| Title: | Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control |
| Journal: | J Cardiovasc Magn Reson |
| Published: | 20 Aug 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32814579/ |
| DOI: | https://doi.org/10.1186/s12968-020-00650-y |
Publication 3293
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Abstract
Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T1 mapping at 1.5 T using a Probabilistic Hierarchical Segmentation (PHiSeg) network (PHCUMIS 119 127, 2019). In addition, we use the uncertainty information provided by the PHiSeg network in a novel automated quality control (QC) step to identify uncertain T1 values. The PHiSeg network and QC were validated against manual analysis on a cohort of the UK Biobank containing healthy subjects and chronic cardiomyopathy patients (N=100 for the PHiSeg network and N=700 for the QC). We used the proposed method to obtain reference T1 ranges for the left ventricular (LV) myocardium in healthy subjects as well as common clinical cardiac conditions.1 Author
- Puyol-Antón E et al.
1 Application
| Application ID | Title |
|---|---|
| 17806 | Motion-based diagnosis and characterisation of heart disease |
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