Katkar Rujuta, Steffy Douglas D, Noujeim Marcel, Deahl S Thomas, Geha Hassem
Assistant Professor, Department of Comprehensive Dentistry, University of Texas Health Science Center, San Antonio, TX, USA.
Past Resident, Department of Comprehensive Dentistry, University of Texas Health Science Center, San Antonio, TX, USA.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2016 Nov;122(5):646-653. doi: 10.1016/j.oooo.2016.08.006. Epub 2016 Aug 18.
The aim of this study was to evaluate the effect of milliamperage, number of basis images, and export slice thickness on contrast-to-noise ratio (CNR) and confidence in detecting mandibular canal.
Two phantoms were used. Each phantom consisted of a dry mandible with an epoxy resin bone tissue substitute block and a water-equivalent block, submerged in water. Each mandible was scanned with a Morita 3D Accuitomo cone beam computed tomography (CBCT) machine (Morita, Kyoto, Japan). Scans were made with 180-degree and 360-degree rotations, at 4, 6, and 8 mA. Each scan was exported in Digital Imaging and Communications in Medicine (DICOM) format at slice thicknesses of 0.125 mm, 0.25 mm, 0.75 mm, and 1.0 mm, resulting in 24 image sets for each phantom. The CNR was calculated. Variables were analyzed using factorial analysis of variance. The scans were also evaluated by five observers who were asked to state their confidence in detecting the mandibular canal on a four-point confidence scale.
Increasing the number of basis images, milliamperage, or export slice thickness significantly increased the CNR. Reducing the export slice thickness improved observers' confidence in detecting the mandibular canal.
The CBCT acquisition settings should be carefully chosen, depending on specific diagnostic tasks. The lowest slice thickness equal to the voxel size should always be used for exporting CBCT data despite the higher noise.
本研究旨在评估毫安数、基础图像数量和输出层厚对下颌神经管对比度噪声比(CNR)及检测下颌神经管信心的影响。
使用了两个体模。每个体模均由一个带有环氧树脂骨组织替代块和一个水等效块的干燥下颌骨组成,浸没于水中。每个下颌骨均使用森田3D Accuitomo锥形束计算机断层扫描(CBCT)机(日本京都森田公司)进行扫描。扫描分别在4、6和8毫安的条件下进行180度和360度旋转。每次扫描均以医学数字成像和通信(DICOM)格式输出,层厚分别为0.125毫米、0.25毫米、0.75毫米和1.0毫米,每个体模由此产生24组图像。计算CNR。使用析因方差分析对变量进行分析。还由五名观察者对扫描图像进行评估,要求他们以四点信心量表说明检测下颌神经管的信心。
增加基础图像数量、毫安数或输出层厚可显著提高CNR。减小输出层厚可提高观察者检测下颌神经管的信心。
应根据具体诊断任务仔细选择CBCT采集设置。尽管噪声较高,但输出CBCT数据时应始终使用等于体素大小的最低层厚。