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Funding

Current Funding

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NIH R42 HD081712-02
“Image-Guided Planning System for Skull Correction in Children with Craniosynostosis”
The goal of this project is to develop personalized preoperative planning for cranial remodeling for infants with craniosynostosis via image and shape analysis to allow for decreased operative time and morbidity.

NIH NHLBI R41 HL145669
“Imaging Biomarkers of Severe Respiratory Infections in Premature Infants”
The goal of this project is to develop and evaluate a quantitative imaging technology to assess the risk for severe respiratory disease in premature babies using non-invasive low-radiation X-ray imaging.

The Royal Society IES\R3\170219
"Multi-Organ Physiology-Adaptive Shape Model of the Abdomen”
This project aims to develop a spatio-temporal multi-organ deformable model of the abdomen, combining advanced techniques for multi-organ statistical analysis with deep learning-based techniques.

NIH NCATS UL1 TR001876/KL2 TR001877
“Quantitative Facial Morphology in Pediatric Populations in the Democratic Republic of the Congo”
The aims of this project are to collect normative and syndromic data to quantify and analyze normative facial feature, and to determine discriminant facial features of Down syndrome in DRC population.

NIH NCATS UL1 TR001876/KL2 TR001877
“Reduced Dosage Acquisition of Computed Tomography Imaging for Pediatric Metastatic Diseases”
We propose to develop a novel deep learning approach to obtain diagnostic-quality CT images that are adequate for the early detection of pulmonary nodules using low radiation dosage acquisition protocols.

Philips Healthcare Award
“Ultrasound Quantification of Pediatric Hydronephrosis"
The goal of this study is to characterize pediatric hydronephrosis more precisely and without radiation by developing quantitative, robust and reproducible ultrasound-based techniques to evaluate hydronephrosis.

Gilbert Family Neurofibromatosis Institute
"Eliminating Vision Loss and Imaging"
The goal of this project is to develop and validate volumetric quantitative imaging markers of risk stratification of vision loss in children with neurofibromatosis and optic pathway gliomas.

Research Advisory Council Grant/Sheikh Zayed Institute
“Quantitative Imaging Program”
The Quantitative Imaging Program aims to develop and validate quantitative image-based software tools that can assess disease, design personalized therapies and anticipate risk factors in the lives of children.

Past Funding (selected)

Pediatric Innovation Fund/Children’s Hospital Foundation
mGene – Early and Mobile Detection of Genetic Syndromes”
The project will develop and validate mobile technology for facial analysis for the early and non-invasive screening of dysmorphic genetic syndromes.

NIH 1R41HD081712
“Image-Guided Planning System for Skull Correction in Children with Craniosynostosis”
The goal of this project is to develop personalized preoperative planning for cranial remodeling for infants with craniosynostosis via image and shape analysis to allow for decreased operative time and morbidity.

Joseph E, Roberts, Jr. Award/Center for Surgical Care
“Early Detection of Ureteropelvic Junction Obstruction using Signal Analysis and Machine Learning”
The project will improve the interpretation of dynamic information embedded in the renal drainage curves trough developing signal processing and machine learning methods to assess disease severity.

NIH ZIA CL040004
“Computer Aided Detection for Radiological Images”
The purpose of this project is to develop computer-aided diagnosis/detection for a variety of radiologic images and disease types using existing NIH CT scan images, including multi-organ segmentation of abdominal CT.

KUIRF Award#1569969527, UAE
"Computer-Aided Diagnosis System for Early Detection of Infantile Dysmorphic Syndromes in the UAE”
The project aims to design and develop a simple and practical framework for the early and non-invasive detection of dysmorphic syndromes in newborns by novel imaging, genetic and data mining technologies.

NIH UL1TR000075/KL2TR000076
“Molecular, Clinical and Imaging Biomarkers of Severity of Viral Respiratory Illness in Children”
The project aims to develop molecular, clinical and computational imaging techniques that can identify trans-disciplinary biomarkers that predict severity during viral respiratory infections in children.