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Quantitative Transportation Assessment in Simulated Curved Canals Prepared with an Adaptive Movement System
Basic Research—Technology
Quantitative Transportation Assessment in Simulated Curved Canals Prepared with an Adaptive Movement System Emmanuel Jo~ ao Nogueira Leal Silva, DDS, MSc, PhD,* Michele Dias Nunes Tameir~ ao, DDS, MSc,* Felipe Gonc¸alves Belladonna, DDS, MSc,† Aline Almeida Neves, DDS, MSc, PhD,‡ Erick Miranda Souza, DDS, MSc, PhD,§ and Gustavo De-Deus, DDS, MSc, PhD* Abstract Introduction: The aim of this study was to evaluate the ability of the Twisted File Adaptive (TF Adaptive; SybronEndo, Orange, CA) system in maintaining the original profile of root canal anatomy. The ProTaper Universal (Dentsply Maillefer, Ballaigues, Switzerland) and Twisted File (TF) (SybronEndo) systems were used as reference techniques for comparison. Methods: Thirty simulated curved root canals manufactured in clear resin blocks were randomly assigned to 3 groups (n = 10) according to the instrumentation system: TF in rotary motion, TF in TF Adaptive motion, and ProTaper Universal. Color stereomicroscopic images from each block were taken exactly at the same position before and after instrumentation. All image processing and data analysis were performed with an open-source program (Fiji). Evaluation of canal transportation was obtained for 2 independent canal regions: straight and curved levels. Univariate analysis of variance and Tukey Honestly Significant Difference were used, and a cutoff for significance was set at alpha = 5%. Results: Instrumentation systems significantly influenced canal transportation (P = .000). A significant interaction between instrumentation system and root canal level (P = .000) was also found as follows: at the straight part, TF and TF Adaptive systems produced similar canal transportation, which was significantly lower than for the ProTaper Universal system; at the curved part, TF resulted in the lowest canal transportation followed by TF Adaptive and ProTaper Universal systems. Canal transportation was higher at the curved canal parts (P = .00). Conclusions: The TF in rotary motion produced overall less canal transportation in the curved portion when compared
From the *Department of Endodontics, School of Dentistry, Grande Rio University (UNIGRANRIO), Rio de Janeiro, Rio de Janeiro; †Department of Endodontics, Fluminense Federal University (UFF), Niteroi, Rio de Janeiro; ‡Department of Pediatric Dentistry and Orthodontics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Rio de Janeiro; and §Department of Dentistry II, Federal University of Maranh~ao, S~ao Luis, Maranh~ao, Brazil. Address requests for reprints to Dr Emmanuel Jo~ao Nogueira Leal da Silva, Rua Herotides de Oliveira, 61/902, Icaraı, Niteroi, RJ, Brazil. E-mail address: nogueiraemmanuel@ hotmail.com 0099-2399/$ - see front matter Copyright ª 2015 American Association of Endodontists. http://dx.doi.org/10.1016/j.joen.2015.02.028
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with the others tested systems. The ProTaper Universal system showed the highest canal transportation. (J Endod 2015;-:1–5)
Key Words Canal transportation, root canal instrumentation, R-phase, Twisted File, Twisted File Adaptive
C
anal shaping remains one of the challenging aspects of endodontic treatment because mishaps such as ledges, zips, perforations, and canal transportation can occur, particularly when preparing curved canals (1). The use of nickel-titanium (NiTi) instruments has enhanced the overall shaping quality and reduced the frequency of procedural errors (2, 3). In short, NiTi files have raised new perspectives for mechanical canal preparation, such as less debris extrusion, better centering ability, and reduced learning curve. Recently, the Twisted File Adaptive system (TF Adaptive; SybronEndo, Orange, CA) has been introduced onto the market. In theory, this system claims to maximize the advantages of reciprocation movement while minimizing its disadvantages. The TF Adaptive system uses a patented unique motion technology, which automatically adapts the movement according to the instrumentation stress input to the file. According to the manufacturer, when the TF Adaptive instrument is lightly stressed in the canal, the motor performs conventional clockwise movement, allowing better cutting efficiency and removal of debris. In contrast, during increased torsional stress, the movement automatically changes into a reciprocation mode. Moreover, TF Adaptive files have 3 unique design features: R-phase heat treatment, twisting of the metal, and special surface conditioning (4). Although this dynamic movement and file design look promising, little knowledge exists regarding the TF Adaptive shaping ability (5). To evaluate the shaping ability of different NiTi systems, the use of simulated curved canals in resin blocks have been largely used (6, 7). Although widespread, different methodological approaches have been proposed for the measurement of canal transportation in the simulated canal blocks model, resulting in the fact that, to the present date, there is no established golden standard method to evaluate morphologic changes in simulated canals. However, a common aspect between methodologies is the evaluation approach based on the establishment of evaluation points, usually randomly preselected by the operator. The direct and deliberated influence of the operator on the selection of points might be a source of bias. Thus, an automatic measurement approach able to reliably assess the entire extension of the simulated canal without direct operator interference would be remarkably welcome and appealing. The present study was designed to assess the ability of the TF Adaptive system in maintaining the original profile of canal anatomy in simulated curved canals of clear resin blocks. An innovative approach to evaluate the canal transportation is introduced and discussed using a skeletonization algorithm to calculate the canal transportation by automatic coregistration of the pre- and postinstrumentation images. ProTaper Universal (Dentsply Maillefer, Ballaigues, Switzerland) and Twisted File (TF) (SybronEndo) systems were used as reference techniques for comparison. The null hypothesis tested was that there are no significant differences in canal transportation among the tested NiTi systems.
