Keywords: Data Acquisition, Segmentation
Motivation: Accurate assessment of hippocampus volume is essential for early diagnosis, disease monitoring, understanding brain function, and guiding treatment decisions in a variety of neurological diseases, psychiatric conditions, and aging.
Goal(s): To assess conventional and proposed high-resolution deep learning-based sequences in order to enable an accurate segmentation of the hippocampal subfields using 3T MRI.
Approach: Utilization of a deep learning based high-resolution 2D proton density-weighted (hrPDW), conventional 3D T1-weighted and T2-weighted sequences for segmenting the hippocampus in Freesurfer.
Results: As compared to conventional sequences, the proposed hrPDW improves the hippocampal contrast and provides accurate hippocampal segmentation.
Impact: This study demonstrates that the deep learning based 2D high-resolution proton density weighted TSE sequence has a potential to reduce inaccuracies in hippocampus volumetry, which will ensure reliable diagnosis and monitoring of neurological and psychiatric conditions.
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