The impact of MRI-CT registration errors on deep learning-based synthetic CT generation

Mateusz C. Florkow; Frank Zijlstra; Linda G. W. Kerkmeijer M.D.; Matteo Maspero; Cornelis A. T. van den Berg; Marijn van Stralen; Peter R. Seevinck

doi:10.1117/12.2512747

15 March 2019 The impact of MRI-CT registration errors on deep learning-based synthetic CT generation

Mateusz C. Florkow, Frank Zijlstra, Linda G. W. Kerkmeijer M.D., Matteo Maspero, Cornelis A. T. van den Berg, Marijn van Stralen, Peter R. Seevinck

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094938 (2019) https://doi.org/10.1117/12.2512747
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Purpose To investigate the impact of image registration on deep learning-based synthetic CT (sCT) generation. Methods Paired MR images and CT scans of the pelvic region of radiotherapy patients were obtained and non-rigidly registered. After a manual verification of the registrations, the dataset was split into two groups containing either well-registered or poorly-registered MR-CT pairs. In three scenarios, a patch-based U-Net deep learning architecture was trained for sCT generation on (i) exclusively well-registered data, (ii) mixtures of well-registered and poorly-registered data or on (iii) poorly-registered data only. Furthermore, a failure case was designed by introducing a single misregistered subject in the training set of six well-registered subjects. Reconstruction quality was assessed using mean absolute error (MAE) in the entire body and specifically in bone and Dice similarity coefficient (DSC) evaluated cortical bone geometric fidelity. Results The model trained on well registered data had an average MAE of 27.6 ± 2.6HU on the entire body contour and 79.1 ± 16.1HU on the bone. The average cortical bone DSC was 0.89. When patients with registration errors were added to the training, MAE’s were higher and DSC lower with variations by up to 36HU for the average MAE_bone. The failure mode demonstrated the potential far-reaching consequences of a single misregistered subject in the training set with variations by up to 38HU for MAE_bone. Conclusion Poor registration quality of the training set had a negative impact on paired, deep learning-based sCT generation. Notably, as low as one poorly-registered MR-CT pair in the training phase was capable of drastically altering a model.

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Mateusz C. Florkow, Frank Zijlstra, Linda G. W. Kerkmeijer M.D., Matteo Maspero, Cornelis A. T. van den Berg, Marijn van Stralen, and Peter R. Seevinck "The impact of MRI-CT registration errors on deep learning-based synthetic CT generation", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094938 (15 March 2019); https://doi.org/10.1117/12.2512747

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