Show abstract - PDF - Reviews

This work proposes MultiTask Learning for accelerated-MRI Reconstruction and Segmentation (MTLRS). Unlike the common single-task approaches, MultiTask Learning identifies relations between multiple tasks to improve the performance of all tasks. The proposed MTLRS consists of a unique cascading architecture, where a recurrent reconstruction network and a segmentation network inform each other through hidden states. The features of the two networks are shared and implicitly enforced as inductive bias. To evaluate the benefit of MTLRS, we compare performing the two tasks of accelerated-MRI reconstruction and MRI segmentation with pre-trained, sequential, end-to-end, and joint approaches. A synthetic multicoil dataset is used to train, validate, and test all approaches with five-fold cross-validation. The dataset consists of 3D FLAIR brain data of relapsing-remitting Multiple Sclerosis patients with known white matter lesions. The acquisition is prospectively undersampled by approximately 7.5 times compared to clinical standards. Reconstruction performance is evaluated by Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). Segmentation performance is evaluated by Dice score for combined brain tissue and white matter lesion segmentation and by per lesion Dice score. Results show that MTLRS outperforms other evaluated approaches, providing high-quality reconstructions and accurate white matter lesion segmentation. A significant correlation was found between the performance of both tasks (SSIM and per lesion Dice score, $\rho=0.92$, $p=0.0005$). Our proposed MTLRS demonstrates that accelerated-MRI reconstruction and MRI segmentation can be effectively combined to improve performance on both tasks, potentially benefiting clinical settings.
Hide abstract