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Development of a computational phantom for validation of automated noise measurement in CT images
Anam C.a, Sutanto H.a, Adi K.a, Budi W.S.a, Muhlisin Z.a, Haryanto F.b, Matsubara K.c, Fujibuchi T.d, Dougherty G.e
a Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Tembalang, Semarang, Central Java, 50275, Indonesia
b Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, West Java, Indonesia
c Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
d Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, 812-8582, Japan
e Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, 93012, United States
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IOP Publishing LtdThe purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from −1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (NG). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (NM) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the NM value was much smaller than the NG. For kernel sizes from 17 × 17 to 21 × 21 pixels, the NM value was about 90% of NG. And for kernel sizes of 23 × 23 pixels and above, NM is greater than NG. It was also found that even with small kernel sizes the relationship between NM and NG is linear with R2 more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Automated noise calculation,Computational phantom,CT dose optimization,Image quality[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was funded by the Riset Publikasi Internasional Bereputasi Tinggi (RPIBT), Diponegoro University (Contract Number: 329-116/UN7.P4.3/ PP/2019).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/2057-1976/abb2f8[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]