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computational biomedical imaging research

fetal MRI segmentation, mesh rendering. doi:10.6084/m9.figshare.12950009


The imaging-lab facilitates high-performance computing, data management and reproducible research for computational biomedical imaging projects in the SickKids Research Institute supported by dedicated access to the Research‑IT High Performance Computing (RIT‑HPC) core facility.


  • provide systems for the collection, analysis and interpretation of imaging research data
  • enable scientific creativity and productivity
  • promote collaboration, open science and reproducible research

Funded by the Canadian Foundation for Innovation.


MRI recon server

Integration and utilization of research MRI reconstruction methods can be hindered by computation lag and ease of use. An MRI reconstruction server was implemented using the Yarra framework, utilizing RIT‑HPC resources for computationally-intensive processes.

  • rapid prototyping of new methods
  • scalable resource allocation
  • ease of use for clinically-focused researchers
  • automated end-to-end data transfer
  • systematic archiving of research data and output
  • enables computation of motion-corrected fetal cardiac MRI

deep learning MRI recon

Reconstruction of under-sampled paediatric phase contrast MRI using a cascade of convolutional neural networks (CNN) with interleaved data consistency (DC). Image reconstruction with CNNs is fast, compared to conventional iterative compressed sensing algorithms, and better-suited to clinical deployment.
[Macgowan Lab]

  • developed in python using SigPy and PyTorch libraries
  • network trained using GPU-enabled RIT‑HPC node

MEG analysis

A million different choices can be made in the process of cleaning and analyzing neural data. It is important for reproducibility that these choices are documented and setup in such a way that facilitates efficient analysis of many participants. An MEG processing pipeline was implemented using MATLAB and the fieldtrip toolbox, and takes advantage of the imaging-lab’s computational power to analyze data from many participants.
[Mabbot Lab]

  • single configuration file where users can set and document all choices (e.g., epoch interval, downsampling, channel repair)
  • progress logging through pipeline in case of crash

diffusion MRI

The Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline was used to minimize thermal noise, Gibbs ringing and EPI spatial distortions, to improve the accuracy and reproducibility of diffusion processing. This diffusion MRI pipeline was implemented using MATLAB, MRtrix and FSL to process large cohorts.
[Mabbot Lab]

  • single python script that performs complete diffusion MRI processing
  • computational power facilitates fast execution time and handling of high resolution data

AWSM-Q slide morphometry

Quantitative Automated Whole Slide Morphometry (AWSM-Q) segments images of histological samples and calculates morphological parameters to investigate rat models of lung disease.
[Santyr Lab]

  • Matlab-based histological analysis
  • enhanced processing capabilities allow for batches analyses
  • enables implementation of previously inaccessible methods

lung MRI quantification

Lung MRI analysis of 129Xe and 1H MRI images obtained from children with lung diseases for quantification of ventilation defect percentage.
[Santyr Lab]

  • Matlab-based co-registration and segmentation

4D flow MRI

Visualization and analysis of complex blood flow patterns using cvi42 on workstations optimized for graphics performance.


Operating system virtualization to create and run 'containers' that package up software with dependencies using the Singularity platform. Containers can be built to include all of the programs, libraries, data and scripts such that comprise an entire experiment for reuse and sharing.

new users

New users should arrange the following accounts:

Contact the imaging-lab to discuss new projects.
Consult the user wiki for reference information and guidance.

Note: some linked pages only accessible via SickKids network

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SickKids Research Institute
  PGCRL 08.9470