Abstract
PURPOSE: To assess the performance and feasibility of generative deep learning in enhancing the image quality of T2-weighted (T2W) prostate magnetic resonance imaging (MRI). METHODS: Axial T2W images from the prostate imaging: cancer artificial intelligence dataset (n = 1,476, biologically males; n = 1,500 scans) were used, partitioned into training (n = 1300), validation (n = 100), and testing (n = 100) sets. A Pix2Pix model was trained on original and synthetically degraded images, generated using operations such as motion, Gaussian noise, blur, ghosting, spikes, and bias field inhomogeneities to enhance image quality. The efficacy of the model was evaluated by seven radiologists using the prostate imaging quality criteria to assess original, degraded, and improved images. The evaluation also included tests to determine whether the images were original or synthetically improved. Additionally, the model's performance was tested on the in-house external testing dataset of 33 patients. The statistical significance was assessed using the Wilcoxon signedrank test. RESULTS: Results showed that synthetically improved images [median score (interquartile range) 4.71 (1)] were of higher quality than degraded images [3.36 (3), P = 0.0001], with no significant difference from original images [5 (1.14), P > 0.05]. Observers equally identified original and synthetically improved images as original (52% and 53%), proving the model's ability to retain realistic attributes. External testing on a dataset of 33 patients confirmed a significant improvement (P = 0.001) in image quality, from a median score of 4 (2.286)-4.71 (1.715). CONCLUSION: The Pix2Pix model, trained on synthetically degraded data, effectively improved prostate MRI image quality while maintaining realism and demonstrating both applicability to real data and generalizability across various datasets. CLINICAL SIGNIFICANCE: This study critically assesses the efficacy of the Pix2Pix generative-adversarial network in enhancing T2W prostate MRI quality, demonstrating its potential to produce high-quality, realistic images indistinguishable from originals, thereby potentially advancing radiology practice by improving diagnostic accuracy and image reliability.