Shallow trochlear groove and narrow medial trochlear width at the proximal trochlea in patients with trochlear dysplasia: A three-dimensional computed tomography analysis.

PURPOSE: Although the Dejour classification is the primary classification system for evaluating trochlear dysplasia, concerns have been raised about its reliability owing to its qualitative criteria and challenges associated with obtaining accurate radiographs. This study aimed to quantify trochlear dysplasia using three-dimensional (3D) computed tomography (CT) reconstruction with novel parameters related to the transepicondylar axis (TEA). METHODS: Sixty patients were enrolled, including 20 with trochlear dysplasia and 40 healthy controls. The 3D CT model was generated using the Materialise Interactive Medical Image Control System software. The following six parameters were measured in eight consecutive planes at 15° intervals (planes 0-105): the distance from the TEA to the most cortical point of the lateral condyle ('LP-TEA', where LP stands for lateral peak), medial condyle ('MP-TEA', MP for medial peak) and deepest point of the trochlea ('TG-TEA', TG for trochlear groove). The distances from the medial epicondyle (MEC) to the corresponding TEA points were measured ('LP-MEC', 'MP-MEC' and 'TG-MEC'). RESULTS: In the dysplasia group, TG-TEA (planes 0, 15 and 30) and MP-MEC (planes 0, 15 and 30) were significantly greater than those in the control group (all p < 0.05 for planes of TG-TEA and MP-MEC). For type A dysplasia, LP-MEC (plane 0) was greater than that in the control group. For type B dysplasia, the MP-MEC (planes 0 and 15) and TG-TEA (planes 0 and 15) were greater than those of the control group. For type D dysplasia, MP-MEC (planes 0, 15 and 30) and TG-TEA (planes 0 and 15) were elevated. CONCLUSION: The 3D CT reconstruction analysis established a reproducible method for quantifying osseous trochlear morphology. Patients with trochlear dysplasia had a shallow TG and narrow medial trochlear width at tracking angles of 0°-30°. This finding corroborates the clinical manifestations of recurrent patellar instability that occur during early flexion. LEVEL OF EVIDENCE: Level III.

Development of real-time motion verification system using in-room optical images for respiratory-gated radiotherapy.

Phase-based respiratory-gated radiotherapy relies on the reproducibility of patient breathing during the treatment. To monitor the positional reproducibility of patient breathing against a 4D CT simulation, we developed a real-time motion verification system (RMVS) using an optical tracking technology. The system in the treatment room was integrated with a real-time position management system. To test the system, an anthropomorphic phantom that was mounted on a motion platform moved on a programmed breathing pattern and then underwent a 4D CT simulation with RPM. The phase-resolved anterior surface lines were extracted from the 4D CT data to constitute 4D reference lines. In the treatment room, three infrared reflective markers were attached on the superior, middle, and inferior parts of the phantom along with the body midline and then RMVS could track those markers using an optical camera system. The real-time phase information extracted from RPM was delivered to RMVS via in-house network software. Thus, the real-time anterior-posterior positions of the markers were simultaneously compared with the 4D reference lines. The technical feasibility of RMVS was evaluated by repeating the above procedure under several scenarios such as ideal case (with identical motion parameters between simulation and treatment), cycle change, baseline shift, displacement change, and breathing type changes (abdominal or chest breathing). The system capability for operating under irregular breathing was also investigated using real patient data. The evaluation results showed that RMVS has a competence to detect phase-matching errors between patient's motion during the treatment and 4D CT simulation. Thus, we concluded that RMVS could be used as an online quality assurance tool for phase-based gating treatments.

Three-dimensional orbital wall modeling using paranasal sinus segmentation.

PURPOSE: Three-dimensional orbital wall modeling is a time-consuming process because of the presence of pseudoforamina. We developed an automated three-dimensional modeling software to characterize the orbital wall, and evaluated it using data from fracture patients. METHODS: We first characterized the air and face regions using multiphase segmentation; the sinuses were segmented by applying morphological operations to air regions. Pseudoforamina of the orbital wall were offset with the segmented sinuses. Finally, the three-dimensional facial bone model, with orbital wall, was reconstructed from the segmented images. RESULTS: Ten computed tomography data sets were used to evaluate the proposed method. Results were compared with those obtained using the active contour model and manual segmentation. The process took 31.7 ± 8.0 s, which was 30-60 times faster than other methods. The average distances between surfaces obtained with the proposed method and those obtained with manual segmentation (normal side: 0.20 ± 0.06 mm; fractured side: 0.28 ± 0.10 mm) were approximately half those obtained using the active contour model. CONCLUSIONS: Three-dimensional orbital wall models, which were very similar to the manually segmented models, were archived within 1 min using the developed software, regardless of fracture presence. The proposed method might improve the safety and accuracy of surgical procedures.

A Novel Noninvasive Patient-Specific Navigation Method for Orbital Reconstructive Surgery: A Phantom Study Using Patient Data.

BACKGROUND: The correction of orbital deformities is an ongoing challenge in maxillofacial surgery. Computer-assisted navigation can improve surgical outcomes. However, conventional registration methods for navigation are not appropriate for orbital reconstructive surgery. This study proposes an accurate, noninvasive, patient-specific navigation method and demonstrates its feasibility. METHODS: A noninvasive, patient-specific registration frame based on the external auditory canals and upper front teeth was designed using software developed in-house. A three-dimensional craniofacial model was segmented from patient computed tomographic data for the registration frame. A customized craniofacial phantom was also made using this three-dimensional model, with 20 embedded target points on the orbital model and 21 landmark points on the reference standard model. The proposed method was compared with two conventional registration methods: the dental splint-based method and the invasive marker frame-based method. Twenty trials were conducted for evaluation. Target registration error and surface registration error were computed to measure accuracy. RESULTS: The proposed method showed a target registration error of 1.05 ± 0.52 mm, with greater accuracy than conventional methods (dental splint, 2.10 ± 0.63 mm; invasive marker frame, 1.22 ± 0.46 mm). The proposed method yielded the best results for surface registration error, with 0.38 mm of deviation (dental splint, 0.82 mm; invasive marker frame, 0.60 mm). CONCLUSION: The proposed noninvasive patient-specific registration method demonstrated superior results for both target registration error and surface registration error compared with other conventional registration methods for computer-assisted navigation in orbital reconstructive surgery. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.

Automatic detection of inferior alveolar nerve canal from cone-beam computed tomography images for dental surgery planning.

The inferior alveolar nerve canal is an important nerve canal in the jaw bone, and any damage to this canal can cause pain or fatal complications. Since such damage can be caused by a wrong surgical procedure or surgery plan, accurate surgery planning is necessary. Cone-beam computed tomography (CBCT) is a three-dimensional medical imaging method that is mainly used in dental treatment; however, identifying the nerve canal is difficult in CBCT images as compared to conventional CT images. This paper proposes a new concept of a panoramic curve for nerve canal detection and a detection algorithm that is usually applied to facial recognition was introduced in this study for the automatic detection of nerve canal in CBCT images.