Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure.

PURPOSE: The diagnosis of chronic increased intracranial pressure (IIP)is often based on subjective evaluation or clinical metrics with low predictive value. We aimed to quantify cranial bone changes associated with pediatric IIP using CT images and to identify patients at risk. METHODS: We retrospectively quantified local cranial bone thickness and mineral density from the CT images of children with chronic IIP and compared their statistical differences to normative children without IIP adjusting for age, sex and image resolution. Subsequently, we developed a classifier to identify IIP based on these measurements. Finally, we demonstrated our methods to explore signs of IIP in patients with non-syndromic sagittal craniosynostosis (NSSC). RESULTS: We quantified a significant decrease of bone density in 48 patients with IIP compared to 1,018 normative subjects (P < .001), but no differences in bone thickness (P = .56 and P = .89 for age groups 0-2 and 2-10 years, respectively). Our classifier demonstrated 83.33% (95% CI: 69.24%, 92.03%) sensitivity and 87.13% (95% CI: 84.88%, 89.10%) specificity in identifying patients with IIP. Compared to normative subjects, 242 patients with NSSC presented significantly lower cranial bone density (P < .001), but no differences were found compared to patients with IIP (P = .57). Of patients with NSSC, 36.78% (95% CI: 30.76%, 43.22%) presented signs of IIP. CONCLUSION: Cranial bone changes associated with pediatric IIP can be quantified from CT images to support earlier diagnoses of IIP, and to study the presence of IIP secondary to cranial pathology such as non-syndromic sagittal craniosynostosis.

Assessing the Relationship between Radiology Department Research Funding and Institutional Community Inclusion and Investment.

Background Health care access disparities and lack of inclusion in clinical research have been well documented for marginalized populations. However, few studies exist examining the research funding of institutions that serve historically underserved groups. Purpose To assess the relationship between research funding awarded to radiology departments by the National Institutes of Health (NIH) and Lown Institute Hospitals Index rankings for inclusivity and community benefit. Materials and Methods This retrospective study included radiology departments awarded funding from the NIH between 2017 and 2021. The 2021 Lown Institute Hospitals Index rankings for inclusivity and community benefit were examined. The inclusivity metric measures how similar a hospital's patient population is to the surrounding community in terms of income, race and ethnicity, and education level. The community benefit metric measures charity care spending, Medicaid as a proportion of patient revenue, and other community benefit spending. Linear regression and Pearson correlation coefficients (r values) were used to evaluate the relationship between aggregate NIH radiology department research funding and measures of inclusivity and community benefit. Results Seventy-five radiology departments that received NIH funding ranging from $195 000 to $216 879 079 were included. A negative correlation was observed between the amount of radiology department research funding received and institutional rankings for serving patients from racial and/or ethnic minorities (r = -0.34; P < .001), patients with low income (r = -0.44; P < .001), and patients with lower levels of education (r = -0.46; P < .001). No correlation was observed between the amount of radiology department research funding and institutional rankings for charity care spending (r = -0.19; P = .06), community investment (r = -0.04; P = .68), and Medicaid as a proportion of patient revenue (r = -0.10; P = .22). Conclusion Radiology departments that received more NIH research funding were less likely to serve patients from racial and/or ethnic minorities and patients who had low income or lower levels of education. © RSNA, 2024 See also the editorial by Mehta and Rosen in this issue.

Neural signatures of emotion regulation.

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individual's emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotion regulation produces a unique signature that is differentiable from passive viewing of negative and neutral imagery.

Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.

BACKGROUND AND OBJECTIVE: Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry. METHODS: We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment. RESULTS: We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment. CONCLUSION: Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.

Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.

Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.

Data-driven Normative Reference of Pediatric Cranial Bone Development.

Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria. Methods: The model was built using a cross-sectional and multi-institutional pediatric computed tomography image dataset with 2068 subjects without cranial pathology (age 0-10 years). We combined principal component analysis and temporal regression to build a statistical model of cranial bone development at every location of the calvaria. We studied the influences of sex on cranial bone growth, and our bone density model allowed quantifying for the first time suture fusion as a continuous temporal process. We evaluated the predictive accuracy of our model using an independent longitudinal image dataset of 51 subjects. Results: Our model achieved temporal predictive errors of 2.98 ± 0.69 mm, 0.27 ± 0.29 mm, and 76.72 ± 91.50 HU in cranial bone shape, thickness, and mineral density changes, respectively. Significant sex differences were found in intracranial volume and bone surface areas (P < 0.01). No significant differences were found in cephalic index, bone thickness, mineral density, or suture fusion. Conclusions: We presented the first pediatric age- and sex-specific statistical reference for local cranial bone shape, thickness, and mineral density changes. We showed its predictive accuracy using an independent longitudinal dataset, we studied developmental differences associated with sex, and we quantified suture fusion as a continuous process.

Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model.

BACKGROUND AND OBJECTIVE: The fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions. METHODS: In this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images. RESULTS: The results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data). CONCLUSIONS: The proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.

Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis.

BACKGROUND: The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection. METHODS: This study presents a planning software to automatically estimate osteotomies in the fronto-orbital region and calculate the optimal configuration of the bone fragments required to achieve an optimal postoperative shape. The optimal cranial shape is obtained using a statistical head shape model built from 201 healthy subjects (age 23 ± 20 months; 89 girls). Automatic virtual plans were computed for nine patients (age 10.68 ± 1.73 months; four girls) with different degrees of overcorrection, and compared with manual plans designed by experienced surgeons. RESULTS: Postoperative cranial shapes generated by automatic interventional plans present accurate matching with normative morphology and enable to reduce the malformations in the fronto-orbital region by 82.01 ± 6.07%. The system took on average 19.22 seconds to provide the automatic plan, and allows for personalized levels of overcorrection. The automatic plans with an overcorrection of 7 mm in minimal frontal breadth provided the closest match (no significant difference) to the manual plans. CONCLUSIONS: The automatic software technology effectively achieves correct cranial morphometrics and volumetrics with respect to normative cranial shapes. The automatic approach has the potential to reduce the duration of preoperative planning, reduce inter-surgeon variability, and provide consistent surgical outcomes.

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child's risk of presenting with a genetic syndrome for use at the point of care. METHODS: In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes. We trained the machine learning models on facial photographs from children (aged <21 years) with a clinical or molecular diagnosis of a genetic syndrome and controls without a genetic syndrome matched for age, sex, and race or ethnicity. Images were obtained from three publicly available databases (the Atlas of Human Malformations in Diverse Populations of the National Human Genome Research Institute, Face2Gene, and the dataset available from Ferry and colleagues) and the archives of the Children's National Hospital (Washington, DC, USA), in addition to photographs taken on a standard smartphone at the Children's National Hospital. We designed a deep learning architecture structured into three neural networks, which performed image standardisation (Network A), facial morphology detection (Network B), and genetic syndrome risk estimation, accounting for phenotypic variations due to age, sex, and race or ethnicity (Network C). Data were divided randomly into 40 groups for cross validation, and the performance of the model was evaluated in terms of accuracy, sensitivity, and specificity in both the total population and stratified by race or ethnicity, age, and sex. FINDINGS: Our dataset included 2800 facial photographs of children (1318 [47%] female and 1482 [53%] male; 1576 [56%] White, 432 [15%] African, 430 [15%] Hispanic, and 362 [13%] Asian). 1400 children with 128 genetic conditions were included (the most prevalent being Williams-Beuren syndrome [19%], Cornelia de Lange syndrome [17%], Down syndrome [16%], 22q11.2 deletion [13%], and Noonan syndrome [12%] syndrome) in addition to 1400 photographs of matched controls. In the total population, our deep learning-based model had an accuracy of 88% (95% CI 87-89) for the detection of a genetic syndrome, with 90% sensitivity (95% CI 88-92) and 86% specificity (95% CI 84-88). Accuracy was greater in White (90%, 89-91) and Hispanic populations (91%, 88-94) than in African (84%, 81-87) and Asian populations (82%, 78-86). Accuracy was also similar in male (89%, 87-91) and female children (87%, 85-89), and similar in children younger than 2 years (86%, 84-88) and children aged 2 years or older (eg, 89% [87-91] for those aged 2 years to <5 years). INTERPRETATION: This genetic screening technology could support early risk stratification at the point of care in global populations, which has the potential accelerate diagnosis and reduce mortality and morbidity through preventive care. FUNDING: Children's National Hospital and Government of Abu Dhabi.

Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo.

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population.

Objective differential diagnosis of Noonan and Williams-Beuren syndromes in diverse populations using quantitative facial phenotyping.

INTRODUCTION: Patients with Noonan and Williams-Beuren syndrome present similar facial phenotypes modulated by their ethnic background. Although distinctive facial features have been reported, studies show a variable incidence of those characteristics in populations with diverse ancestry. Hence, a differential diagnosis based on reported facial features can be challenging. Although accurate diagnoses are possible with genetic testing, they are not available in developing and remote regions. METHODS: We used a facial analysis technology to identify the most discriminative facial metrics between 286 patients with Noonan and 161 with Williams-Beuren syndrome with diverse ethnic background. We quantified the most discriminative metrics, and their ranges both globally and in different ethnic groups. We also created population-based appearance images that are useful not only as clinical references but also for training purposes. Finally, we trained both global and ethnic-specific machine learning models with previous metrics to distinguish between patients with Noonan and Williams-Beuren syndromes. RESULTS: We obtained a classification accuracy of 85.68% in the global population evaluated using cross-validation, which improved to 90.38% when we adapted the facial metrics to the ethnicity of the patients (p = 0.024). CONCLUSION: Our facial analysis provided for the first time quantitative reference facial metrics for the differential diagnosis Noonan and Williams-Beuren syndromes in diverse populations.

Three-dimensional photography for intraoperative morphometric analysis in metopic craniosynostosis surgery.

PURPOSE: Surgical correction of metopic craniosynostosis typically involves open cranial vault remodeling. Accurate translation of the virtual surgical plan into the operating room is challenging due to the lack of tools for intraoperative analysis of the surgical outcome. This study aimed to evaluate the feasibility of using a hand-held 3D photography device for intraoperative evaluation and guidance during cranial vault surgical reconstruction. METHODS: A hand-held structured light scanner was used for intraoperative 3D photography during five craniosynostosis surgeries, obtaining 3D models of skin and bone surfaces before and after the remodeling. The accuracy of this device for 3D modeling and morphology quantification was evaluated using preoperative computed tomography imaging as gold-standard. In addition, the time required for intraoperative 3D photograph acquisition was measured. RESULTS: The average error of intraoperative 3D photography was 0.30 mm. Moreover, the interfrontal angle and the transverse forehead width were accurately measured in the 3D photographs with an average error of 0.72 degrees and 0.62 mm. Surgeon's feedback indicates that this technology can be integrated into the surgical workflow without substantially increasing surgical time. CONCLUSION: Hand-held 3D photography is an accurate technique for objective quantification of intraoperative cranial vault morphology and guidance during metopic craniosynostosis surgical reconstruction. This noninvasive technique does not substantially increase surgical time and does not require exposure to ionizing radiation, presenting a valuable alternative to computed tomography imaging. The proposed methodology can be integrated into the surgical workflow to assist during cranial vault remodeling and ensure optimal surgical outcomes.

Neurocranium thickness mapping in early childhood.

The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.

Automated Measurement of Intracranial Volume Using Three-Dimensional Photography.

