Optimal configurations of an electromagnetic tracking system for 3D ultrasound imaging of pediatric hips - A phantom study.

Tracking the position and orientation of a two-dimensional (2D) ultrasound scanner to reconstruct a 3D volume is common, and its accuracy is important. In this study, a specific miniaturized electromagnetic (EM) tracking system was selected and integrated with a 2D ultrasound scanner, which was aimed to capture hip displacement in children with cerebral palsy. The objective of this study was to determine the optimum configuration, including the distance between the EM source and sensor, to provide maximum accuracy. The scanning volume was aimed to be 320 mm × 320 mm × 76 mm. The accuracy of the EM tracking was evaluated by comparing its tracking with those from a motion capture camera system. A static experiment showed that a warm-up time of 20 min was needed. The EM system provided the highest precision of 0.07 mm and 0.01° when the distance between the EM source and sensor was 0.65 m. Within the testing volume, the maximum position and rotational errors were 2.31 mm and 1.48°, respectively. The maximum error of measuring hip displacement on the 3D hip phantom study was 4 %. Based on the test results, the tested EM system was suitable for 3D ultrasound imaging of pediatric hips to assess hip displacement when optimal configuration was used.

DetSegDiff: A joint periodontal landmark detection and segmentation in intraoral ultrasound using edge-enhanced diffusion-based network.

Individuals with malocclusion require an orthodontic diagnosis and treatment plan based on the severity of their condition. Assessing and monitoring changes in periodontal structures before, during, and after orthodontic procedures is crucial, and intraoral ultrasound (US) imaging has been shown a promising diagnostic tool in imaging periodontium. However, accurately delineating and analyzing periodontal structures in US videos is a challenging task for clinicians, as it is time-consuming and subject to interpretation errors. This paper introduces DetSegDiff, an edge-enhanced diffusion-based network developed to simultaneously detect the cementoenamel junction (CEJ) and segment alveolar bone structure in intraoral US videos. An edge feature encoder is designed to enhance edge and texture information for precise delineation of periodontal structures. Additionally, we employed the spatial squeeze-attention module (SSAM) to extract more representative features to perform both detection and segmentation tasks at global and local levels. This study used 169 videos from 17 orthodontic patients for training purposes and was subsequently tested on 41 videos from 4 additional patients. The proposed method achieved a mean distance difference of 0.17 ± 0.19 mm for the CEJ and an average Dice score of 90.1% for alveolar bone structure. As there is a lack of multi-task benchmark networks, thorough experiments were undertaken to assess and benchmark the proposed method against state-of-the-art (SOTA) detection and segmentation individual networks. The experimental results demonstrated that DetSegDiff outperformed SOTA approaches, confirming the feasibility of using automated diagnostic systems for orthodontists.

Reliability and Accuracy of Ultrasound Measurement of Hip Displacement in Children with Cerebral Palsy.

OBJECTIVE: Hip migration percentage (MP) measured on anteroposterior pelvis radiographs is the gold standard to assess the severity of hip displacement in children with cerebral palsy (CP). Repeated exposure of these children to ionizing radiation under a hip surveillance program is undesirable. Recently, a semi-automatic approach to measure MPUS on ultrasound (US) images was validated in a phantom study. This pilot in vivo study applied the previous phantom method and aimed to determine the reliability and accuracy of the MPUS. METHODS: Thirty-four children (23 boys and 11 girls) aged 8.9 ± 3.1 y old and diagnosed with CP were recruited. A total of 59 hips were scanned once, while 43 of these were scanned twice to evaluate the test-retest reliability. Two raters (R1 and R2) manually measured MPUS; procedures included selecting images of interest, cropping a region of interest and removing soft tissues on hip US images. Custom software was developed to measure MP automatically after the manual pre-image processing. RESULTS: The intra-class correlation coefficients (ICC2,1) for the test-retest (R1), intra-rater (R1) and inter-rater (R1 vs R2) reliabilities were 0.90, 0.94 and 0.82, respectively. The standard error of measurement of MPUS for all three evaluations was ≤3.0%. The mean absolute difference between MPUS and MPX-ray and the percentage of MPUS within clinical acceptance error of 10% for R1 and R2 were (R1: 6.2% ± 4.9%, 84.7%) and (R2: 7.6% ± 6.1%, 73.7%), respectively. CONCLUSION: This study demonstrated that US scans were repeatable and MPUS could be measured reliably and accurately.

