Joint Calibrationless Reconstruction and Segmentation of Parallel MRI
This work addresses the challenge of accurate brain region segmentation in clinical MRI applications by reducing artifacts from accelerated scans, though it is incremental as it builds on existing deep-learning methods for MRI reconstruction and segmentation.
The paper tackles the problem of image reconstruction artifacts in accelerated parallel MRI, which degrade segmentation accuracy for brain volume estimation, by introducing a joint deep-learning framework that improves both image quality and segmentation performance with a few-shot training strategy requiring only 10% of segmented data.
The volume estimation of brain regions from MRI data is a key problem in many clinical applications, where the acquisition of data at high spatial resolution is desirable. While parallel MRI and constrained image reconstruction algorithms can accelerate the scans, image reconstruction artifacts are inevitable, especially at high acceleration factors. We introduce a novel image domain deep-learning framework for calibrationless parallel MRI reconstruction, coupled with a segmentation network to improve image quality and to reduce the vulnerability of current segmentation algorithms to image artifacts resulting from acceleration. The combination of the proposed image domain deep calibrationless approach with the segmentation algorithm offers improved image quality, while increasing the accuracy of the segmentations. The novel architecture with an encoder shared between the reconstruction and segmentation tasks is seen to reduce the need for segmented training datasets. In particular, the proposed few-shot training strategy requires only 10% of segmented datasets to offer good performance.