Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, pituitary microadenoma
Motivation: The spatial resolution of MRI is still limited to the detection of pituitary microadenomas. CSAISR framework can reduce noise and improve image resolution.
Goal(s): To assess the image quality and pituitary microadenoma detection performance of the thin-slice MRI using CSAISR framework.
Approach: In this work, 1.5mm-CSAISR, 1.5mm-CSAI, 1.5mm-CS and 3mm-CSAISR images were obtained. These 1.5mm images were evaluated subjectively and objectively, and the detection rate of 1.5mm-CSAISR and 3mm-CSAISR were compared.
Results: Combined with subjective and objective evaluation, the image quality of 1.5mm-CSAISR images was the best. Meanwhile, the detection rate of 1.5mm-CSAISR reached 92.5%, which was significantly better than that of 3mm-CSAISR.
Impact: The results suggest that thin-slice MRI combined with CSAISR framework can balance the relationship between noise reduction and spatial resolution improvement, increase the detection rate of pituitary microadenoma and is meaningful for the diagnosis, follow-up and localization of this disease.
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