Transportation Assessment Using Adaptive Movement System
1
Basic Research—Technology Materials and Methods Digital Image Acquisition Thirty simulated curved root canals in clear resin blocks with a 2% taper, 10-mm radius of curvature, 70 angle of curvature, and 17-mm length (Endo Training Blocks ISO 15, Dentsply Maillefer) were randomly assigned into 3 groups according to the instrumentation system (n = 10): TF group, TF Adaptive group, and ProTaper Universal group. Before any instrumentation procedure, a round silicon base with a rectangular slot fitting the microscope base was positioned under a color stereomicroscope (1005t Opticam stereomicroscope; Opticam, S~ao Paulo, Brazil) connected to a digital camera (CMOS 10 megapixels, Opticam). The rectangular slot matched the exact dimensions of the simulated canal blocks. Each specimen was then inserted into the slot, and images were taken and stored in TIFF format. After the instrumentation procedures, all blocks were imaged again following the same protocol. Ten blocks were used as a control group in which no instrumentation was performed at all. In this group, 1 color stereoscopic image of the block was taken first, removed, and replaced back to take another image of the same noninstrumented canal. Instrumentation Initially, for all groups, stainless steel size 10- and 15 K-file instruments (Dentsply Maillefer) were used to scout the canal up to the working length (WL), creating an initial and standardized glide path. TF Group. TF instruments were used in rotary motion at 300 rpm with 2 Ncm torque (VDW Silver, Munich, Germany). The following sequence was used: SM1 (20/0.04, full WL) and SM2 (25/0.06, full WL). TF Adaptive Group. TF instruments were used under TF Adaptive motion (Elements Adaptive motor, SybronEndo). The following sequence was used: SM1 (20/0.04, full WL) and SM2 (25/0.06, full WL). ProTaper Universal Group. ProTaper Universal instruments were used at 300 rpm with 2 Ncm torque (VDW Silver). The following sequence was used: SX (1/2 of the WL), S1, S2, F1 (20/0.07), and F2 (25/0.08) files at the full WL. A single operator performed all instrumentation, and only new instruments were used. Apical patency was confirmed between each preparation step by the use of a size 10 K-file just beyond the WL, and canals were irrigated with 1.0 mL sterile water using a 30-G sidevented needle (Max-i-Probe; Dentsply Rinn, Elgin, IL) placed to a depth just short of binding. After final irrigation with 1.0 mL sterile water, postinstrumentation images were performed as described earlier. Image Processing and Analysis All image processing, registration, segmentation, and extraction of attributes were performed within the Fiji (Fiji is Just ImageJ) opensource software interface or 1 of its associated plug-ins (8). The images were first converted into 8-bit gray scale, and after that each pair of image (baseline and after instrumentation) was registered using the ‘‘Rigid Registration’’ plug-in (9) (Fig. 1A and B). The baseline image was used as the template for the rigid transformation. A coregistered image from the pre- and postinstrumentation canals is shown in Figure 1C. After that, each canal (baseline and instrumented) was segmented from the background using an iterative polygon tracing tool. Each line segment was defined by the user following the geometry of the canal and aided by an automatic segmentation algorithm to appropriately define edges. After polygon definition, a simple binarization scheme (0 for background, 255 for the defined polygon) was attributed (Fig. 1D and 1E). A skeletonization algorithm was then applied to the segmented 2
Silva et al.
images. This algorithm uses binary thinning (symmetric erosion) for finding the centerlines (skeleton) of objects in the input image (10). An example of the final centerline of each baseline and instrumented canals is depicted in Figure 1F and G. The XY coordinates of each skeleton were saved in a spreadsheet, and the distance (in pixels) between each XY coordinate found for the baseline and the instrumented skeleton images were calculated using the following formula: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðxb xi Þ2 þ ðyb yi Þ2 where xb and yb are the coordinates for the baseline canal and xi and yi are the coordinates for the instrumented canal. The obtained values were further converted to millimeters by applying the used microscope magnification scale. These values were considered as measurements of canal transportation, which were then averaged for the whole canal or for 2 independent regions (straight and curved levels), as seen in Figure 1H.
