Optimal acquisition sequence for AI-assisted brain tumor segmentation under the constraint of largest information gain per additional MRI sequence

Abstract

Purpose: Different imaging sequences (T1 etc.) depict different aspects of a brain tumor. As clinical MRI examinations of the brain might be terminated prematurely, not all sequences may be acquired, decreasing the performance of automated tumor segmentation. We attempt to optimize the order of sequences, to maximize information gain in case of incomplete examination. Methods: For segmentation we used the winner algorithm of the Brain Tumor Segmentation challenge 2018, trained on the BraTS 2020 dataset, with the objective to segment necrotic core, peritumoral edema, and enhancing tumor. We compared the segmentation performance for all combinations of sequences, using the Dice score (DS) as the primary metric. We compare the results with those which would be obtained by attempting to follow the consensus recommendations for brain tumor imaging [T1, FLAIR, T2, T1CE]. Results: The average segmentation accuracy varies between 0.476 for T1 only and 0.751 for the full set of sequences. T1CE has a high information content, even regarding peritumoral edema and information of T2 and FLAIR were highly redundant. The optimal order of sequences appears to be [T1, T2, T1CE, FLAIR]. Comparing segmentation accuracy after each fully acquired sequence, the first sequence (T1) is the same for both, DS for [T1, T2] (proposed) is 6.2% higher than [T1, FLAIR] (aborted recommendations), and [T1, T2, T1CE] (proposed) is 34.8% higher than [T1, FLAIR, T2] (aborted recommendations). Conclusion: For the purpose of optimal deep-learning-based segmentation purposes in potentially incomplete MRI examinations, the T1CE sequence should be acquired as early as possible.

Keywords

• Deep learning
• Tumor segmentation
• Glioma
• Magnetic resonance imaging
• Imaging protocol