Automated Method for Growing Rod Length Measurement on Ultrasound Images in Children With Early Onset Scoliosis.

OBJECTIVE: To develop and validate machine learning algorithms to automatically extract the rod length of the magnetically controlled growing rod from ultrasound images (US) in a pilot study. METHODS: Two machine-learning (ML) models, called the "Boundary model" and "Rod model," were developed to identify specific rod segments on ultrasound images. The models were developed utilizing Mask Regional Convolutional Neural Networks (Mask RCNN). Ninety US images were acquired from 23 participants who had early onset scoliosis (EOS) surgeries; among those, 70 were used for model development, including training and validation, and 20 were used for testing by comparing the AI-based vs. manual measurements. RESULTS: The average precision (AP) of the ML models was 88.5% and 60.2%, respectively. The inter-method correlation coefficient (ICC) was 0.98, and the mean absolute difference ± standard deviation (MAD ± SD) between AI and manual measurements was 0.86 ± 1.0 mm. The Bland-Altman analysis showed no bias, and 90% of the data were within the 95% confidence interval. The automated method was reliable, accurate, and fast. Measurements were displayed in 4.6 seconds after the US image was inputted. CONCLUSION: This was the first AI-based method to measure the MCGR rod length on US images automatically.

Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints.

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and on-surface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.

Validity of machine learning algorithms for automatically extract growing rod length on radiographs in children with early-onset scoliosis.

The magnetically controlled growing rod technique is an effective surgical treatment for children who have early-onset scoliosis. The length of the instrumented growing rods is adjusted regularly to compensate for the normal growth of these patients. Manual measurement of rod length on posteroanterior spine radiographs is subjective and time-consuming. A machine learning (ML) system using a deep learning approach was developed to automatically measure the adjusted rod length. Three ML models-rod model, 58 mm model, and head-piece model-were developed to extract the rod length from radiographs. Three-hundred and eighty-seven radiographs were used for model development, and 60 radiographs with 118 rods were separated for final testing. The average precision (AP), the mean absolute difference (MAD) ± standard deviation (SD), and the inter-method correlation coefficient (ICC[2,1]) between the manual and artificial intelligence (AI) adjustment measurements were used to evaluate the developed method. The AP of the 3 models were 67.6%, 94.8%, and 86.3%, respectively. The MAD ± SD of the rod length change was 0.98 ± 0.88 mm, and the ICC[2,1] was 0.90. The average time to output a single rod measurement was 6.1 s. The developed AI provided an accurate and reliable method to detect the rod length automatically.

Using machine learning to automatically measure kyphotic and lordotic angle measurements on radiographs for children with adolescent idiopathic scoliosis.

Measuring the kyphotic angle (KA) and lordotic angle (LA) on lateral radiographs is important to truly diagnose children with adolescent idiopathic scoliosis. However, it is a time-consuming process to measure the KA because the endplate of the upper thoracic vertebra is normally difficult to identify. To save time and improve measurement accuracy, a machine learning algorithm was developed to automatically extract the KA and LA. The accuracy and reliability of the T1-T12 KA, T5-T12 KA, and L1-L5 LA were reported. A convolutional neural network was trained using 100 radiographs with data augmentation to segment the T1-L5 vertebrae. Sixty radiographs were used to test the method. Accuracy and reliability were reported using the percentage of measurements within clinical acceptance (≤9°), standard error of measurement (SEM), and inter-method intraclass correlation coefficient (ICC2,1). The automatic method detected 95 % (57/60), 100 %, and 100 % for T1-T12 KA, T5-T12 KA, and L1-L5 LA, respectively. The clinical acceptance rate, SEM, and ICC2,1 for T1-T12 KA, T5-T12 KA, and L1-L5 LA were (98 %, 0.80°, 0.91), (75 %, 4.08°, 0.60), and (97 %, 1.38°, 0.88), respectively. The automatic method measured quickly with an average of 4 ± 2 s per radiograph and illustrated how measurements were made on the image, allowing verifications by clinicians.

