Open AccessEFFECTS OF SMOOTH, MEDIUM SMOOTH AND MEDIUM RECONSTRUCTION KERNELS ON IMAGE QUALITY IN THREE-PHASE CT OF LIVER
Author(s) -
NUR AMALINA AMERUDDIN,
Sulaiman Md Dom,
Mohd Hafizi Mahmud
Publication year - 2021
Publication title -
malaysian applied biology/malaysian applied biology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.153
H-Index - 8
eISSN - 2462-151X
pISSN - 0126-8643
DOI - 10.55230/mabjournal.v50i2.1974
Subject(s) - iterative reconstruction , kernel (algebra) , image quality , image noise , noise (video) , attenuation , mathematics , tomography , lesion , medicine , image (mathematics) , artificial intelligence , nuclear medicine , computer science , radiology , physics , optics , pathology , combinatorics
Reconstruction kernel is one of the parameters that affects the computed tomography (CT) image quality. This study aimed to evaluate the effects of applying three different reconstruction kernels on image quality in 3-phased CT of the liver. A total of 63 CT liver images including normal liver (n = 43) and liver lesion (n = 20) were retrospectively reviewed. Smooth (B20f), medium smooth (B30f) and medium (B40f) reconstruction kernels were employed in the image reconstruction process. Mean attenuation, image noise, and signal-to-noise ratio (SNR) values from each kernel reconstruction were quantified and compared among those kernels using One Way Analysis of Variance (ANOVA) statistical analysis. Significant changes in image noise and SNR were observed in the normal liver (p 0.05). Application of smooth (B20f), medium smooth (B30f), and medium (B40f) kernel reconstructions would significantly affect the image noise and SNR in the normal liver of CT images instead of liver lesions. Hence, proper selection of reconstruction kernel is important in CT images reconstruction to improve precision in diagnostic CT interpretation.