What question did this study set out to answer?

The aim is to enhance the reliability of cortical thickness measurements from MRI by reducing sensitivity to acquisition parameters.

July 5, 2026Open Access

Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL‐Based Cortical Thickness Estimates

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Randomized trial demonstrates robust cortical thickness estimates in RRMS patients, suggesting improved clinical evaluations.

The aim is to enhance the reliability of cortical thickness measurements from MRI by reducing sensitivity to acquisition parameters.
Fine-tuned a deep-learning segmentation tool (DL+DiReCT) using simulated MPRAGE images.
Evaluated performance on synthetic atrophy datasets and in relapsing–remitting multiple sclerosis patients.
Controlled for covariates including age, sex, field strength, and EDSS score.
Contrast sensitivity reduced from -0.644 to 0.094, indicating significant improvement.
The model accurately replicated atrophy trends, outperforming FreeSurfer and SynthSeg.
Maintained stable performance across demographic variables, confirming its robustness.

Blattner et al. (2026) studied this question.

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