Breaking Speed Limits with Simultaneous Ultra-Fast MRI Reconstruction and Tissue Segmentation | AISC

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Published on ● Video Link: https://www.youtube.com/watch?v=vFrsIdr6Vl8


Duration: 49:09
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For slides and more information on the paper, visit https://ai.science/e/breaking-speed-limits-with-simultaneous-ultra-fast-mri-reconstruction-and-tissue-segmentation--ce6Eghj0gfg1O3kgAFiL

Speaker: Francesco Caliva; Discussion Facilitator: Akram Bayat

Motivation:
Magnetic Resonance Image (MRI) acquisition, reconstruction and tissue segmentation are
usually considered separate problems. This can be limiting when it comes to rapidly extracting relevant clinical parameters. In many applications, availability of reconstructed
images with high fidelity may not be a priority as long as biomarker extraction is reliable and feasible. Built upon this concept, we demonstrate that it is possible to perform
tissue segmentation directly from highly undersampled k-space and obtain quality results
comparable to those in fully-sampled scenarios. We propose ‘TB-recon’, a 3D task-based
reconstruction framework. TB-recon simultaneously reconstructs MRIs from raw data and
segments tissues of interest. To do so, we devised a network architecture with a shared encoding path and two task-related decoders where features flow among tasks. We deployed
TB-recon on a set of (up to 24×) retrospectively undersampled MRIs from the Osteoarthritis Initiative dataset, where we automatically segmented knee cartilage and menisci. An experimental study was conducted showing the superior performance of the proposed method
over a combination of a standard MRI reconstruction and segmentation method, as well as
alternative deep learning based solutions. In addition, our ablation study highlighted the
importance of skip connections among the decoders for the segmentation task. Ultimately,
we conducted a reader study, where two musculoskeletal radiologists assessed the proposed
model’s reconstruction performance



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