背景:在牙科临床实践中,锥形束计算机断层扫描(CBCT)通常用于帮助从业者识别根管系统的复杂形态。但由于其分辨率的局限性,某些小的解剖结构仍然不能在CBCT上准确识别。这项研究的目的是在深度学习模型的帮助下,对提取的人类牙齿的CBCT图像进行图像超分辨率(SR)处理,并比较CBCT之间的差异,SRCT,和显微计算机断层扫描(Micro-CT)图像通过三维重建。
方法:选择并修改深度学习模型(Basicvsr++)。该数据集由符合纳入标准的171颗拔牙组成,40颗上颌第一磨牙作为训练组,40颗上颌第一磨牙以及来自其他牙齿位置的91颗牙齿作为外部测试组。相应的CBCT,SRCT,使用MimicsResearch17.0重建测试集中每颗牙齿的Micro-CT图像,并记录三组的根管识别率。测量以下参数:硬组织体积(V1),牙髓腔和根管系统的体积(V2),孔口下可见根管的长度(VL-X,其中X代表特定的根管),根管冠状轴与牙齿长轴的交角(周四X,其中X代表特定的根管)。数据在CBCT和SRCT图像之间采用配对样本t检验和Wilcoxon检验分析进行统计学分析。以微CT图像的测量为黄金标准。
结果:使用超分辨率程序成功处理了所有测试牙齿的图像。在4道上颌第一磨牙中,CBCT组MB2检出率为72%(18/25),SRCT组92%(23/25),Micro-CT组100%(25/25)。除4根下颌第一磨牙外,所有受试牙齿的硬组织体积的差异均明显小于CBCT和Micro-CT(P<0.05)。在所有测试的牙齿中,牙髓腔和根管系统的体积均获得了相似的结果(P<0.05)。至于孔口下可见根管的长度,在大多数根管中,SRCT和Micro-CT之间的差异明显小于CBCT和Micro-CT之间的差异(P<0.05)。
结论:本研究开发的深度学习模型有助于优化CBCT中拔牙的根管形态。并可能有助于上颌第一磨牙中MB2的鉴定。
BACKGROUND: In dental clinical practice, cone-beam computed tomography (CBCT) is commonly used to assist practitioners to recognize the complex morphology of root canal systems; however, because of its resolution limitations, certain small anatomical structures still cannot be accurately recognized on CBCT. The purpose of this study was to perform image super-resolution (SR) processing on CBCT images of extracted human teeth with the help of a deep learning model, and to compare the differences among CBCT, super-resolution computed tomography (SRCT), and micro-computed tomography (Micro-CT) images through three-dimensional reconstruction.
METHODS: The deep learning model (Basicvsr++) was selected and modified. The dataset consisted of 171 extracted teeth that met inclusion criteria, with 40 maxillary first molars as the training set and 40 maxillary first molars as well as 91 teeth from other tooth positions as the external test set. The corresponding CBCT, SRCT, and Micro-CT images of each tooth in test sets were reconstructed using Mimics Research 17.0, and the root canal recognition rates in the 3 groups were recorded. The following parameters were measured: volume of hard tissue (V1), volume of pulp chamber and root canal system (V2), length of visible root canals under orifice (VL-X, where X represents the specific root canal), and intersection angle between coronal axis of canal and long axis of tooth (∠X, where X represents the specific root canal). Data were statistically analyzed between CBCT and SRCT images using paired sample t-test and Wilcoxon test analysis, with the measurement from Micro-CT images as the gold standard.
RESULTS: Images from all tested teeth were successfully processed with the SR program. In 4-canal maxillary first molar, identification of MB2 was 72% (18/25) in CBCT group, 92% (23/25) in SRCT group, and 100% (25/25) in Micro-CT group. The difference of hard tissue volume between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT in all tested teeth except 4-canal mandibular first molar (P < .05). Similar results were obtained in volume of pulp chamber and root canal system in all tested teeth (P < .05). As for length of visible root canals under orifice, the difference between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT (P < .05) in most root canals.
CONCLUSIONS: The deep learning model developed in this study helps to optimize the root canal morphology of extracted teeth in CBCT. And it may be helpful for the identification of MB2 in the maxillary first molar.