Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets.
Antonio CanditoRichard HolbreyAna RibeiroChristina MessiouNina TunariuDow-Mu KohMatthew D BlackledgePublished in: Bioengineering (Basel, Switzerland) (2024)
Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.
Keyphrases
- contrast enhanced
- diffusion weighted
- diffusion weighted imaging
- deep learning
- magnetic resonance imaging
- magnetic resonance
- computed tomography
- high intensity
- air pollution
- convolutional neural network
- spinal cord
- randomized controlled trial
- case report
- rna seq
- machine learning
- optical coherence tomography
- dual energy
- spinal cord injury
- big data
- single cell