Automatic spine station identification from surface coil sensitivity maps of MR imaging using deep learning

Motivation: In spine scanning with MRI, multiple localizer scans are acquired to manually set the stations. 3D surface coil sensitivity maps, with low-resolution but large FOV, which are acquired as part of the prescan can potentially be used to automatically determine the station boundaries. Goal(s): Utilize the existing information in the MRI scanner to automatically predict the location of spine stations and thereby accelerate the workflow. Approach: Use a deep learning framework to automatically identify the stations of the spine anatomy from the coil sensitivity maps. Results: The deep learning model shows good localization of spine stations with mean centroid errors less than 15mm. Impact: Spine stations can be identified from large FOV, low-resolution surface coil sensitivity maps in MRIs using our deep learning framework, which can be used for fast and automatic spine anatomical planning and imaging.