Introduction
Low-dose cone-beam computed tomography (CBCT) for three-dimensional (3D) imaging of the maxillo-facial structures is increasingly used in the latest years in the medical and dental field, and it extends to a wide range of applications [
1‐
3]. Accurate visualization of the face, jaws, and its components are important for clinical diagnostics and decision-making [
4]. Nowadays, the application of 3D cephalometric analysis plays an important role in cases of complex maxillo-facial abnormalities [
5,
6] and for the evaluation of growth or treatment outcomes [
7‐
9]. Accurate 3D surface-rendered models, especially of the hard tissues, are crucial in applications such as virtual surgical planning on patients undergoing orthognathic surgery, during dental implant and prosthetic procedures, and when simulating treatment outcomes [
10‐
15].
Surface-rendered 3D models of the hard tissues derived from the CBCT Digital Imaging and Communication in Medicine (DICOM) data that are originally composed of voxels, each with its own gray value based on the radiation absorbed by the tissues during the scan. By means of specific software applications, reconstructions are possible in sagittal, coronal, and axial planes, allowing the operator to scroll through the tissue structures in any direction of interest. Most applications also include reconstruction techniques allowing image extraction out of the DICOM data similar to those from conventional radiographic techniques, such as orthopantomograms and lateral cephalometric radiographs [
16]. In order to construct a 3D digital model of specific tissues out of the original voxel-based data, a specific process called segmentation has to be followed. Briefly, the operator provides the software an upper and a lower threshold matching the gray-level range of the voxels which specifies the tissues of interest. The software then discards all data outside these limits and depicts only the voxels within the set threshold values.
Factors influencing the quality and accuracy of the models provided by the segmentation process can be divided in three main categories [
17]. The first comprises CBCT system factors, such as scanner type, field of view settings, and voxel size settings [
18‐
21]. The second category is patient-related factors such as positioning of the patient in the scanner, metal artifacts, and the soft tissue covering [
22‐
25]. The third are operator-related factors, such as the segmentation process itself, the employed software, and the operator performing the segmentation [
26‐
29]. Most studies published on this particular subject were based on research using dry skulls.
The use of the dry skull without any soft tissue substitute may present a drawback, as it does not simulate the human anatomy in real life. The soft tissues are sources of scattering radiation during a radiographic examination, resulting in deterioration of the signal-to-noise ratio and, subsequently, of the gray-level value differentiation in voxels between different density tissues, presumably resulting to lower bone image quality [
30]. As a result, measurements obtained from images on a human dry skull may deviate from the “true” values if they be obtained from the same subjects with soft tissue coverage. Interpretation of these results may be misleading for decision-making in clinical practice. To overcome this error, some studies introduced latex balloons filled with water as a soft tissue equivalent [
31,
32]. One of the drawbacks of this method is that it does not reflect the soft tissue properties or distributions in real life. Alternatively, human cadavers may represent better the human anatomy, which are, however, difficult to obtain. The effect of soft tissue presence on the segmentation process and the resulting 3D surface-rendered volumetric models is not yet well documented, specifically when all the above-mentioned affecting side parameters are standardized.
Therefore, the aim of this study is to investigate whether the soft tissue presence affects the segmentation accuracy of the 3D surface-rendered volumetric models from cone-beam CT in a setting in which other affecting parameters were controlled.
Discussion
The aim of this study was to investigate whether the soft tissue covering affects the segmentation accuracy of the 3D surface-rendered volumetric models derived from cone-beam CT, which to our knowledge has not been studied previously. Well-defined linear and angular measurements combined with volume comparisons demonstrated as an accurate tool to measure differences between 3D digital models were used as a method to illustrate the soft tissue effect [
41‐
43]. Our results indicate that the soft tissue presence seems to affect the accuracy of the 3D hard tissue model of both the maxillo-facial complex and the mandible obtained from a cone-beam CT, however, below a generally accepted level of clinical significance of 1 mm [
44]. It must nevertheless be noted that this level of accuracy may not meet the requirements of all applications, especially where higher precision is paramount [
45,
46].
