Pituitary neuroendocrine tumor: evaluation with super resolution deep learning reconstruction

PURPOSE: To evaluate the impact of super-resolution deep learning reconstruction (SR-DLR) algorithm on the evaluations of pituitary neuroendocrine tumor (PitNET) and on the image quality of pituitary MRI compared to conventional images with zero-filling interpolation (ZIP) technique. METHODS: This retrospective study included 29 patients with PitNET who underwent pituitary MRI imaging. T2-weighted coronal images were reconstructed with SR-DLR and ZIP. Three readers assessed the images in terms of pituitary stalk deviation, noise, sharpness, depiction of PitNET, and diagnostic acceptability. A radiologist placed circular or ovoid regions of interest (ROIs) on the lateral ventricle and the tumor, and signal-to-noise ratio (SNR) and contrast-to-noise ratio were calculated. The radiologist also placed a linear ROI crossing the septum pellucidum perpendicularly. From the signal intensity profile along this ROI, edge rise slope (ERS) and full width at half maximum (FWHM) were calculated. RESULTS: Inter-reader agreement in the evaluations of pituitary stalk deviation in SR-DLR (0.518) tended to be superior to that in ZIP (0.405). Scores in the qualitative image analyses in SR-DLR were significantly better than those in ZIP for all evaluation items (p < 0.001). SNR and CNR in SR-DLR were significantly higher compared to ZIP (p < 0.001). Results of ERS (5433/2177 in SR-DLR/ZIP) and FWHM (0.67/1.27 mm in SR-DLR/ZIP) indicated significantly enhanced spatial resolution in SR-DLR compared to ZIP. CONCLUSION: SR-DLR tended to enhance inter-reader agreement in the evaluations of pituitary stalk deviation and significantly improved quality of pituitary MRI images compared to conventional ZIP images.