Statistical Analysis At the straight canal level, a total number of 23,250 dots referring to canal transportation were registered, whereas 25,200 dots at the curved parts were analyzed. Each dot has been considered as a unit for statistical analysis. Because of the considerable data size, a bellshaped distribution was assumed, and, thus, a univariate 2-way analysis of variance procedure was selected. Instrumentation systems and root canal levels were selected as independent variables and canal transportation (mm) as the dependent variable. Tukey Honestly Significant Difference (HSD) was used for pair-wise comparisons, and the Student t test was used to compare means for straight and curved canal parts. Alpha-type error was selected at 5%.
Results The control group showed no canal transportation, confirming the reliability and consistency of the current method. As seen in Table 1, instrumentation systems significantly influenced canal transportation (P = .000). Compared with the straight part of the root canal, transportation was higher at the curved canal portion (0.036 0.036 and 0.072 0.058, respectively) (P = .000). For both canal levels, the TF system induced the lowest mean of canal transportation followed by TF Adaptive and ProTaper Universal systems. However, a significant interaction between instrumentation system and root canal level was also found. At the straight portion, TF and TF Adaptive systems produced similar canal transportation (P > .05), which was significantly lower than the ProTaper Universal system (P = .000); at the curved part, the TF system resulted in the lowest canal transportation followed by the TF Adaptive and ProTaper Universal systems (P = .000).
Discussion Overall, the TF system used in rotary motion showed lower canal transportation in the curved canal level than the other tested systems. Therefore, the null hypothesis was rejected. Previous studies showed that the TF Adaptive system induces lower canal transportation and better centering ability when compared with reciprocating systems (11, 12). However, this is the first time that the centering ability of the TF was compared using rotary and adaptive movements, allowing the isolation of 1 variable: movement kinematic. Rotary motion showed improved performance compared with this new combined rotary/reciprocating motion in simulated root canals, remarkably in the canal curvature. The ProTaper Universal system showed the highest canal transportation in both straight and curved portions, which is in line with previous JOE — Volume -, Number -, - 2015
Basic Research—Technology
Figure 1. (A) A stereomicroscopic image of the sound simulated canal. (B) A stereomicroscopic image of the instrumented simulated canal. (C) Superimposition of sound and instrumented canals after image registration. (D) The segmented sound canal. (E) The segmented instrumented canal. (F) A skeleton of the sound canal. (G) A skeleton of the instrumented canal. (H) A schematic representation of evaluated regions (straight and curvature) in a sound simulated resin block canal.
studies (13, 14). Some aspects might give some rationale to support these results. First, although the last instrument used to prepare the canals in both TF groups has a .06 taper, ProTaper F2 file presents a .08 taper and, therefore, a considerable larger core mass. In addition, it is established that the ProTaper Universal system has a tendency to straighten curved canals causing transportation toward JOE — Volume -, Number -, - 2015
the furcation at the middle-coronal thirds and toward the outer aspect of the curvature at the apical third (15). To top it off, TF instruments use an R-phase heat alloy treatment technology, which optimizes their strength and flexibility (16). From an experimental point of view, 2 main experimental models have been currently used to rank the shaping ability of different NiTi
Transportation Assessment Using Adaptive Movement System
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Basic Research—Technology TABLE 1. Mean (mm), Standard Deviation (SD), and 95% Confidence Interval (CI) for Canal Transportation between Instrumentation Systems and Canal Portions Straight canal part System Twisted File TF Adaptive ProTaper Universal
Mean (SD) 0.023 (0.018) 0.030 (0.036) 0.054 (0.042)
Curved canal part
95% CI
Mean (SD) a
0.022–0.024 0.029–0.031a 0.054–0.056b
0.054 (0.038) 0.061 (0.043) 0.100 (0.075)
All canal parts
95% CI a
0.053–0.055 0.061–0.062b 0.099–0.102c
Mean (SD)
95% CI
0.039 (0.033) 0.047 (0.043) 0.079 (0.065)
0.039–0.040a 0.046–0.047b 0.077–0.079c
Different lowercase letters indicate a significant difference in columns as depicted from the 95% CI and confirmed by Tukey HSD for both straight and curved canal portions.
