Computer-aided design and fabrication of nasal prostheses: a semi-automated algorithm using statistical shape modeling.

PURPOSE: This research aimed to develop an innovative method for designing and fabricating nasal prostheses that reduces anaplastologist expertise dependency while maintaining quality and appearance, allowing patients to regain their normal facial appearance. METHODS: The method involved statistical shape modeling using a morphable face model and 3D data acquired through optical scanning or CT. An automated design process generated patient-specific fits and appearances using regular prosthesis materials and 3D printing of molds. Manual input was required for specific case-related details. RESULTS: The developed method met all predefined requirements, replacing analog impression-making and offering compatibility with various data acquisition methods. Prostheses created through this method exhibited equivalent aesthetics to conventionally fabricated ones while reducing the skill dependency typically associated with prosthetic design and fabrication. CONCLUSIONS: This method provides a promising approach for both temporary and definitive nasal prostheses, with the potential for remote prosthesis fabrication in areas lacking anaplastology care. While new skills are required for data acquisition and algorithm control, these technologies are increasingly accessible. Further clinical studies will help validate its effectiveness, and ongoing technological advancements may lead to even more advanced and skill-independent prosthesis fabrication methods in the future.

Clinical validation of the accuracy of an intra-operative assessment tool using 3D ultrasound compared to histopathology in patients with squamous cell carcinoma of the tongue.

BACKGROUND: Histopathological analysis often shows close resection margins after surgical removal of tongue squamous cell carcinoma (TSCC). This study aimed to investigate the agreement between intraoperative 3D ultrasound (US) margin assessment and postoperative histopathology of resected TSCC. METHODS: In this study, ten patients were prospectively included. Three fiducial cannulas were inserted into the specimen. To acquire a motorized 3D US volume, the resected specimen was submerged in saline, after which images were acquired while the probe moved over the specimen. The US volumes were annotated twice: (1) automatically and (2) manually, with the automatic segmentation as initialization. After standardized histopathological processing, all hematoxylin-eosin whole slide images (WSI) were included for analysis. Corresponding US images were found based on the known WSI spacing and fiducials. Blinded observers measured the tumor thickness and the margin in the caudal, deep, and cranial directions on every slide. The anterior and posterior margin was measured per specimen. RESULTS: The mean difference in all measurements between manually segmented US and histopathology was 2.34 (SD: ±3.34) mm, and Spearman's rank correlation coefficient was 0.733 (p < 0.001). The smallest mean difference was in the tumor thickness with 0.80 (SD: ±2.44) mm and a correlation of 0.836 (p < 0.001). Limitations were observed in the caudal region, where no correlation was found. CONCLUSION: This study shows that 3D US and histopathology have a moderate to strong statistically significant correlation (r = 0.733; p < 0.001) and a mean difference between the modalities of 2.3 mm (95%CI: -4.2; 8.9). Future research should focus on patient outcomes regarding resection margins.

Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-dimensional ultrasound volumes.

Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes. Additionally, it investigates the clinical effect of automatic segmentation. A 3D No New U-Net (nnUNet) was trained on 113 manually annotated ultrasound volumes of resected tongue carcinoma. The model was implemented on a mobile workstation and clinically validated on 16 prospectively included tongue carcinoma patients. Different prediction settings were investigated. Automatic segmentations with multiple islands were adjusted by selecting the best-representing island. The final margin status (FMS) based on automatic, semi-automatic, and manual segmentation was computed and compared with the histopathological margin. The standard 3D nnUNet resulted in the best-performing automatic segmentation with a mean (SD) Dice volumetric score of 0.65 (0.30), Dice surface score of 0.73 (0.26), average surface distance of 0.44 (0.61) mm, Hausdorff distance of 6.65 (8.84) mm, and prediction time of 8 seconds. FMS based on automatic segmentation had a low correlation with histopathology (r = 0.12, p = 0.67); MS resulted in a moderate but insignificant correlation with histopathology (r = 0.4, p = 0.12, n = 16). Implementing the 3D nnUNet yielded fast, automatic segmentation of tongue carcinoma in 3D ultrasound volumes. Correlation between FMS and histopathology obtained from these segmentations was lower than the moderate correlation between MS and histopathology.

Comparison of image quality of 3D ultrasound: motorized acquisition versus freehand navigated acquisition, a phantom study.

PURPOSE: Intra-operative assessment of resection margins during oncological surgery is a field that needs improvement. Ultrasound (US) shows the potential to fulfill this need, but this imaging technique is highly operator-dependent. A 3D US image of the whole specimen may remedy the operator dependence. This study aims to compare and evaluate the image quality of 3D US between freehand acquisition (FA) and motorized acquisition (MA). METHODS: Multiple 3D US volumes of a commercial phantom were acquired in motorized and freehand fashion. FA images were collected with electromagnetic navigation. An integrated algorithm reconstructed the FA images. MA images were stacked into a 3D volume. The image quality is evaluated following the metrics: contrast resolution, axial and elevation resolution, axial and elevation distance calibration, stability, inter-operator variability, and intra-operator variability. A linear mixed model determined statistical differences between FA and MA for these metrics. RESULTS: The MA results in a statistically significant lower error of axial distance calibration (p < 0.0001) and higher stability (p < 0.0001) than FA. On the other hand, the FA has a better elevation resolution (p < 0.003) than the MA. CONCLUSION: MA results in better image quality of 3D US than the FA method based on axial distance calibration, stability, and variability. This study suggests acquiring 3D US volumes for intra-operative ex vivo margin assessment in a motorized fashion.

