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Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing
https://repo.qst.go.jp/records/85089
https://repo.qst.go.jp/records/850891c103c16-49d1-4334-968c-99dff9e66c52
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2022-01-11 | |||||
| タイトル | ||||||
| タイトル | Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Ueki, Wataru
× Ueki, Wataru× Nishii, Tatsuya× Kensuke, Umehara× Junko, Ota× Higuchi, Satoshi× Ohta, Yasutoshi× Nagai, Yasuhiro× Murakawa, Keizo× Ishida, Takayuki× Fukuda, Tetsuya× Kensuke, Umehara× Junko, Ota |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Background It is unclear whether deep-learning–based super-resolution technology (SR) or compressed sensing technology (CS) can accelerate magnetic resonance imaging (MRI) . Purpose To compare SR accelerated images with CS images regarding the image similarity to reference 2D- and 3D gradient-echo sequence (GRE) brain MRI. Material and Methods We prospectively acquired 1.3× and 2.0× faster 2D and 3D GRE images of 20 volunteers from the reference time by reducing the matrix size or increasing the CS factor. For SR, we trained the generative adversarial network (GAN), upscaling the low-resolution images to the reference images with twofold cross-validation. We compared the structural similarity (SSIM) index of accelerated images to the reference image. The rate of incorrect answers of a radiologist discriminating faster and reference image was used as a subjective image similarity (ISM) index. Results The SR demonstrated significantly higher SSIM than the CS (SSIM=0.9993–0.999 vs. 0.9947–0.9986; P < 0.001). In 2D GRE, it was challenging to discriminate the SR image from the reference image, compared to the CS (ISM index 40% vs. 17.5% in 1.3×; P = 0.039 and 17.5% vs. 2.5% in 2.0×; P = 0.034). In 3D GRE, the CS revealed a significantly higher ISM index than the SR (22.5% vs. 2.5%; P = 0.011) in 2.0 × faster images. However, the ISM index was identical for the 2.0× CS and 1.3× SR (22.5% vs. 27.5%; P = 0.62) with comparable time costs. Conclusion The GAN-based SR outperformed CS in image similarity with 2D GRE for MRI acceleration. In addition, CS was more advantageous in 3D GRE than SR. |
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| 書誌情報 |
Acta Radiologica 発行日 2022-02 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0284-1851 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 35118883 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1177/02841851221076330 | |||||