Reliability and accuracy of scoliotic parameters on using a wireless handheld 3D ultrasound for children with adolescent idiopathic scoliosis: a pilot study.

PURPOSE: To report the accuracy and reliability of Cobb angle (CA), axial vertebral rotation (AVR), kyphotic and lordotic angles (KA and LA) measurements on using a new 3D ultrasound (US) system. METHODS: Forty participants (34 F, 6 M, aged 14.0 ± 2.3 years) were recruited. The first 20 participants were scanned by the validated US system and the new US system. The other 20 participants were scanned with the new US system only. Two raters (R1 and R2) performed the measurements: R1 has 10 years of experience in radiology but is new in ultrasound scoliosis, while R2 has 30 years of scoliosis experience. All US images were measured twice by R1, and once by R2. Forty posteroanterior and 30 lateral standing radiographs were obtained and measured once by R1. Statistical analysis consisted of mean absolute difference (MAD), intraclass correlation coefficient (ICC (2,1)), and Bland-Altman plots. RESULTS: R1 showed excellent intra-rater and inter-rater reliability for US measurements with ICCs(2,1) ≥ 0.91. The inter-method reliability was good between the two US systems for all parameters with ICCs(2,1) ≥ 0.85 and maximum MAD of 3.4°. The new US showed good reliability and accuracy compared to radiographs for CA, AVR and KA with ICCs(2,1) ≥ 0.81 and maximum MAD of 5.8°, but poor results for LA with ICCs(2,1) of 0.27-0.35 and MADs of 14.0°-15.4°. CONCLUSION: The new 3D US system showed good reliability and accuracy for CA, AVR and KA measurements, but a large measurement discrepancy on LA. A new measurement method for US LA may need to investigate.

Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information for oral health diagnosis and treatment and shed light on real-time detection of developmental dentistry. However, the grand challenge is that the current ultrasound technologies are meant for external use due to their bulkiness and low frequency. We report a compact versatile ultrasound intraoral device that consists of a rotational probe head robustly pivoted around a hand-held and portable handle for real-time imaging of intraoral anatomy using high-frequency ultrasonography (up to 25 MHz). The intraoral ultrasound device that could be adjusted for various orientations of the imaging planes by rotating the head provides real-time, high-resolution ultrasonograms of intraoral structures, including dento-periodontium of most tooth types and maxillary palate. Machine learning-based algorithms are integrated to automate the identification of important structures, including alveolar bone and cementum-enamel junction. The intraoral ultrasound device smartened with artificial intelligence could innovate oral health diagnosis and treatment plans toward precision health and patient care.

Validity and accuracy of automatic cobb angle measurement on 3D spinal ultrasonographs for children with adolescent idiopathic scoliosis: SOSORT 2024 award winner.

PURPOSE: Ultrasonography for scoliosis is a novel imaging method that does not expose children with adolescent idiopathic scoliosis (AIS) to radiation. A single ultrasound scan provides 3D spinal views directly. However, measuring ultrasonograph parameters is challenging, time-consuming, and requires considerable training. This study aimed to validate a machine learning method to measure the coronal curve angle on ultrasonographs automatically. METHODS: A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed. RESULTS: The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7 s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement. CONCLUSION: The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm's quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.

Validity of a fast automated 3d spine reconstruction measurements for biplanar radiographs: SOSORT 2024 award winner.