systems: 3-dimensional (3D) micro–computed tomographic (microCT) assessment using extracted human teeth and simulated artificial root canals in resin blocks (17). In the current study, simulated root canals in resin blocks were chosen because anatomy standardization is a decisive factor to evaluate the outcome variables in this study (ie, centering ability and canal transportation). Previous studies have already shown the usefulness of simulated artificial root canals to study mechanical preparation (1, 18). Nonetheless, there are clear limitations with this model, such as the microhardness difference between the resin and natural dentin tissue (19). Heat generation is another major disadvantage of simulated artificial root canals because it may soften the resin material, leading to binding of cutting blades and facilitating instrument fracture (17, 19–22). Thus, care must be taken before extrapolating the results of studies using simulated canals to the clinical setting (13, 14). It is important to standardize the experimental design when evaluating the shaping ability of different NiTi systems; several factors, such as file design, instrument alloy, preparation technique, operator, and root canal anatomy are known to significantly impact the overall canal transportation degree (13). Thus, to achieve some standardization for the final shape size among the tested systems, TF instruments (used either under rotary or adaptive motion) were used until the SM2 file (25/0.06), whereas the ProTaper Universal system was used until the F2 file (25/0.08). Even so, scientific logic dictates that a greater taper of the F2 file must have an impact on the current results. In the present study, transportation in the straight parts of the canals was also detected although its numeric values were, in fact, much lower than in curved parts. Overall, transportation in the straight part of canals is not uncommon. Because files were used to the full extent of the simulated canals (including the curved portion), they are prompted to rectify, generating rectification pressure toward the opposite curvature wall and explaining transportation in the straight portion. Moreover, ProTaper Universal values were higher than those found for the TF in rotary and adaptive motions, which could be explained by differences in taper, file flexibility, and the number of files of each system, increasing the odds of transportation, even in straight canals. Moreover, the ProTaper Universal group used 5 files, and the TF and TF Adaptive groups used only 2 files to perform instrumentation. 3D-based techniques such as micro-CT imaging are already available in the field of endodontics to study root canal transportation, and it is very tempting to try to fully represent the real tridimensional transportation of root canals; however, a fully 3D analysis is not so straightforward in this regard. Initial problems are related to the ideal definition of 3D measurements of root canal transportation; although the center of gravity measurement (23, 24) is certainly a very sound approach, it can only be applied to oval-shaped and individual canals. For this reason, the great majority of studies use micro-CT imaging with bidimensional analysis by providing information on only selected slices (5, 25–27). The current study used a relatively common bidimensional approach to study transportation in simulated root canals by comparing images before and after instrumentation with different systems 4
Silva et al.
(17, 19, 28). Regarding the effectiveness of the repositioning method, although a custom-made silicon device was used to guarantee that the blocks were placed in the same position between the 2 stereomicroscopic exposures, the exact position was only obtained after the automatic image processing coregistration procedure (rigid registration), considerably reducing bias related to a visually driven and operator-based image superimposition scheme. After segmentation of sound and instrumented canal areas, a skeletonization algorithm was used to find the thin version of that shape that is equidistant to its boundaries. Final calculation of transportation was based on the distance between equivalent coordinates in both images. In this way, this methodologic approach brings some improvements because it is almost not operator dependent, providing results from the whole canal length and not only from selected slices.
Conclusion Under the conditions of this study, it can be concluded that the TF system used in rotary motion produced overall less canal transportation than the TF system used in adaptive motion and the ProTaper Universal system.
Acknowledgments The authors deny any conflicts of interest related to this study.
References 1. Weine FS, Kelly RF, Lio PJ. The effect of preparation procedures on original canal shape and on apical foramen shape. J Endod 1975;1:255–62. 2. Bergmans L, Van Cleynenbreugel J, Wevers M, Lambrechts P. Mechanical root canal preparation with NiTi rotary instruments: rationale performance and safety—status report for the American Journal of Dentistry. Am J Dent 2001;14:324–33. 3. Gergi R, Rjeily JA, Sader J, Naaman A. Comparison of canal transportation and centering ability of Twisted Files, Pathfile-ProTaper system, and stainless steel hand K-files by using computed tomography. J Endod 2010;36:904–7. 4. Gambarini G, Gergi R, Naaman A, et al. Cyclic fatigue of twisted file rotary NiTi instruments used in reciprocating motion. Int Endod J 2012;45:802–6. 5. Gergi R, Osta N, Bourbouze G, et al. Effects of three nickel titanium instrument systems on root canal geometry assessed by micro-computed tomography. Int Endod J 2015;48:162–70. 6. Ajuz NC, Armada L, Gonc¸alves LS, et al. Glide path preparation in S-shaped canals with rotary pathfinding nickel-titanium instruments. J Endod 2013;39:534–7. 7. Giuliani V, Di Nasso L, Pace R, Pagavino G. Shaping ability of WaveOne Primary reciprocating files and ProTaper system used in continuous and reciprocating motion. J Endod 2014;40:1468–71. 8. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9:671–5. 9. Schindelin J, Longair M. Rigid 3D alignment of stacks. Available at: http://132.187. 25.13/home/?category=Download&page=RigidRegistration. Accessed August 8, 2014. 10. Lee TC, Kashyap RL, Chu CN. Building skeleton models via 3-D medial surface/axis thinning algorithms. Comput Vis Graph Image Process 1994;56:462–78. 11. Gergi R, Arbab-Chirani R, Osta N, Naaman A. Micro-computed tomographic evaluation of canal transportation instrumented by different kinematics rotary nickeltitanium instruments. J Endod 2014;40:1223–7. 12. Ordinola-Zapata R, Bramante CM, Duarte MA, et al. Shaping ability of Reciproc and TF Adaptive systems in severely curved canals of rapid microCT-based prototyping molar replicas. J Appl Oral Sci 2014;22:509–15.
