences between ventilation images increased, indicating that 4D‐CT based ventilation imaging is sensitive to image noise. This work was partially supported by a grant from the Varian Medical Systems, Inc.