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Automated Calculation of Water-equivalent Diameter (DW) Based on AAPM Task Group 220
Anam C.a,b, Haryanto F.b, Widita R.b, Arif I.b, Dougherty G.c
a Department of Physics, Faculty of Mathematics and Sciences, Diponegoro University, Semarang, Indonesia
b Department of Physics, Faculty of Mathematics and Sciences, Bandung Institute of Technology, Bandung, Indonesia
c Department of Applied Physics, California State University Channel Islands (CSUCI), California, 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]© This work is licensed under a Creative Commons Attribution 3.0 Unported License.The purpose of this study is to accurately and effectively automate the calculation of the water-equivalent diameter (DW) from 3D CT images for estimating the size-specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated DW calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of DW based on the automated contouring for every slice (DW, all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW, all using DW, n=1 (central slice along longitudinal axis) produces percentage differences of-0.92% ± 3.37% and 6.75%± 1.92%, and estimating DW, all using DW, n=9 produces percentage differences of 0.23% ± 0.16% and 0.87% ± 0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size-specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74% ± 2.82% and-4.35% ± 1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00% ± 0.46% and-0.60% ± 0.24% for thorax and head examinations, respectively.[/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]Adolescent,Adult,Aged,Aged, 80 and over,Female,Head,Humans,Image Processing, Computer-Assisted,Male,Middle Aged,Monte Carlo Method,Phantoms, Imaging,Radiation Dosage,Radiography, Thoracic,Tomography, X-Ray Computed,Young Adult[/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]water-equivalent diameter (DW), size-specific dose estimates (SSDE), volume CT dose index (CTDIVOL), patient dose[/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][/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/10.1120/jacmp.v17i4.6171[/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]