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Basic Research—Technology 13. Bonaccorso A, Cantatore G, Condorelli GG, et al. Shaping ability of four nickeltitanium rotary instruments in S-shaped canals. J Endod 2009;35:883–6. 14. Burroughs JR, Bergeron BE, Roberts MD, et al. Shaping ability of three nickeltitanium file system in simulated S-shaped root canals. J Endod 2012;38:1618–21. 15. B€urklein S, Sch€afer E. Critical evaluation of root canal transportation by instrumentation. Endod Topics 2013;29:110–24. 16. Maitin N, Arunagiri D, Brave D, et al. An ex vivo comparative analysis on shaping ability of four NiTi rotary endodontic instruments using spiral computed tomography. J Conserv Dent 2013;16:219–23. 17. Yoo YS, Cho YB. A comparison of the shaping ability of reciprocating NiTi instruments in simulated curved canals. Restor Dent Endod 2012;37:220–7. 18. Dummer PM, Alodeh MH, al-Omari MA. A method for the construction of simulated root canals in clear resin blocks. Int Endod J 1991;24:63–6. 19. Lim YG, Park SJ, Kim HC, Min KS. Comparison of the centering ability of WaveOne and Reciproc nickel-titanium instruments in simulated curved canals. Restor Dent Endod 2013;38:21–5. 20. Kum KY, Sp€angberg L, Cha BY, et al. Shaping ability of three ProFile rotary instrumentation techniques in simulated resin root canals. J Endod 2000;26:719–23. 21. Thomson SA, Dummer PM. Shaping ability of ProFile.04 Taper Series 29 rotary nickel-titanium instruments in simulated root canals. Part 1. Int Endod J 1997; 30:1–7.
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22. Baumann MA, Roth A. Effect of experience on quality of canal preparation with rotary nickel-titanium files. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1999;88:714–8. 23. Peters OA, Sch€onenberger K, Laib A. Effects of four Ni-Ti preparation techniques on root canal geometry assessed by micro computed tomography. Int Endod J 2001;34: 221–30. 24. Bergmans L, Van Cleynenbreugel J, Wevers M, Lambrechts P. A methodology for quantitative evaluation of root canal instrumentation using microcomputed tomography. Int Endod J 2001;34:390–8. 25. Junaid A, Freire LG, Bueno CES, et al. Influence of single-file endodontics on apical transportation in curved root canals: an ex vivo micro-computed tomographic study. J Endod 2014;40:717–20. 26. Freire LG, Gavini G, Cunha RS, Santos MD. Assessing apical transportation in curved canals: comparison between cross-sections and micro-computed tomography. Braz Oral Res 2012;26:222–7. 27. Hartmann MS, Barletta FB, Fontanella VR, Vanni JR. Canal transportation after root canal instrumentation: a comparative study with computed tomography. J Endod 2007;33:962–5. 28. B€urklein S, Poschmann T, Sch€afer E. Shaping ability of different nickel-titanium systems in simulated s-shaped canals with and without glide path. J Endod 2014;40: 1231–4.
Transportation Assessment Using Adaptive Movement System
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Quantitative Transportation Assessment in Simulated Curved Canals Prepared with an Adaptive Movement System Emmanuel Jo~ ao Nogueira Leal Silva, DDS, MSc, PhD,* Michele Dias Nunes Tameir~ ao, DDS, MSc,* Felipe Gonc¸alves Belladonna, DDS, MSc,† Aline Almeida Neves, DDS, MSc, PhD,‡ Erick Miranda Souza, DDS, MSc, PhD,§ and Gustavo De-Deus, DDS, MSc, PhD* Abstract Introduction: The aim of this study was to evaluate the ability of the Twisted File Adaptive (TF Adaptive; SybronEndo, Orange, CA) system in maintaining the original profile of root canal anatomy. The ProTaper Universal (Dentsply Maillefer, Ballaigues, Switzerland) and Twisted File (TF) (SybronEndo) systems were used as reference techniques for comparison. Methods: Thirty simulated curved root canals manufactured in clear resin blocks were randomly assigned to 3 groups (n = 10) according to the instrumentation system: TF in rotary motion, TF in TF Adaptive motion, and ProTaper Universal. Color stereomicroscopic images from each block were taken exactly at the same position before and after instrumentation. All image processing and data analysis were performed with an open-source program (Fiji). Evaluation of canal transportation was obtained for 2 independent canal regions: straight and curved levels. Univariate analysis of variance and Tukey Honestly Significant Difference were used, and a cutoff for significance was set at alpha = 5%. Results: Instrumentation systems significantly influenced canal transportation (P = .000). A significant interaction between instrumentation system and root canal level (P = .000) was also found as follows: at the straight part, TF and TF Adaptive systems produced similar canal transportation, which was significantly lower than for the ProTaper Universal system; at the curved part, TF resulted in the lowest canal transportation followed by TF Adaptive and ProTaper Universal systems. Canal transportation was higher at the curved canal parts (P = .00). Conclusions: The TF in rotary motion produced overall less canal transportation in the curved portion when compared
From the *Department of Endodontics, School of Dentistry, Grande Rio University (UNIGRANRIO), Rio de Janeiro, Rio de Janeiro; †Department of Endodontics, Fluminense Federal University (UFF), Niteroi, Rio de Janeiro; ‡Department of Pediatric Dentistry and Orthodontics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Rio de Janeiro; and §Department of Dentistry II, Federal University of Maranh~ao, S~ao Luis, Maranh~ao, Brazil. Address requests for reprints to Dr Emmanuel Jo~ao Nogueira Leal da Silva, Rua Herotides de Oliveira, 61/902, Icaraı, Niteroi, RJ, Brazil. E-mail address: nogueiraemmanuel@ hotmail.com 0099-2399/$ - see front matter Copyright ª 2015 American Association of Endodontists. http://dx.doi.org/10.1016/j.joen.2015.02.028
