Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks

Christiane Posselt; Mehmet Yigit Avci; Mehmet Yigitsoy; Patrick Schuenke; Christoph Kolbitsch; Tobias Schaeffter; Stefanie Remmele

doi:10.1117/1.JMI.11.2.024013

24 April 2024 Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks

Christiane Posselt, Mehmet Yigit Avci, Mehmet Yigitsoy, Patrick Schuenke, Christoph Kolbitsch, Tobias Schaeffter, Stefanie Remmele

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 024013 (April 2024). https://doi.org/10.1117/1.JMI.11.2.024013

Abstract

Purpose

To provide a simulation framework for routine neuroimaging test data, which allows for “stress testing” of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols.

Approach

The approach simulates “acquisition shift derivatives” of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI).

Results

The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R2>0.9). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI.

Conclusions

We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Christiane Posselt, Mehmet Yigit Avci, Mehmet Yigitsoy, Patrick Schuenke, Christoph Kolbitsch, Tobias Schaeffter, and Stefanie Remmele "Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks," Journal of Medical Imaging 11(2), 024013 (24 April 2024). https://doi.org/10.1117/1.JMI.11.2.024013

Received: 10 August 2023; Accepted: 29 March 2024; Published: 24 April 2024

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Magnetic resonance imaging

Image segmentation

Data modeling

Photovoltaics

Tissues

Education and training

Artificial intelligence

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