PURPOSE: To validate a fast 3D biplanar spinal radiograph reconstruction method with automatic extract curvature parameters using artificial intelligence (AI). METHODS: Three-hundred eighty paired, posteroanterior and lateral, radiographs from the EOS X-ray system of children with adolescent idiopathic scoliosis were randomly selected from the database. For the AI model development, 304 paired images were used for training; 76 pairs were employed for testing. The validation was evaluated by comparing curvature parameters, including Cobb angles (CA), apical axial vertebral rotation (AVR), kyphotic angle (T1-T12 KA), and lordotic angle (L1-L5 LA), to manual measurements from a rater with 8 years of scoliosis experience. The mean absolute differences ± standard deviation (MAD ± SD), the percentage of measurements within the clinically acceptable errors, the standard error of measurement (SEM), and the inter-method intraclass correlation coefficient ICC[2,1] were calculated. The average reconstruction speed of the 76 test images was recorded. RESULTS: Among the 76 test images, 134 and 128 CA were exported automatically and measured manually, respectively. The MAD ± SD for CA, AVR at apex, KA, and LA were 3.3° ± 3.5°, 1.5° ± 1.5°, 3.3° ± 2.6° and 3.5° ± 2.5°, respectively, and 98% of these measurements were within the clinical acceptance errors. The SEMs and the ICC[2,1] for the compared parameters were all less than 0.7° and > 0.94, respectively. The average time to display the 3D spine and report the measurements was 5.2 ± 1.3 s. CONCLUSION: The developed AI algorithm could reconstruct a 3D scoliotic spine within 6 s, and the automatic curvature parameters were accurately and reliably extracted from the reconstructed images.

Ultrasonic mapping of midpalatal suture - An ex-vivo study.

OBJECTIVE: Rapid maxillary expansion is a common orthodontic procedure to correct maxillary constriction. Assessing the midpalatal suture (MPS) expansion plays a crucial role in treatment planning to determine its effectiveness. The objectives of this preliminary investigation are to demonstrate a proof of concept that the palatal bone underlying the rugae can be clearly imaged by ultrasound (US) and the reconstructed axial view of the US image accurately maps the MPS patency. METHODS: An ex-vivo US scanning was conducted on the upper jawbones of two piglet's carcasses before and after the creation of bone defects, which simulated the suture opening. The planar images were processed to enhance bone intensity distribution before being orderly stacked to fuse into a volume. Graph-cut segmentation was applied to delineate the palatal bone to generate a bone volume. The accuracy of the reconstructed bone volume and the suture opening was validated by the micro-computed tomography (µCT) data used as the ground truth and compared with cone beam computed tomography (CBCT) data as the clinical standard. Also included in the comparison is the rugae thickness. Correlation and Bland-Altman plots were used to test the agreement between the two methods: US versus µCT/CBCT. RESULTS: The reconstruction of the US palatal bone volumes was accurate based on surface topography comparison with a mean error of 0.19 mm for pre-defect and 0.15 mm and 0.09 mm for post-defect models of the two samples, respectively when compared with µCT volumes. A strong correlation (R2 ≥ 0.99) in measuring MPS expansion was found between US and µCT/CBCT with MADs of less than 0.05 mm, 0.11 mm and 0.23 mm for US, µCT and CBCT, respectively. CONCLUSIONS: It was possible to axially image the MPS opening and rugae thickness accurately using high-frequency ultrasound. CLINICAL SIGNIFICANCE: This study introduces an ionizing radiation-free, low-cost, and portable technique to accurately image a difficult part of oral cavity anatomy. The advantages of conceivable visualization could promise a successful clinical examination of MPS to support the predictable treatment outcome of maxillary transverse deficiency.

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System.

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Applying Machine Learning and Point-Set Registration to Automatically Measure the Severity of Spinal Curvature on Radiographs.