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with the others tested systems. The ProTaper Universal system showed the highest canal transportation. (J Endod 2015;-:1–5)
Key Words Canal transportation, root canal instrumentation, R-phase, Twisted File, Twisted File Adaptive
C
anal shaping remains one of the challenging aspects of endodontic treatment because mishaps such as ledges, zips, perforations, and canal transportation can occur, particularly when preparing curved canals (1). The use of nickel-titanium (NiTi) instruments has enhanced the overall shaping quality and reduced the frequency of procedural errors (2, 3). In short, NiTi files have raised new perspectives for mechanical canal preparation, such as less debris extrusion, better centering ability, and reduced learning curve. Recently, the Twisted File Adaptive system (TF Adaptive; SybronEndo, Orange, CA) has been introduced onto the market. In theory, this system claims to maximize the advantages of reciprocation movement while minimizing its disadvantages. The TF Adaptive system uses a patented unique motion technology, which automatically adapts the movement according to the instrumentation stress input to the file. According to the manufacturer, when the TF Adaptive instrument is lightly stressed in the canal, the motor performs conventional clockwise movement, allowing better cutting efficiency and removal of debris. In contrast, during increased torsional stress, the movement automatically changes into a reciprocation mode. Moreover, TF Adaptive files have 3 unique design features: R-phase heat treatment, twisting of the metal, and special surface conditioning (4). Although this dynamic movement and file design look promising, little knowledge exists regarding the TF Adaptive shaping ability (5). To evaluate the shaping ability of different NiTi systems, the use of simulated curved canals in resin blocks have been largely used (6, 7). Although widespread, different methodological approaches have been proposed for the measurement of canal transportation in the simulated canal blocks model, resulting in the fact that, to the present date, there is no established golden standard method to evaluate morphologic changes in simulated canals. However, a common aspect between methodologies is the evaluation approach based on the establishment of evaluation points, usually randomly preselected by the operator. The direct and deliberated influence of the operator on the selection of points might be a source of bias. Thus, an automatic measurement approach able to reliably assess the entire extension of the simulated canal without direct operator interference would be remarkably welcome and appealing. The present study was designed to assess the ability of the TF Adaptive system in maintaining the original profile of canal anatomy in simulated curved canals of clear resin blocks. An innovative approach to evaluate the canal transportation is introduced and discussed using a skeletonization algorithm to calculate the canal transportation by automatic coregistration of the pre- and postinstrumentation images. ProTaper Universal (Dentsply Maillefer, Ballaigues, Switzerland) and Twisted File (TF) (SybronEndo) systems were used as reference techniques for comparison. The null hypothesis tested was that there are no significant differences in canal transportation among the tested NiTi systems.
Transportation Assessment Using Adaptive Movement System
1
Basic Research—Technology Materials and Methods Digital Image Acquisition Thirty simulated curved root canals in clear resin blocks with a 2% taper, 10-mm radius of curvature, 70 angle of curvature, and 17-mm length (Endo Training Blocks ISO 15, Dentsply Maillefer) were randomly assigned into 3 groups according to the instrumentation system (n = 10): TF group, TF Adaptive group, and ProTaper Universal group. Before any instrumentation procedure, a round silicon base with a rectangular slot fitting the microscope base was positioned under a color stereomicroscope (1005t Opticam stereomicroscope; Opticam, S~ao Paulo, Brazil) connected to a digital camera (CMOS 10 megapixels, Opticam). The rectangular slot matched the exact dimensions of the simulated canal blocks. Each specimen was then inserted into the slot, and images were taken and stored in TIFF format. After the instrumentation procedures, all blocks were imaged again following the same protocol. Ten blocks were used as a control group in which no instrumentation was performed at all. In this group, 1 color stereoscopic image of the block was taken first, removed, and replaced back to take another image of the same noninstrumented canal. Instrumentation Initially, for all groups, stainless steel size 10- and 15 K-file instruments (Dentsply Maillefer) were used to scout the canal up to the working length (WL), creating an initial and standardized glide path. TF Group. TF instruments were used in rotary motion at 300 rpm with 2 Ncm torque (VDW Silver, Munich, Germany). The following sequence was used: SM1 (20/0.04, full WL) and SM2 (25/0.06, full WL). TF Adaptive Group. TF instruments were used under TF Adaptive motion (Elements Adaptive motor, SybronEndo). The following sequence was used: SM1 (20/0.04, full WL) and SM2 (25/0.06, full WL). ProTaper Universal Group. ProTaper Universal instruments were used at 300 rpm with 2 Ncm torque (VDW Silver). The following sequence was used: SX (1/2 of the WL), S1, S2, F1 (20/0.07), and F2 (25/0.08) files at the full WL. A single