Shuo Zhang, Meimeng Zhong, Hanxu Shenliu, Nan Wang, Shuai Hu, Xulun Lu, Liangjie Lin, Haonan Zhang, Yan Zhao, Chao Yang, Hongbo Feng, Qingwei Song
{"title":"基于深度学习的脑弥散加权MRI欠采样超分辨率重建：与常规迭代重建的比较。","authors":"Shuo Zhang, Meimeng Zhong, Hanxu Shenliu, Nan Wang, Shuai Hu, Xulun Lu, Liangjie Lin, Haonan Zhang, Yan Zhao, Chao Yang, Hongbo Feng, Qingwei Song","doi":"10.3174/ajnr.A8482","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.</p><p><strong>Materials and methods: </strong>This retrospective study enrolled 114 consecutive participants who underwent brain DWI. The DWI images were reconstructed with 2 schemes: 1) DL-based super-resolution reconstruction (DWI<sub>DL</sub>); and 2) conventional compressed sensing reconstruction (DWI<sub>CS</sub>). Qualitative image analysis included overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke of different lesion sizes. Quantitative image quality assessments were performed by measurements of SNR, contrast-to-noise ratio (CNR), ADC, and edge rise distance. Group comparisons were conducted by using a paired <i>t</i> test for normally distributed data and the Wilcoxon test for non-normally distributed data. The overall agreement between readers for qualitative ratings was assessed by using the Cohen <i>κ</i> coefficient. A <i>P</i> value less than .05 was considered statistically significant.</p><p><strong>Results: </strong>A total of 114 DWI examinations constituted the study cohort. For the qualitative assessment, overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke lesions (lesion size <1.5 cm) improved by DWI<sub>DL</sub> compared with DWI<sub>CS</sub> (all <i>P</i> < .001). For the quantitative analysis, edge rise distance of DWI<sub>DL</sub> was reduced compared with that of DWI<sub>CS</sub> (<i>P</i> < .001), and no significant difference in SNR, CNR, and ADC values (all <i>P</i> > .05).</p><p><strong>Conclusions: </strong>Compared with the conventional compressed sensing reconstruction, the DL-based super-resolution reconstruction demonstrated superior image quality and was feasible for achieving higher diagnostic confidence in infarction stroke.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":"46 1","pages":"41-48"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735436/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.\",\"authors\":\"Shuo Zhang, Meimeng Zhong, Hanxu Shenliu, Nan Wang, Shuai Hu, Xulun Lu, Liangjie Lin, Haonan Zhang, Yan Zhao, Chao Yang, Hongbo Feng, Qingwei Song\",\"doi\":\"10.3174/ajnr.A8482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and purpose: </strong>DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.</p><p><strong>Materials and methods: </strong>This retrospective study enrolled 114 consecutive participants who underwent brain DWI. The DWI images were reconstructed with 2 schemes: 1) DL-based super-resolution reconstruction (DWI<sub>DL</sub>); and 2) conventional compressed sensing reconstruction (DWI<sub>CS</sub>). Qualitative image analysis included overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke of different lesion sizes. Quantitative image quality assessments were performed by measurements of SNR, contrast-to-noise ratio (CNR), ADC, and edge rise distance. Group comparisons were conducted by using a paired <i>t</i> test for normally distributed data and the Wilcoxon test for non-normally distributed data. The overall agreement between readers for qualitative ratings was assessed by using the Cohen <i>κ</i> coefficient. A <i>P</i> value less than .05 was considered statistically significant.</p><p><strong>Results: </strong>A total of 114 DWI examinations constituted the study cohort. For the qualitative assessment, overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke lesions (lesion size <1.5 cm) improved by DWI<sub>DL</sub> compared with DWI<sub>CS</sub> (all <i>P</i> < .001). For the quantitative analysis, edge rise distance of DWI<sub>DL</sub> was reduced compared with that of DWI<sub>CS</sub> (<i>P</i> < .001), and no significant difference in SNR, CNR, and ADC values (all <i>P</i> > .05).</p><p><strong>Conclusions: </strong>Compared with the conventional compressed sensing reconstruction, the DL-based super-resolution reconstruction demonstrated superior image quality and was feasible for achieving higher diagnostic confidence in infarction stroke.</p>\",\"PeriodicalId\":93863,\"journal\":{\"name\":\"AJNR. American journal of neuroradiology\",\"volume\":\"46 1\",\"pages\":\"41-48\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735436/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AJNR. 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Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.
Background and purpose: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.
Materials and methods: This retrospective study enrolled 114 consecutive participants who underwent brain DWI. The DWI images were reconstructed with 2 schemes: 1) DL-based super-resolution reconstruction (DWIDL); and 2) conventional compressed sensing reconstruction (DWICS). Qualitative image analysis included overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke of different lesion sizes. Quantitative image quality assessments were performed by measurements of SNR, contrast-to-noise ratio (CNR), ADC, and edge rise distance. Group comparisons were conducted by using a paired t test for normally distributed data and the Wilcoxon test for non-normally distributed data. The overall agreement between readers for qualitative ratings was assessed by using the Cohen κ coefficient. A P value less than .05 was considered statistically significant.
Results: A total of 114 DWI examinations constituted the study cohort. For the qualitative assessment, overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke lesions (lesion size <1.5 cm) improved by DWIDL compared with DWICS (all P < .001). For the quantitative analysis, edge rise distance of DWIDL was reduced compared with that of DWICS (P < .001), and no significant difference in SNR, CNR, and ADC values (all P > .05).
Conclusions: Compared with the conventional compressed sensing reconstruction, the DL-based super-resolution reconstruction demonstrated superior image quality and was feasible for achieving higher diagnostic confidence in infarction stroke.
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