New method for analysing spatial relationships of facial muscles on MRI: a pilot study

Dysfunction of the facial musculature can have significant physical, social, and psychological consequences. In surgeries such as cleft surgery or craniofacial bimaxillary osteotomies, the perioral facial muscles may be detached or severed, potentially altering their functional vectors and mimicry capabilities. Ensuring correct reconstruction and maintenance of anatomical sites and muscle vectors is crucial in these procedures. However, a standardized method for perioperative assessment of the facial musculature and function is currently lacking. The aim of this study was to develop a workflow to analyse the three-dimensional vectors of the facial musculature using magnetic resonance imaging (MRI) scans. A protocol for localizing the origins and insertions of these muscles was established. The protocol was implemented using the 3DMedX computer program and tested on 7 Tesla MRI scans obtained from 10 healthy volunteers. Inter- and intra-observer variability were assessed to validate the protocol. The absolute intra-observer variability was 2.6 mm (standard deviation 2.0 mm), and absolute inter-observer variability was 2.6 mm (standard deviation 1.5 mm). This study presents a reliable and reproducible method for analysing the spatial relationships and functional significance of the facial muscles. The workflow developed facilitates perioperative assessment of the facial musculature, potentially aiding clinicians in surgical planning and potentially enhancing the outcomes of midface surgery.

Fully automated landmarking and facial segmentation on 3D photographs

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

The Ability to Sustain Facial Expressions

To gain more insight into facial muscle function, imaging during action would be optimal. Magnetic resonance imaging is highly suitable for visualizing facial muscles. However, magnetic resonance imaging requires the individual to remain as still as possible for a while. Knowledge of the ability to sustain facial expressions is requisite before scanning individuals. This could help adapting the scanning protocol to obtain optimal quality of imaging the muscles in action. A study, including 10 healthy volunteers, was done to perceive the extent of movement while holding facial expressions of smiling and pouting. During 6 minutes, 3-dimensional photographs were taken every consecutive minute while the participants maintained their facial expressions as motionless as possible. The movement was objectified by creating distance maps between the 2 models and calculating the Root Mean Square using the software 3DMedX. The results showed that most movements occurred in the first minute, with a decrease of the intensity of the expression. After the first minute, the expression, although less intense, could be held stable. This implies that magnetic resonance imaging scanning during facial expression is possible, provided that the scanning starts after the first minute has elapsed. In addition, results demonstrated that more slackening of the muscles while smiling compared with pouting.

Anatomical position of the mandibular condyle after open versus closed treatment of unilateral fractures: A three-dimensional analysis

This study aimed to compare open and closed treatment for unilateral mandibular condyle neck and base fractures by final three-dimensional (3D) condylar position at 6 months follow-up. 3D position was associated with mandibular functioning and pain. A total of 21 patients received open (n = 11) or closed (n = 10) treatment. 3D positions were assessed on cone-beam computed tomography scans. Volume differences, root mean square, translations, and rotations were obtained related to the pursued anatomical position and compared between treatment groups by the Mann-Whitney U test. The 3D position parameters were associated with the maximum interincisal opening (MIO), mixing ability test (MAT), Mandibular Function Impairment Questionnaire (MFIQ), and pain based on Spearman correlation coefficients (rs). Translation in the medial-lateral direction was smaller after open treatment (P = 0.014). 3D position was not associated with the MAT; however, worse position was associated with a smaller MIO. A larger pitch rotation was associated with a worse MFIQ (rs = 0.499, P = 0.025). Volume reduction of the affected condyle was associated with more pain (rs = -0.503, P = 0.020). In conclusion, after unilateral condylar fractures, worse 3D position is associated with a smaller mouth opening and worse patient-reported outcomes. This is independent of the chosen treatment, despite a better anatomical reduction after open treatment.

The average three-dimensional face for different sex and age groups in a Dutch population

The increasing use of three-dimensional imaging calls for reference models representing large parts of the population. The aim of this prospective study was to create templates depicting facial maturation in the younger age groups. Healthy Dutch volunteers were captured, without selection of inclusions. Three-dimensional average faces were created using MATLAB, for both genders in four age groups (4-8 years, 8-12 years, 12-16 years, and ≥16 years). Variation within the groups was calculated and depicted on an average face with a green to red colour scale, corresponding to standard deviations between 0 and ≥ 3 mm, respectively. Measurements of the distances of eight peri-oral landmarks were provided as ratios. The statistical analysis was performed using ANOVA and Tukey's test. Three-dimensional reconstructions of the average face and their first principal component were created for each gender and age group. The first principal component comprised the facial width for each group, and the variation of landmarks was low. All ratios showed an increasing trend with increasing age, except for the ratio of philtrum width to mouth width. This study is novel in comparing facial morphology by means of ratios and in creating average faces for the different young age groups. These data provide useful insights into facial maturation, which might be beneficial for facial surgeons.

Creating Three-Dimensional Templates of Smiling and Pouting Faces for Different Sex- and Age Groups

Smile appearance has a major psychological impact. Orthognathic surgery, which has harmonizing results on skeletal structures, can negatively influence the smile appearance due to soft tissue effects. To enhance the aesthetic effects of orthognathic surgery on soft tissues, reference models for large parts of the hospital’s adherent area are called for. This study aims to create average facial models for different sex and age groups in two facial exercises: maximum closed smile, and pouting. These models were created using coherent point drift and Procrustes algorithms in MATLAB. Principal component analysis was performed, and of 20 surgical landmarks, the in-group variation using standard deviation was calculated. Three distances were analyzed: nasal width, philtral width, and mouth width. To correct for facial size, these distances were analyzed as a ratio of intercanthal width. In total, 328 healthy subjects were included in the study. Subjects were grouped by sex, and in age categories spanning four years each, with an adult group with all ages >16 years. For both smiling and pouting faces, all ratios increased with ageing. These templates and data can benefit facial surgeons, to obtain an expected outcome according to the age of the patient.