operator performed all instrumentation, and only new instruments were used. Apical patency was confirmed between each preparation step by the use of a size 10 K-file just beyond the WL, and canals were irrigated with 1.0 mL sterile water using a 30-G sidevented needle (Max-i-Probe; Dentsply Rinn, Elgin, IL) placed to a depth just short of binding. After final irrigation with 1.0 mL sterile water, postinstrumentation images were performed as described earlier. Image Processing and Analysis All image processing, registration, segmentation, and extraction of attributes were performed within the Fiji (Fiji is Just ImageJ) opensource software interface or 1 of its associated plug-ins (8). The images were first converted into 8-bit gray scale, and after that each pair of image (baseline and after instrumentation) was registered using the ‘‘Rigid Registration’’ plug-in (9) (Fig. 1A and B). The baseline image was used as the template for the rigid transformation. A coregistered image from the pre- and postinstrumentation canals is shown in Figure 1C. After that, each canal (baseline and instrumented) was segmented from the background using an iterative polygon tracing tool. Each line segment was defined by the user following the geometry of the canal and aided by an automatic segmentation algorithm to appropriately define edges. After polygon definition, a simple binarization scheme (0 for background, 255 for the defined polygon) was attributed (Fig. 1D and 1E). A skeletonization algorithm was then applied to the segmented 2
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images. This algorithm uses binary thinning (symmetric erosion) for finding the centerlines (skeleton) of objects in the input image (10). An example of the final centerline of each baseline and instrumented canals is depicted in Figure 1F and G. The XY coordinates of each skeleton were saved in a spreadsheet, and the distance (in pixels) between each XY coordinate found for the baseline and the instrumented skeleton images were calculated using the following formula: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðxb xi Þ2 þ ðyb yi Þ2 where xb and yb are the coordinates for the baseline canal and xi and yi are the coordinates for the instrumented canal. The obtained values were further converted to millimeters by applying the used microscope magnification scale. These values were considered as measurements of canal transportation, which were then averaged for the whole canal or for 2 independent regions (straight and curved levels), as seen in Figure 1H.
Statistical Analysis At the straight canal level, a total number of 23,250 dots referring to canal transportation were registered, whereas 25,200 dots at the curved parts were analyzed. Each dot has been considered as a unit for statistical analysis. Because of the considerable data size, a bellshaped distribution was assumed, and, thus, a univariate 2-way analysis of variance procedure was selected. Instrumentation systems and root canal levels were selected as independent variables and canal transportation (mm) as the dependent variable. Tukey Honestly Significant Difference (HSD) was used for pair-wise comparisons, and the Student t test was used to compare means for straight and curved canal parts. Alpha-type error was selected at 5%.
Results The control group showed no canal transportation, confirming the reliability and consistency of the current method. As seen in Table 1, instrumentation systems significantly influenced canal transportation (P = .000). Compared with the straight part of the root canal, transportation was higher at the curved canal portion (0.036 0.036 and 0.072 0.058, respectively) (P = .000). For both canal levels, the TF system induced the lowest mean of canal transportation followed by TF Adaptive and ProTaper Universal systems. However, a significant interaction between instrumentation system and root canal level was also found. At the straight portion, TF and TF Adaptive systems produced similar canal transportation (P > .05), which was significantly lower than the ProTaper Universal system (P = .000); at the curved part, the TF system resulted in the lowest canal transportation followed by the TF Adaptive and ProTaper Universal systems (P = .000).
Discussion Overall, the TF system used in rotary motion showed lower canal transportation in the curved canal level than the other tested systems. Therefore, the null hypothesis was rejected. Previous studies showed that the TF Adaptive system induces lower canal transportation and better centering ability when compared with reciprocating systems (11, 12). However, this is the first time that the centering ability of the TF was compared using rotary and adaptive movements, allowing the isolation of 1 variable: movement kinematic. Rotary motion showed improved performance compared with this new combined rotary/reciprocating motion in simulated root canals, remarkably in the canal curvature. The ProTaper Universal system showed the highest canal transportation in both straight and curved portions, which is in line with previous JOE — Volume -, Number -, - 2015
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Figure 1. (A) A stereomicroscopic image of the sound simulated canal. (B) A stereomicroscopic image of the instrumented simulated canal. (C) Superimposition of sound and instrumented canals after image registration. (D) The segmented sound canal. (E) The segmented instrumented canal. (F) A skeleton of the sound canal. (G) A skeleton of the instrumented canal. (H) A schematic representation of evaluated regions (straight and curvature) in a sound simulated resin block canal.
