Deep learning–based MR‐to‐CT synthesis: The influence of varying gradient echo–based MR images as input channels

Purpose To study the influence of gradient echo–based contrasts as input channels to a 3D patch‐based neural network trained for synthetic CT (sCT) generation in canine and human populations. Methods Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired u...

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Veröffentlicht in:Magnetic resonance in medicine Jg. 83; H. 4; S. 1429 - 1441
Hauptverfasser: Florkow, Mateusz C., Zijlstra, Frank, Willemsen, Koen, Maspero, Matteo, Berg, Cornelis A. T., Kerkmeijer, Linda G. W., Castelein, René M., Weinans, Harrie, Viergever, Max A., Stralen, Marijn, Seevinck, Peter R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.04.2020
John Wiley and Sons Inc
Schlagworte:
Animals
Bones
Channels
Computed tomography
Configurations
Deep Learning
Dogs
Errors
Full Papers—Computer Processing and Modeling
gradient echo
Humans
Image acquisition
Image Processing, Computer-Assisted
Image reconstruction
Machine learning
Magnetic Resonance Imaging
MR contrasts
Neural networks
Neural Networks, Computer
Replication
Reproducibility
Statistical analysis
synthetic CT
Tomography, X-Ray Computed
gradient echo
deep learning
synthetic CT
MR contrasts
ISSN:0740-3194, 1522-2594, 1522-2594
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Zusammenfassung:Purpose To study the influence of gradient echo–based contrasts as input channels to a 3D patch‐based neural network trained for synthetic CT (sCT) generation in canine and human populations. Methods Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in‐phase and opposed‐phase images were obtained from a single T1‐weighted multi‐echo gradient‐echo acquisition. From this set, 6 input configurations were defined, each containing 1 to 4 MR images regarded as input channels. For each configuration, a UNet‐derived deep learning model was trained for synthetic CT generation. Reconstructed Hounsfield unit maps were evaluated with peak SNR, mean absolute error, and mean error. Dice similarity coefficient and surface distance maps assessed the geometric fidelity of bones. Repeatability was estimated by replicating the training up to 10 times. Results Seventeen canines and 23 human subjects were included in the study. Performance and repeatability of single‐channel models were dependent on the TE‐related water–fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines. Conclusion Significant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In‐phase images outperformed opposed‐phase images, and Dixon reconstructed multichannel inputs outperformed single‐channel inputs.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.28008