Noriko Nishioka, Noriyuki Fujima, Satonori Tsuneta, Masato Yoshikawa, Rina Kimura, Keita Sakamoto, Fumi Kato, Haruka Miyata, Hiroshi Kikuchi, Ryuji Matsumoto, Takashige Abe, Jihun Kwon, Masami Yoneyama, Kohsuke Kudo
European journal of radiology open 13 100588-100588 2024年12月
PURPOSE: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). METHODS: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. RESULTS: In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). CONCLUSION: Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.