studies (13, 14). Some aspects might give some rationale to support these results. First, although the last instrument used to prepare the canals in both TF groups has a .06 taper, ProTaper F2 file presents a .08 taper and, therefore, a considerable larger core mass. In addition, it is established that the ProTaper Universal system has a tendency to straighten curved canals causing transportation toward JOE — Volume -, Number -, - 2015
the furcation at the middle-coronal thirds and toward the outer aspect of the curvature at the apical third (15). To top it off, TF instruments use an R-phase heat alloy treatment technology, which optimizes their strength and flexibility (16). From an experimental point of view, 2 main experimental models have been currently used to rank the shaping ability of different NiTi
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Basic Research—Technology TABLE 1. Mean (mm), Standard Deviation (SD), and 95% Confidence Interval (CI) for Canal Transportation between Instrumentation Systems and Canal Portions Straight canal part System Twisted File TF Adaptive ProTaper Universal
Mean (SD) 0.023 (0.018) 0.030 (0.036) 0.054 (0.042)
Curved canal part
95% CI
Mean (SD) a
0.022–0.024 0.029–0.031a 0.054–0.056b
0.054 (0.038) 0.061 (0.043) 0.100 (0.075)
All canal parts
95% CI a
0.053–0.055 0.061–0.062b 0.099–0.102c
Mean (SD)
95% CI
0.039 (0.033) 0.047 (0.043) 0.079 (0.065)
0.039–0.040a 0.046–0.047b 0.077–0.079c
Different lowercase letters indicate a significant difference in columns as depicted from the 95% CI and confirmed by Tukey HSD for both straight and curved canal portions.
systems: 3-dimensional (3D) micro–computed tomographic (microCT) assessment using extracted human teeth and simulated artificial root canals in resin blocks (17). In the current study, simulated root canals in resin blocks were chosen because anatomy standardization is a decisive factor to evaluate the outcome variables in this study (ie, centering ability and canal transportation). Previous studies have already shown the usefulness of simulated artificial root canals to study mechanical preparation (1, 18). Nonetheless, there are clear limitations with this model, such as the microhardness difference between the resin and natural dentin tissue (19). Heat generation is another major disadvantage of simulated artificial root canals because it may soften the resin material, leading to binding of cutting blades and facilitating instrument fracture (17, 19–22). Thus, care must be taken before extrapolating the results of studies using simulated canals to the clinical setting (13, 14). It is important to standardize the experimental design when evaluating the shaping ability of different NiTi systems; several factors, such as file design, instrument alloy, preparation technique, operator, and root canal anatomy are known to significantly impact the overall canal transportation degree (13). Thus, to achieve some standardization for the final shape size among the tested systems, TF instruments (used either under rotary or adaptive motion) were used until the SM2 file (25/0.06), whereas the ProTaper Universal system was used until the F2 file (25/0.08). Even so, scientific logic dictates that a greater taper of the F2 file must have an impact on the current results. In the present study, transportation in the straight parts of the canals was also detected although its numeric values were, in fact, much lower than in curved parts. Overall, transportation in the straight part of canals is not uncommon. Because files were used to the full extent of the simulated canals (including the curved portion), they are prompted to rectify, generating rectification pressure toward the opposite curvature wall and explaining transportation in the straight portion. Moreover, ProTaper Universal values were higher than those found for the TF in rotary and adaptive motions, which could be explained by differences in taper, file flexibility, and the number of files of each system, increasing the odds of transportation, even in straight canals. Moreover, the ProTaper Universal group used 5 files, and the TF and TF Adaptive groups used only 2 files to perform instrumentation. 3D-based techniques such as micro-CT imaging are already available in the field of endodontics to study root canal transportation, and it is very tempting to try to fully represent the real tridimensional transportation of root canals; however, a fully 3D analysis is not so straightforward in this regard. Initial problems are related to the ideal definition of 3D measurements of root canal transportation; although the center of gravity measurement (23, 24) is certainly a very sound approach, it can only be applied to oval-shaped and individual canals. For this reason, the great majority of studies use micro-CT imaging with bidimensional analysis by providing information on only selected slices (5, 25–27). The current study used a relatively common bidimensional approach to study transportation in simulated root canals by comparing images before and after instrumentation with different systems 4
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(17, 19, 28). Regarding the effectiveness of the repositioning method, although a custom-made silicon device was used to guarantee that the blocks were placed in the same position between the 2 stereomicroscopic exposures, the exact position was only obtained after the automatic image processing coregistration procedure (rigid registration), considerably reducing bias related to a visually driven and operator-based image superimposition scheme. After segmentation of sound and instrumented canal areas, a skeletonization algorithm was used to find the thin version of that shape that is equidistant to its boundaries. Final calculation of transportation was based on the distance between equivalent coordinates in both images. In this way, this methodologic approach brings some improvements because it is almost not operator dependent, providing results from the whole canal length and not only from selected slices.
Conclusion Under the conditions of this study, it can be concluded that the TF system used in rotary motion produced overall less canal transportation than the TF system used in adaptive motion and the ProTaper Universal system.
Acknowledgments The authors deny any conflicts of interest related to this study.
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