OBJECTIVE: Measuring the severity of the lateral spinal curvature, or Cobb angle, is critical for monitoring and making treatment decisions for children with adolescent idiopathic scoliosis (AIS). However, manual measurement is time-consuming and subject to human error. Therefore, clinicians seek an automated measurement method to streamline workflow and improve accuracy. This paper reports on a novel machine learning algorithm of cascaded convolutional neural networks (CNN) to measure the Cobb angle on spinal radiographs automatically. METHODS: The developed method consisted of spinal column segmentation using a CNN, vertebra localization and segmentation using iterative vertebra body location coupled with another CNN, point-set registration to correct vertebra segmentations, and Cobb angle measurement using the final segmentations. Measurement performance was evaluated with the circular mean absolute error (CMAE) and percentage within clinical acceptance ([Formula: see text]) between automatic and manual measurements. Analysis was separated by curve severity to identify any potential systematic biases using independent samples Student's t-tests. RESULTS: The method detected 346 of the 352 manually measured Cobb angles (98%), with a CMAE of 2.8° and 91% of measurements within the 5° clinical acceptance. No statistically significant differences were found between the CMAEs of mild ([Formula: see text]), moderate (25°-45°), and severe ([Formula: see text]) groups. The average measurement time per radiograph was 17.7±10.2s, improving upon the estimated average of 30s it takes an experienced rater to measure. Additionally, the algorithm outputs segmentations with the measurement, allowing clinicians to interpret measurement results. DISCUSSION/CONCLUSION: The developed method measured Cobb angles on radiographs automatically with high accuracy, quick measurement time, and interpretability, suggesting clinical feasibility.

Validation of an artificial intelligence-based method to automate Cobb angle measurement on spinal radiographs of children with adolescent idiopathic scoliosis.

BACKGROUND: Accurately measuring the Cobb angle on radiographs is crucial for diagnosis and treatment decisions for adolescent idiopathic scoliosis (AIS). However, manual Cobb angle measurement is time-consuming and subject to measurement variation, especially for inexperienced clinicians. AIM: This study aimed to validate a novel artificial-intelligence-based (AI) algorithm that automatically measures the Cobb angle on radiographs. DESIGN: This is a retrospective cross-sectional study. SETTING: The population of patients attended the Stollery Children's Hospital in Alberta, Canada. POPULATION: Children who: 1) were diagnosed with AIS, 2) were aged between 10 and 18 years old, 3) had no prior surgery, and 4) had a radiograph out of brace, were enrolled. METHODS: A total of 330 spinal radiographs were used. Among those, 130 were used for AI model development and 200 were used for measurement validation. Automatic Cobb angle measurements were validated by comparing them with manual ones measured by a rater with 20+ years of experience. Analysis was performed using the standard error of measurement (SEM), inter-method intraclass correlation coefficient (ICC2,1), and percentage of measurements within clinical acceptance (≤5°). Subgroup analysis was conducted by severity, region, and X-ray system to identify any systematic biases. RESULTS: The AI method detected 346 of 352 manually measured curves (mean±standard deviation: 24.7±9.5°), achieving 91% (316/346) of measurements within clinical acceptance. Excellent reliability was obtained with 0.92 ICC and 0.79° SEM. Comparable performance was found throughout all subgroups, and no systematic biases in performance affecting any subgroup were discovered. The algorithm measured each radiograph approximately 18s on average which is slightly faster than the estimated measurement time of an experienced rater. Radiographs taken by the EOS X-ray system were measured more quickly on average than those taken by a conventional digital X-ray system (10s vs. 26s). CONCLUSIONS: An AI-based algorithm was developed to measure the Cobb angle automatically on radiographs and yielded reliable measurements quickly. The algorithm provides detailed images on how the angles were measured, providing interpretability that can give clinicians confidence in the measurements. CLINICAL REHABILITATION IMPACT: Employing the algorithm in practice could streamline clinical workflow and optimize measurement accuracy and speed in order to inform AIS treatment decisions.

A feasibility study of application of purpose-design frame and 3-D clinical ultrasound in assessment and design of spinal orthoses for adolescent idiopathic scoliosis.

