Impact of CT slice thickness on volume and dose evaluation during thoracic cancer radiotherapy

Abstract

IntroductionAccurate delineation of targets and organs at risk (OAR) is required to ensure treatment efficacy and minimize risk of normal tissue toxicity with radiotherapy. Therefore, we evaluated the impacts of computed tomography (CT) slice thickness and reconstruction methods on the volume and dose evaluations of targets and OAR.Patients and methodsEleven CT datasets from patients with thoracic cancer were included. 3D images with a slice thickness of 2 mm (2–CT) were created automatically. Images of other slice thickness (4–CT, 6–CT, 8–CT, 10–CT) were reconstructed manually by the selected 2D images using two methods; internal tumor information and external CT Reference markers. Structures and plans on 2–CT images, as a reference data, were copied to the reconstructed images.ResultsThe maximum error of volume was 84.6% for the smallest target in 10–CT, and the maximum error (≥20 cm3) was 10.1%, 14.8% for the two reconstruction methods, internal tumor information and external CT Reference, respectively. Changes in conformity index for a target of <20 cm3 were 5.4% and 17.5% in 8–CT. Changes on V30 and V40 of the heart were considerable. In the internal tumor information method, volumes of hearts decreased by 3.2% in 6–CT, while V30 and V40 increased by 18.4% and 46.6%.ConclusionThe image reconstruction method by internal tumor information was less affected by slice thickness than the image reconstruction method by external CT Reference markers. This study suggested that before positioning scanning, the largest section through the target should be determined and the optimal slice thickness should be estimated.

Automatic slice thickness measurement on three types of Catphan CT phantoms

“…In terms of image quality, they are required to evaluate many quantitative parameters such as spatial resolution, image noise, low contrast, alignment, and slice thickness [11][12][13]. The latter parameter is important in the quality of CT images, since it has a direct impact on the image noise and accuracy of the 3D reconstruction of an image [14,15].…”

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase

“…The super-resolution imaging aims at improving the resolution in the axial plane by creating more pixels, while our goal is to improve the resolution in the longitudinal direction by creating more slices (densely sliced CT reconstruction from sparsely sliced CT data). Although creating a whole slice is much more difficult than creating a new pixel, this kind of study would be worthwhile because longitudinal resolution plays an important role on disease diagnosis [ 15 , 16 ]. However, we found that there are rare researches related to deep-learning-based resolution enhancement in the longitudinal direction.…”

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso‐center, and reconstruction filter

“…Slice thickness is one of the important parameters, and it has to be optimized as needed. Slice thickness affects the cross‐plane resolution of the clinical image, which then impacts the accuracy of the size determination of the organ 11,12 . The slice thickness also directly impacts image noise.…”