BACKGROUND: Current methods to analyze three-dimensional photography do not quantify intracranial volume, an important metric of development. This study presents the first noninvasive, radiation-free, accurate, and reproducible method to quantify intracranial volume from three-dimensional photography. METHODS: In this retrospective study, cranial bones and head skin were automatically segmented from computed tomographic images of 575 subjects without cranial abnormality (average age, 5 ± 5 years; range, 0 to 16 years). The intracranial volume and the head volume were measured at the cranial vault region, and their relation was modeled by polynomial regression, also accounting for age and sex. Then, the regression model was used to estimate the intracranial volume of 30 independent pediatric patients from their head volume measured using three-dimensional photography. Evaluation was performed by comparing the estimated intracranial volume with the true intracranial volume of these patients computed from paired computed tomographic images; two growth models were used to compensate for the time gap between computed tomographic and three-dimensional photography. RESULTS: The regression model estimated the intracranial volume of the normative population from the head volume calculated from computed tomographic images with an average error of 3.81 ± 3.15 percent (p = 0.93) and a correlation (R) of 0.96. The authors obtained an average error of 4.07 ± 3.01 percent (p = 0.57) in estimating the intracranial volume of the patients from three-dimensional photography using the regression model. CONCLUSION: Three-dimensional photography with image analysis provides measurement of intracranial volume with clinically acceptable accuracy, thus offering a noninvasive, precise, and reproducible method to evaluate normal and abnormal brain development in young children. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, V.

Occult Scaphocephaly: A Forme Fruste Phenotype of Sagittal Craniosynostosis.

INTRODUCTION: Latent cranial suture fusions may present with mild or absent phenotypic changes that make the clinical diagnosis challenging. Recent reports describe patients with sagittal synostosis and a normal cranial index (CI), a condition termed normocephalic sagittal craniosynostosis (NSC). The goal of this study is to evaluate the shape and intracranial volume (ICV) in a cohort of NSC patients using quantitative cranial shape analysis (CSA). METHODS: We identified 19 patients (7.5 ±â€Š2.28 years) between 2011 and 2016, who presented to our hospital with NSC. Cranial index and CSA were measured from the computed tomography image. Cranial shape analysis calculates the distances between the patient's cranial shape and its closest normal shape. Intracranial volume was measured and compared to an established age-matched normative database. RESULTS: Cranial index revealed 15 (78.9%) patients within the mesocephalic range and 4 patients (21.1%) in the brachycephalic range. Detailed CSA identified 15 (78.9%) patients with subtle phenotypic changes along the scaphocephalic spectrum (ie, subtle anterior and posterior elongation with inter-parietal narrowing) and 1 patient (5.3%) with isolated overdevelopment on the posterior part of the right parietal bone. Three patients (15.8%) had a CSA close to normal. Mean ICV was 1410.5 ±â€Š192.77cc; most patients (78.9%) fell within ±2 standard deviations. CONCLUSION: Quantitative CSA revealed that most of the patients with NSC had cranial shape abnormalities, consistent with a forme fruste scaphocephaly that could not be otherwise recognized by clinical observation or CI. Given these findings, we propose the term occult scaphocephaly to describe this condition. The associated incidence of intracranial hypertension is unknown.

Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.

Deep learning strategies have become ubiquitous optimization tools for medical image analysis. With the appropriate amount of data, these approaches outperform classic methodologies in a variety of image processing tasks. However, rare diseases and pediatric imaging often lack extensive data. Specially, MRI are uncommon because they require sedation in young children. Moreover, the lack of standardization in MRI protocols introduces a strong variability between different datasets. In this paper, we present a general deep learning architecture for MRI homogenization that also provides the segmentation map of an anatomical region of interest. Homogenization is achieved using an unsupervised architecture based on variational autoencoder with cycle generative adversarial networks, which learns a common space (i.e. a representation of the optimal imaging protocol) using an unpaired image-to-image translation network. The segmentation is simultaneously generated by a supervised learning strategy. We evaluated our method segmenting the challenging anterior visual pathway using three brain T1-weighted MRI datasets (variable protocols and vendors). Our method significantly outperformed a non-homogenized multi-protocol U-Net.