Information

Related Research Units

Fetal Neonatal Neuroimaging and Developmental Science Center Research
Radiology Research

Research Overview

Dr. Pienaar is actively engaged with architecting novel approaches to computation in medicine – in particular image processing and AI. He leads a team within the Fetal Neonatal Neuroimaging and Developmental Science Center that has created an open source / open science platform called ChRIS that allows for advanced research and clinical computation to occur in the hybrid cloud. Dr Pienaar and team are passionate about open solutions to medicine and in actively bridging the gap between research computation and clinical practice to better inform patient care and outcomes.

Dr. Pienaar is also engaged in research endeavors examining the internal tractography of the human brain, and applying information processing theory to how the brain itself is organized.

Research Background

Dr. Pienaar completed a Doctoral Degree in Biomedical Engineering at the Cleveland Clinic Organization where he conducted research on using Artificial Intelligence to potentially help paraplegic patients stand up again through coordinate muscle stimulation.

Currently he is the Technical Director of the FNNDSC at BCH, where he overseas the compute infrastructure of the Center, as well as leads a team developing new and novel platforms to bringing complex computation to the clinical front lines.

Publications

  1. Publisher Correction to: Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex. J Autism Dev Disord. 2024 Mar; 54(3):1232. View Abstract
  2. Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex. J Autism Dev Disord. 2024 Aug; 54(8):3155-3175. View Abstract
  3. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Med Image Anal. 2021 08; 72:102091. View Abstract
  4. Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge. Med Image Anal. 2021 05; 70:101972. View Abstract
  5. BRAIN AGE ESTIMATION USING LSTM ON CHILDREN'S BRAIN MRI. Proc IEEE Int Symp Biomed Imaging. 2020 Apr; 2020:420-423. View Abstract
  6. Disorganized Patterns of Sulcal Position in Fetal Brains with Agenesis of Corpus Callosum. Cereb Cortex. 2018 09 01; 28(9):3192-3203. View Abstract
  7. Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization. Front Neuroinform. 2017; 11:32. View Abstract
  8. Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images. Neuroimage. 2015 Nov 15; 122:246-61. View Abstract
  9. Longitudinal changes in diffusion properties in white matter pathways of children with tuberous sclerosis complex. Pediatr Neurol. 2015 Jun; 52(6):615-23. View Abstract
  10. ChRIS--A web-based neuroimaging and informatics system for collecting, organizing, processing, visualizing and sharing of medical data. Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2015:206-9. View Abstract
  11. Quantification and discrimination of abnormal sulcal patterns in polymicrogyria. Cereb Cortex. 2013 Dec; 23(12):3007-15. View Abstract
  12. A quantitative method for correlating observations of decreased apparent diffusion coefficient with elevated cerebral blood perfusion in newborns presenting cerebral ischemic insults. Neuroimage. 2012 Nov 15; 63(3):1510-8. View Abstract
  13. Diffusion tensor analysis of pediatric multiple sclerosis and clinically isolated syndromes. AJNR Am J Neuroradiol. 2013 Feb; 34(2):417-23. View Abstract
  14. Regional infant brain development: an MRI-based morphometric analysis in 3 to 13 month olds. Cereb Cortex. 2013 Sep; 23(9):2100-17. View Abstract
  15. Intrinsic curvature: a marker of millimeter-scale tangential cortico-cortical connectivity? Int J Neural Syst. 2011 Oct; 21(5):351-66. View Abstract
  16. Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: a twin study. Neuroimage. 2011 Aug 01; 57(3):1077-86. View Abstract
  17. Assessment of the frequency-domain multi-distance method to evaluate the brain optical properties: Monte Carlo simulations from neonate to adult. Biomed Opt Express. 2011 Feb 11; 2(3):552-67. View Abstract
  18. White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci U S A. 2010 Nov 02; 107(44):19067-72. View Abstract
  19. Tract-based analysis of callosal, projection, and association pathways in pediatric patients with multiple sclerosis: a preliminary study. AJNR Am J Neuroradiol. 2010 Jan; 31(1):121-8. View Abstract
  20. A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT. Int J Imaging Syst Technol. 2008 Jun 01; 18(1):42-68. View Abstract
  21. Cortical surface shape analysis based on spherical wavelets. IEEE Trans Med Imaging. 2007 Apr; 26(4):582-97. View Abstract
  22. Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation. Conf Comput Vis Pattern Recognit Workshops. 2006 Jun; 2006. View Abstract

Contact Rudolph Pienaar

Phone: 857-218-5139
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