Three-Dimensional Computer-Assisted Surgical Planning, Manufacturing, Intraoperative Navigation, and Computed Tomography in Maxillofacial Trauma

Amundson, Melissa; Newman, Marshall F.; Cheng, Allen; Khatib, Baber; Cuddy, Karl; Patel, Ashish

doi:10.1016/j.cxom.2020.05.006

Cited by 10 publications

(11 citation statements)

References 17 publications

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“…Computer-assisted procedures represent an essential component of contemporary craniomaxillofacial surgery: Virtual three-dimensional datasets enable detailed preoperative planning of resection and/or reconstruction in head and neck cancer and trauma and provide the basis for the fabrication of patient-specific cutting guides and patient specific implants [ 1 , 2 ]. Moreover, computer-assisted planning can aid in preoperatively determining the optimal surgical approach, especially in highly complex regions such as the skull base [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

et al. 2022

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Purpose Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. Methods The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). Results Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen. Conclusion In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

et al. 2022

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“…Pronounced post‐traumatic deformities may be associated with loss of sagittal projection, changes in vertical facial height, widening of the face, and facial asymmetry. In addition, impaired visual function, masticatory dysfunction, malocclusion, or temporomandibular joint disorders can occur 1 …”

Section: Introductionmentioning

confidence: 99%

“…In addition, impaired visual function, masticatory dysfunction, malocclusion, or temporomandibular joint disorders can occur. 1 Reconstructive surgery appears to benefit substantially from individualized virtual surgical planning (VSP), yielding more predictable outcomes than freehand techniques. 1 The advantages of computerassisted planning, especially in complex surgery, are well documented in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…1 Reconstructive surgery appears to benefit substantially from individualized virtual surgical planning (VSP), yielding more predictable outcomes than freehand techniques. 1 The advantages of computerassisted planning, especially in complex surgery, are well documented in the literature. [2][3][4][5][6] The application of surgical navigation systems supplementarily to these procedures is proposed to further improve outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Digital planning and individual implants for secondary reconstruction of midfacial deformities: A pilot study

Liokatis

Malenova

Fegg

et al. 2022

Laryngoscope Investig Oto

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Objective To evaluate the feasibility and accuracy of implementing three‐dimensional virtual surgical planning (VSP) and subsequent transfer by additive manufactured tools in the secondary reconstruction of residual post‐traumatic deformities in the midface. Methods Patients after secondary reconstruction of post‐traumatic midfacial deformities were included in this case series. The metrical deviation between the virtually planned and postoperative position of patient‐specific implants (PSI) and bone segments was measured at corresponding reference points. Further information collected included demographic data, post‐traumatic symptoms, and type of transfer tools. Results Eight consecutive patients were enrolled in the study. In five patients, VSP with subsequent manufacturing of combined predrilling/osteotomy guides and PSI was performed. In three patients, osteotomy guides, repositioning guides, and individually prebent plates were used following VSP. The median distances between the virtually planned and the postoperative position of the PSI were 2.01 mm (n = 18) compared to a median distance concerning the bone segments of 3.05 mm (n = 12). In patients where PSI were used, the median displacement of the bone segments was lower (n = 7, median 2.77 mm) than in the group with prebent plates (n = 5, 3.28 mm). Conclusion This study demonstrated the feasibility of VSP and transfer by additive manufactured tools for the secondary reconstruction of complex residual post‐traumatic deformities in the midface. However, the median deviations observed in this case series were unexpectedly high. The use of navigational systems may further improve the level of accuracy.

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“…Computer-assisted surgical procedures have gained considerable importance in dentistry and craniomaxillofacial surgery in recent years, and it seems likely that this trend will accelerate further. Important application areas include orthognathic surgery, 1 dental implantology, 2,3 maxillofacial trauma, 4 craniomaxillofacial reconstruction, 5 and craniofacial malformation. 6 The process of the computer-aided surgery (CAS) follows a characteristic sequence.…”

Section: Introductionmentioning

confidence: 99%

Geometric accuracy of magnetic resonance imaging–derived virtual 3‐dimensional bone surface models of the mandible in comparison to computed tomography and cone beam computed tomography: A porcine cadaver study

Probst

Burian

Malenova

et al. 2021

Clin Implant Dent Rel Res

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Background: Providing accurate 3-dimensional virtual bone surface models is a prerequisite for virtual surgical planning and additive manufacturing in craniomaxillofacial surgery. For this purpose, magnetic resonance imaging (MRI) may be a radiation-free alternative to computed tomography (CT) and cone beam computed tomography (CBCT).Purpose: The aim of this study was to assess the geometric accuracy of 3-dimensional T1-weighted MRI-derived virtual bone surface models of the mandible in comparison to CT and CBCT.Materials and methods: Specimens of the mandible from porcine cadavers were scanned with (1) a 3-dimensional T1-weighted MRI sequence (0.6 mm isotropic voxel) optimized for bone imaging, (2) CT, and (3) CBCT. Cortical mandibular structures (n = 10) were segmented using semiautomated and manual techniques.Imaging-based virtual 3-dimensional models were aligned with a high-resolution optical 3-dimensional surface scan of the dissected bone (=ground truth) and global geometric deviations were calculated (mean surface distance [MSD]/root-mean-square distance [RMSD]). Agreement between the imaging modalities was assessed by equivalence testing and Bland-Altman analysis.Results: Intra-and inter-rater agreement was on a high level for all modalities. Global geometric deviations (MSD/RMSD) between optical scans and imaging modalities were 0.225 ± 0.020 mm/0.345 ± 0.074 mm for CT, 0.280 ± 0.067 mm/0.371 ± 0.074 mm for MRI, and 0.352 ± 0.076 mm/0.454 ± 0.071 mm for CBCT. All imaging modalities were statistically equivalent within an equivalence margin of ±0.3 mm, and Bland-Altman analysis indicated high agreement as well.

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Three-Dimensional Computer-Assisted Surgical Planning, Manufacturing, Intraoperative Navigation, and Computed Tomography in Maxillofacial Trauma

Cited by 10 publications

References 17 publications

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks

Digital planning and individual implants for secondary reconstruction of midfacial deformities: A pilot study

Geometric accuracy of magnetic resonance imaging–derived virtual 3‐dimensional bone surface models of the mandible in comparison to computed tomography and cone beam computed tomography: A porcine cadaver study

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