We demonstrate a deep learning-based workflow for intelligent slice placement (ISP) in MR knee imaging: meniscus plane, femoral condyle plane, tibial plane, sagittal plane and ACL plane, based on standard 2D tri-planar localizer images. We leveraged a previously described generalized architecture for ISP planning in brain, with only the training data and plane definitions adapted for knee. The mean absolute distance error between GT plane and predicted plane was < 0.5 mm for all planes except tibial plane (~ 1 mm). The results indicate the generalization of deep-learning ISP framework and its suitability for ISP in any anatomy of interest.
This abstract and the presentation materials are available to members only; a login is required.