A hybrid registration method using the mandibular bone surface for electromagnetic navigation in mandibular surgery.

PURPOSE: To utilize navigated mandibular (reconstructive) surgery, accurate registration of the preoperative CT scan with the actual patient in the operating room (OR) is required. In this phantom study, the feasibility of a noninvasive hybrid registration method is assessed. This method consists of a point registration with anatomic landmarks for initialization and a surface registration using the bare mandibular bone surface for optimization. METHODS: Three mandible phantoms with reference notches on two osteotomy planes were 3D printed. An electromagnetic tracking system in combination with 3D Slicer software was used for navigation. Different configurations, i.e., different surface point areas and number and configuration of surface points, were tested with a dentate phantom (A) in a metal-free environment. To simulate the intraoperative environment and different anatomies, the registration procedure was also performed with an OR bed using the dentate phantom and two (partially) edentulous phantoms with atypical anatomy (B and C). The accuracy of the registration was calculated using the notches on the osteotomy planes and was expressed as the target registration error (TRE). TRE values of less than 2.0 mm were considered as clinically acceptable. RESULTS: In all experiments, the mean TRE was less than 2.0 mm. No differences were found using different surface point areas or number or configurations of surface points. Registration accuracy in the simulated intraoperative setting was-mean (SD)-0.96 (0.22), 0.93 (0.26), and 1.50 (0.28) mm for phantom A, phantom B, and phantom C. CONCLUSION: Hybrid registration is a noninvasive method that requires only a small area of the bare mandibular bone surface to obtain high accuracy in phantom setting. Future studies should test this method in clinical setting during actual surgery.

Registration methods for surgical navigation of the mandible: a systematic review.

Image-to-patient registration in navigated mandibular surgery is complex due to the mobile nature of the mandible compared with other craniofacial bones. As a result, surgical navigation is rarely employed in the mandibular region. This systematic review provides an overview of the different registration methods that are used for surgical navigation of the mandible. A systematic search was performed in the MEDLINE Ovid, Scopus, and Embase databases on March 25, 2021. Search terms included synonyms for mandibular surgery, surgical navigation, and registration methods. Articles about navigated mandibular surgery, where the registration method was explicitly mentioned, were included. The database search yielded a total of 2952 articles, from which 81 articles remained for analysis. Four main registration methods were identified: point registration, surface registration, hybrid registration, and computer vision-based registration. The mobility of the mandible is accounted for by either keeping the mandible in a fixed position during preoperative imaging and surgery, or by tracking the mandibular movements. Although different registration methods are available for navigated mandibular surgery, there is always a trade-off between accuracy, registration time, usability, and invasiveness. Future studies should focus on testing the different methods in larger patient studies and should report the registration accuracy.

Electromagnetic surgical navigation in patients undergoing mandibular surgery.

The purpose of this study was to evaluate the feasibility of electromagnetic (EM) navigation for guidance on osteotomies in patients undergoing oncologic mandibular surgery. Preoperatively, a 3D rendered model of the mandible was constructed from diagnostic computed tomography (CT) images. Cutting guides and patient specific reconstruction plates were designed and printed for intraoperative use. Intraoperative patient registration was performed using a cone beam CT scan (CBCT). The location of the mandible was tracked with an EM sensor fixated to the mandible. The real-time location of both the mandible and a pointer were displayed on the navigation system. Accuracy measurements were performed by pinpointing four anatomical landmarks and four landmarks on the cutting guide using the pointer on the patient and comparing these locations to the corresponding locations on the CBCT. Differences between actual and virtual locations were expressed as target registration error (TRE). The procedure was performed in eleven patients. TREs were 3.2 ± 1.1 mm and 2.6 ± 1.5 mm using anatomical landmarks and landmarks on the cutting guide, respectively. The navigation procedure added on average half an hour to the duration of the surgery. This is the first study that reports on the accuracy of EM navigation in patients undergoing mandibular surgery.

Evaluating the accuracy of resection planes in mandibular surgery using a preoperative, intraoperative, and postoperative approach.

In mandibular surgery, three-dimensionally printed patient-specific cutting guides are used to translate the preoperative virtually planned resection planes to the operating room. This study was performed to determine whether cutting guides are positioned according to the virtual plan and to compare the intraoperative position of the cutting guide with the resection performed. Nine patients were included. The exact positions of the resection planes were planned virtually and a patient-specific cutting guide was designed and printed. After surgical placement of the cutting guide, intraoperative cone beam computed tomography (CBCT) was performed. Postoperative CT was used to obtain the final resection planes. Distances and yaw and pitch angles between the preoperative, intraoperative, and postoperative resection planes were calculated. Cutting guides were positioned on the mandible with millimetre accuracy. Anterior osteotomies were performed more accurately than posterior osteotomies (intraoperatively positioned and final resection planes differed by 1.2±1.0mm, 4.9±6.6°, and 1.8±1.5°, respectively, and by 2.2±0.9mm, 9.3±9°, and 8.3±6.5° respectively). Differences between intraoperatively planned and final resection planes imply a directional freedom of the saw through the saw slots. Since cutting guides are positioned with millimetre accuracy compared to the virtual plan, the design of the saw slots in the cutting guides needs improvement to allow more accurate resections.