BACKGROUND: The immediate in-orthosis correction of adolescent idiopathic scoliosis (AIS) is a useful prognostic parameter for the long-term orthotic treatment outcome. The 3-D clinical ultrasound technique is considered a noninvasive alternative to assess scoliotic deformities that could be applied in the orthotic treatment of AIS. OBJECTIVE: This study aimed to investigate the feasibility of a purpose-design assessment frame in estimating biomechanical effects of the controlling pads of a spinal orthosis under the guidance of the ultrasound system. METHODS: Twenty-six subjects with AIS were recruited and arranged to position inside the assessment frame, and controlling pads were applied strategically while the scoliotic deformities were assessed by clinical ultrasound to obtain at least 30% curvature correction, and the body shape was then captured using a computer-aided design and computer-aided manufacture system, and spinal orthoses were subsequently fabricated. The preorthosis and immediate in-orthosis coronal and sagittal X-rays were used for comparison. RESULTS: X-ray assessments showed that the mean coronal Cobb angle and lumbar lordosis of the subjects from the preorthosis to immediate in-orthosis visits decreased significantly ( p < 0.05) from 29.6° to 16.6°, and from 47.2° to 35.3°, respectively. CONCLUSIONS: This feasibility study showed that the proposed method would have a good potential to improve orthotic treatment outcome in a documented approach that should be considered for implementation into routine clinical practice aiming to reduce the chance of deformity deterioration leading to surgical intervention. However, a controlled group study is required to compare the results.

Measuring the alveolar bone level in adolescents: A comparison between ultrasound and cone beam computed tomography.

BACKGROUND: Cone beam computed tomography (CBCT) is an imaging modality, which is used routinely in orthodontic diagnosis and treatment planning but delivers much higher radiation than conventional dental radiographs. Ultrasound is a noninvasive imaging method that creates an image without ionizing radiation. AIM: To investigate the reliability of ultrasound and the agreement between ultrasound and CBCT in measuring the alveolar bone level (ABL) on the buccal/labial side of the incisors in adolescent orthodontic patients. DESIGN: One hundred and eighteen incisors from 30 orthodontic adolescent patients were scanned by CBCT with 0.3-mm voxel size and ultrasound at 20 MHz frequency. The ABL, distance from the cementoenamel junction (CEJ) to the alveolar bone crest (ABC), was measured twice to evaluate the agreement between ultrasound and CBCT. In addition, the intra- and inter-rater reliabilities in measuring the ABL by four raters were compared. RESULTS: The mean difference (MD) in the ABL between ultrasound and CBCT was -0.07 mm with 95% limit of agreement (LoA) from -0.47 to 0.32 mm for all teeth. For each jaw, the MDs between the ultrasound and CBCT were -0.18 mm (for mandible with 95% LoA from -0.53 to 0.18 mm) and 0.03 mm (for maxilla with 95% LoA from -0.28 to 0.35 mm). In comparison, ultrasound had higher intra-rater (ICC = 0.83-0.90) and inter-rater reliabilities (ICC = 0.97) in ABL measurement than CBCT (ICC = 0.56-0.78 for intra-rater and ICC = 0.69 for inter-rater reliabilities). CONCLUSION: CBCT parameters used in orthodontic diagnosis and treatment planning in adolescents may not be a reliable tool to assess the ABL for the mandibular incisors. On the contrary, ultrasound imaging, an ionizing radiation-free, inexpensive, and portable diagnostic tool, has potential to be a reliable diagnostic tool in assessing the ABL in adolescent patients.

Ultrasound Imaging of Hip Displacement in Children With Cerebral Palsy.

OBJECTIVE: An approach to estimation of hip displacement on ultrasound (US) images is described. Its accuracy is validated through numerical simulation, an in vitro study with 3-D-printed hip phantoms and pilot in vivo data. METHODS: A diagnostic index, migration percentage (MP), is defined by the ratio of acetabulum-femoral head distance to femoral head width. The acetabulum-femoral head distance could be measured directly on hip US images, while the femoral head width was estimated from the diameter of a best-fit circle. Simulation was performed to evaluate the accuracy of circle fitting with noiseless and noisy data. Surface roughness was also considered. Nine hip phantoms (three different sizes of femur head × three MP values) and 10 US hip images were used in this study. RESULTS: The maximum diameter error was 16.1 ± 8.5% when the roughness and noise were 20% of the original radius and 20% of the wavelet peak, respectively. In the phantom study, the percentage errors of MPs between the 3-D-design US and X-ray US were 0.3%-6.6% and 0.0%-5.7%, respectively. From the pilot clinical trial, the mean absolute difference between the X-ray-US MPs was 3.5 ± 2.8% (1%-9%). CONCLUSION: This study indicates that the US method can be used to evaluate hip displacement in children.