Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction

Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction

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...

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Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:European Journal of Radiology Open
Subjects:
Prostate
DWI
Deep learning reconstruction
Image quality
Online Access:http://www.sciencedirect.com/science/article/pii/S2352047724000431
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Summary: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.
ISSN:2352-0477

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