A. Siddiqui | Searcharxiv

arXiv2025

Early stakeholder engagement for a possible new multipurpose research reactor for Canada

Canada has a rich history of nuclear technology development. Since the 1940s, nuclear research infrastructure and facilities, such as National Research Universal (NRU) reactor at Canadian Nuclear Laboratories in Chalk River, Ontario, have played a key role for R&D and for building Canadian expertise and competency in nuclear technology. The NRU reactor retired in 2018. Since the needs of stakeholders vary with time, consideration of a new multipurpose research reactor for Canada must contemplate current user input for their requirements. As an early step in such consideration, a systematic approach was employed to engage various national stakeholders from academia, industry, and research organizations, including the government. Several virtual workshops were held, each with a specific theme around utilizations. To provide international context, our workshops also included presentations from leading nuclear laboratories around the world. We provide a summary of Canada's approach and the main findings of this early stakeholder engagement.

physics.soc-ph↗

arXiv2020

Neurodevelopmental Age Estimation of Infants Using a 3D-Convolutional Neural Network Model based on Fusion MRI Sequences

The ability to determine if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size characteristics in determining if brain maturity matches the chronological age of the patient, this requires years of experience with pediatric neuroradiology. Due to the lack of standardized criteria, estimation of brain maturity before age three remains fraught with interobserver and intraobserver variability. An objective measure of brain developmental age estimation (BDAE) could be a useful tool in helping physicians identify developmental delay as well as other neurological diseases. We investigated a three-dimensional convolutional neural network (3D CNN) to rapidly classify brain developmental age using common MRI sequences. MRI datasets from normal newborns were obtained from the National Institute of Mental Health Data Archive from birth to 3 years. We developed a BDAE method using T1-weighted, as well as a fusion of T1-weighted, T2-weighted, and proton density (PD) sequences from 112 individual subjects using 3D CNN. We achieved a precision of 94.8% and a recall of 93.5% in utilizing multiple MRI sequences in determining BDAE.

eess.IV↗