All segmentations, linear and angular measurements, and volume comparison procedures were performed repeatedly by one single operator. The repeatability of the segmentation process was found excellent with the differences between repeated segmentations below the set voxel size of 0.3 mm. The ICCs of the linear and angular measurements were classified as “excellent” which is in agreement with previous studies reporting excellent reliability of this method within and among different observers [
43,
47]. The differences of the linear and angular measurements for the maxillo-facial complex were below 1 mm. In the mandible, larger differences were observed which could have possibly demonstrated a considerable effect of soft tissue on the inaccuracy of the models. However, carefully analyzing these results and taking into consideration the maceration and drying process involved in this study, such a conclusion might be found misleading. Previous studies showed an effect of drying on bone morphology particularly concerning a mandible. The posterior transversal dimension of the pig mandibles were affected up to an extent of 2.7 % [
48]. This is in agreement with the present study, in which both the linear and angular measurements and the superimpositions showed differences for the mandibular transversal measurements. Although comparing the maxillo-facial complexes did not reveal such dimensional distortion as seen in the mandible, we could not presume that this region was not affected. In order to minimize any effect of the possible transversal deformation due to the maceration and drying process involved, the mandible and the maxillo-facial complex models were split in their midsagittal planes and the left and right parts were registered and compared separately. The characteristic measure of the inaccuracy of a tested model compared to the reference is the root-mean-square error or RMSE that serves as a measure of how far is the average error from 0, i.e., the distance difference, between the surfaces of the two models. Whereas the mean RMSE values of the maxillo-facial complex barely differ before and after the splitting, that of the mandible significantly decreased to less than 1 mm, which is generally considered as a clinical acceptable threshold [
44]. Further, all mean differences between the surfaces were negative values. This shows a trend in which the dry skull models lie slightly within the cadaver models when the two models were optimally superimposed, indicating the dry skull models being smaller in general which could be caused by a change in humidity [
39]. Although this is a consistent finding, the differences never exceeded the limits of the voxel size of 0.3 mm used in this study. These findings confirm the results from previous studies demonstrating that interpretation of research data, in which dry skulls were used, especially concerning the mandible, should be analyzed with the knowledge that significant changes may have taken place in the cranio-facial dimensions [
48].
Special consideration was taken to control the possible affecting parameters, in order to focus on the effect of soft tissue presence in the accuracy of the 3D model, although this would partly limit the external validity of this study. All dry skulls were scanned using the same cone-beam CT unit, with identical scan settings and similar positioning of the skull in the field of view. Given the fact that the voxel size is another affecting parameter, adjusting the setting to a higher scan resolution could compensate the effect of soft tissue presence. Concerning this parameter, it has been indicated that the accuracy of the models appears to be connected to the voxel size [
49], although the differences were small. However, others did not find increased accuracy of linear measurements on segmented surface models decreasing the voxel size from 0.4 to 0.25 mm [
34]. In our study, the voxel size was fixed to 0.3 mm.
Head positioning is also considered as a possible affecting parameter. The accuracy of linear measurements using wires glued on the skull reported no clinically relevant effect [
50]. Head positioning has been shown to be an affecting variable in the accuracy of the 3D cephalometric measurements based on 3D CBCT surface images and the reliability of linear measurements in some studies [
50‐
52] and to be an insignificant factor for measurement accuracy in others [
19]. Since the range of positioning deviations for error-free measurements are not yet known, we specifically aimed at achieving similar positioning using the laser reference lines of the unit to control the positioning as a parameter affecting the accuracy.
Two other parameters assumed to have a possible effect on the segmentation accuracy are the operator performing the segmentation process and the software utilized. The software package employed requires the operator to define individual hard tissue threshold values for each skull and cadaver. A previous study showed that a commercial software company produced more accurate surface models compared to an experienced 3D clinician [
33]. This higher accuracy could be the result of a more experienced operator performing the segmentation or ascribed to the use of different tools and methods provided by different software packages. Both these affecting factors were well controlled in this study by utilizing one operator with a high intraobserver reliability performing segmentations on both the cadaver and dry skull and the use of one professional software package.
This study used a limited but unique sample of paired CBCT DICOM datasets from seven human cadaver heads and their respective dry skulls, contributing to a method in which the surface models with and without a natural soft tissue coverage could be compared. The effect of the scattering radiation due to the presence of soft tissues, resulting in deterioration of the signal-to-noise ratio and subsequently of the gray-level value differentiation in voxels, was well simulated. The method has a clear advantage over the use of artificial media, such as water balloons, to mimic the coverage of soft tissues [
32,
53]. However, it has to be acknowledged that this method still contains a number of limitations. The preservation and storage of the cadavers was done in formaldehyde embalming fluid which could have increased the soft tissue thickness, altered the bone properties of the cadaver heads, and subsequently influenced the scattering radiation during the radiographic examination [
54]. Therefore, in this study, the cadaver heads were not meant for representing the exact human anatomy of a living being. For the purpose of the present study, they were merely used as ex vivo equivalent of human heads to investigate the effect of soft tissue coverage.
Further research should focus on other affecting parameters resulting in a 3D surface-rendered model including the hardware, software, and operator-dependent parameters. By adjusting and improving these parameters, the effect of soft tissue presence should be minimized resulting in more accurate 3D surface-rendered models suitable for high-accuracy demanding applications.
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