Utilization of a 3D printed dental splint for registration during electromagnetically navigated mandibular surgery.

PURPOSE: A dental splint was developed for non-invasive rigid point-based registration in electromagnetically (EM) navigated mandibular surgery. Navigational accuracies of the dental splint were compared with the common approach, that is, using screws as landmarks. METHODS: A dental splint that includes reference registration notches was 3D printed. Different sets of three points were used for rigid point-based registration on a mandibular phantom: notches on the dental splint only, screws on the mandible, contralateral screws (the side of the mandible where the sensor is not fixated) and a combination of screws on the mandible and notches on the dental splint. The accuracy of each registration method was calculated using 45 notches at one side of the mandible and expressed as the target registration error (TRE). RESULTS: Average TREs of 0.83 mm (range 0.7-1.39 mm), 1.28 mm (1.03-1.7 mm), 2.62 mm (1.91-4.0 mm), and 1.34 mm (1.30-1.39 mm) were found, respectively, for point-based registration based on the splint only, screws on the mandible, screws on the contralateral side only, and screws combined with the splint. CONCLUSION: For dentate patients, rigid point-based registration performs best utilizing a dental splint with notches. The dental splint is easy to implement in the surgical, and navigational, workflow, and the notches can be pinpointed and designated on the CT scan with high accuracy. For edentate patients, screws can be used for rigid point-based registration. However, a new design of the screws is recommended to improve the accuracy of designation on the CT scan.

Quantification of tongue mobility impairment using optical tracking in patients after receiving primary surgery or chemoradiation.

PURPOSE: Tongue mobility has shown to be a clinically interesting parameter on functional results after tongue cancer treatment which can be objectified by measuring the Range Of Motion (ROM). Reliable measurements of ROM would enable us to quantify the severity of functional impairments and use these for shared decision making in treatment choices, rehabilitation of speech and swallowing disturbances after treatment. METHOD: Nineteen healthy participants, eighteen post-chemotherapy patients and seventeen post-surgery patients were asked to perform standardized tongue maneuvers in front of a 3D camera system, which were subsequently tracked and corrected for head and jaw motion. Indicators, such as the left-right tongue range and the deflection angle with the horizontal axis were extracted from the tongue trajectory to serve as a quantitative measure for the impaired tongue mobility. RESULTS: The range and deflection angle showed an excellent intra- and interrater reliability (ICC 0.9) The repeatability experiment showed an average standard deviation of 2.5 mm to 3.5 mm for every movement, except the upward movement. The post-surgery patient group showed a smaller tongue range and higher deflection angle overall than the healthy participants. Post-chemoradiation patients showed less difference in tongue ROM compared with healthy participants. Only a few patients showed asymmetrical movement after treatment, which could not always be explained by T-stage or the side of treatment alone. CONCLUSION: We introduced a reliable and reproducible method for measuring the ROM and to quantify for motion impairments, that was able to show differences in tongue ROM between healthy subjects and patients after chemoradiation or surgery. Future research should focus on measuring patients with oral cancer pre- and post-treatment in combination with the collection of detailed information about the individual tongue anatomy, so that the full ROM trajectory can be used to identify changes over time and to quantify functional impairment.

An interactive surgical simulation tool to assess the consequences of a partial glossectomy on a biomechanical model of the tongue.

Oral cancer surgery has a negative influence on the quality of life (QOL). As a result of the complex physiology involved in oral functions, estimation of surgical effects on functionality remains difficult. We present a user-friendly biomechanical simulation of tongue surgery, including closure with suturing and scar formation, followed by an automated adaptation of a finite element (FE) model to the shape of the tongue. Different configurations of our FE model were evaluated and compared to a well-established FE model. We showed that the post-operative impairment as predicted by our model was qualitatively comparable to a patient case for five different tongue maneuvers.

Towards virtual surgery in oral cancer to predict postoperative oral functions preoperatively.

Our aim was to develop a dynamic virtual model of the oral cavity and oropharynx so that we could incorporate patient-specific factors into the prediction of functional loss after advanced resections for oral cancer. After a virtual resection, functional consequences can be assessed, and a more substantiated decision about treatment can be made. In this study we used a finite element model of the tongue, which can be implemented in the total virtual environment in the future. We analysed the movements and changes in volume, and the effects of changes in the material variables, to mimic scar tissue. The observed movements were in accordance with descriptions of in vivo movements. Affected movements caused by the mimicked scar tissue were also similar to expectations. Some changes in volume were measured, particularly in individual elements. We have taken the first steps in the development of a finite element model of the tongue. Now, refinement is necessary to make the model suitable for future use in virtual surgery.