Effects of quantum noise in 4D-CT on deformable image registration and derived ventilation data
| Autor: | Craig W. Stevens, Andre Dekker, Kujtim Latifi, Mikalai M. Budzevich, Tzung Chi Huang, Thomas J. Dilling, Vladimir Feygelman, Eduardo G. Moros, Wouter van Elmpt, Geoffrey Zhang |
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| Přispěvatelé: | Radiotherapie, RS: GROW - School for Oncology and Reproduction |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
Gaussian Image Processing Image Processing Computer-Assisted: methods Optical flow Image registration Signal-To-Noise Ratio Standard deviation Root mean square symbols.namesake Signal-to-noise ratio Computer-Assisted Four-Dimensional Computed Tomography: methods Hounsfield scale Computer-Assisted: methods Image noise Image Processing Computer-Assisted Radiology Nuclear Medicine and imaging Computer vision Four-Dimensional Computed Tomography Radiological and Ultrasound Technology business.industry Quantum noise Noise symbols Breathing Artificial intelligence business Pulmonary Ventilation Algorithms |
| Zdroj: | Physics in Medicine and Biology, 7661-7672. IOP Publishing Ltd. STARTPAGE=7661;ENDPAGE=7672;ISSN=0031-9155;TITLE=Physics in Medicine and Biology |
| ISSN: | 0031-9155 |
| Popis: | Quantum noise is common in CT images and is a persistent problem in accurate ventilation imaging using 4D-CT and deformable image registration (DIR). This study focuses on the effects of noise in 4D-CT on DIR and thereby derived ventilation data. A total of six sets of 4D-CT data with landmarks delineated in different phases, called point-validated pixel-based breathing thorax models (POPI), were used in this study. The DIR algorithms, including diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B-spline, were used to register the inspiration phase to the expiration phase. The DIR deformation matrices (DIRDM) were used to map the landmarks. Target registration errors (TRE) were calculated as the distance errors between the delineated and the mapped landmarks. Noise of Gaussian distribution with different standard deviations (SD), from 0 to 200 Hounsfield Units (HU) in amplitude, was added to the POPI models to simulate different levels of quantum noise. Ventilation data were calculated using the ΔV algorithm which calculates the volume change geometrically based on the DIRDM. The ventilation images with different added noise levels were compared using Dice similarity coefficient (DSC). The root mean square (RMS) values of the landmark TRE over the six POPI models for the four DIR algorithms were stable when the noise level was low (SD |
| Databáze: | OpenAIRE |
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