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Congratulations to Professor Pew-Thian Yap, PhD, on securing his 4th RO1 grant for 2024! This is an outstanding achievement and a testament to his unparalleled dedication and expertise. Dr. Yap continues to excel and set the bar even higher with his remarkable contributions. We are genuinely in awe of his continued success!

See details below on his latest grant awards.

 

Title: Optimized High-Resolution Fast Magnetic Resonance Fingerprinting with Cloud-Based Reconstruction
Grant #: 1R01NS134849-01A1
PI: MPI: Pew-Thian Yap (Contact); Yong Chen
Sponsor: NIH – NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
Project Dates: 09/12/2024-06/30/2029
Budget: $3,089,694

Abstract: Magnetic resonance imaging (MRI), despite its wide utility, is inherently limited due to its inability to measure tissue properties quantitatively, which is critical for objective and scanner-independent diagnosis and treatment monitoring. MR Fingerprinting (MRF) is a relatively new quantitative MRI framework for simultaneous quantification of multiple tissue properties. While MRF outperforms most conventional methods in quantitative imaging, existing MRF techniques are still handicapped by limited spatial resolution and coverage, long acquisition times, suboptimal acquisition parameters, long data reconstruction times, and complicated post-processing workflows, hindering large-scale clinical validation and translation.

In this project, we will leverage the expertise of our team in MRF, machine learning, and pulse sequence optimization to develop and optimize a rapid and robust quantitative MR technique, applicable to high-resolution volumetric brain imaging. Our team has recently developed a new B1-insensitive MRF method using low flip angles and multiple magnetization preparations for improved accuracy and precision in tissue quantification compared with existing MRF methods. We will first develop and optimize this new MRF method for 3D high-resolution brain imaging, using our newly developed pulse sequence design framework. Novel fat navigator will be incorporated to improve motion robustness (Aim 1). We will leverage state-of-the-art deep learning techniques to accelerate both acquisition and post-processing (Aim 2). Finally, a complete MRF post-processing pipeline empowered by GPU cloud computing will be developed to significantly simplify the post-processing workflow and facilitate efficient clinical translation and validation of the proposed methods for patients with neurological diseases (Aim 3).

 

Title: Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays
Grant #: 1R01HD112923-01A1
PI: MPI: Dan Ma (Contact PI); Deanne Wilson-Costello; Pew-Thian Yap
Sponsor: Case Western University NIH – EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
Project Dates: 08/16/2024-04/30/2029
UNC-CH Budget:  $827,587

Abstract:  Abstract Neuroimaging of infants and young children is increasingly used to monitor brain development that can ultimately influence long-term health and behavioral outcomes. Our research team is one of four centers participating in the Outcomes of Babies with Opioid Exposure (OBOE) study, a national effort to assess the effects of antenatal opioid exposure on baby development. These babies have neonatal opioid withdrawal symptoms at birth and struggle to maintain sleep or stillness during the MR scan. This patient population, and other pediatric patients in general, stress significant unmet needs for motion robust and quantitative imaging techniques for baby developmental assessment. However, there are unique challenges to imaging non-sedated babies using MRI, including high failure rate (no usable MRI data) due to motion, lack of quantitative and sensitive image markers for developmental assessment, and lack of imaging analysis tools specific for fast-evolving baby brains. In this proposal, we have established a multi-PI team, including MR Fingerprinting imaging developers (CWRU), baby imaging analysis and AI experts (UNC) and high-risk neonate clinical experts (UH) to address the unmet needs for baby imaging, and use the imaging tools to assess developmental delays of the opioid- exposed babies. We will achieve our goal with the following aims: Aim 1: Develop a motion-robust and comprehensive MRF scan to provide multiple quantitative tissue property maps for non-sedated babies; Aim 2: Develop baby-centric image processing tools and derive quantitative image features to characterize whole brain tissue changes; and Aim 3: Quantitatively assess developmental changes in opioid-exposed babies and predict the risk of developmental delays. This project will provide an imaging tool to relate quantitative features in brain structure and development to neurologic functions, opening the opportunity for early targeted interventions aimed at improving outcomes. The high quality, fast and motion robust MRF scans will have a broad impact for pediatric patients on improving scan success rate and reducing the sedation rate. The quantitative and comprehensive MRF scans will also have great potentials in assessing longitudinal changes regarding development alternations and therapeutic response.