Value of deep learning-based reconstruction in optimizing the image quality of focus-field-of-view diffusion weighted imaging for the prostate and its influence on the distribution characteristics of apparent diffusion coefficient

Wan Lu; Zhang Zhiwei; Chen Yi; He Chongyang; Yu Renqiang; Zhang Yanhua; Yu Bin

doi:10.13406/j.cnki.cyxb.003931

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Value of deep learning-based reconstruction in optimizing the image quality of focus-field-of-view diffusion weighted imaging for the prostate and its influence on the distribution characteristics of apparent diffusion coefficient

Clinical research | 更新时间：2026-10-26

- Value of deep learning-based reconstruction in optimizing the image quality of focus-field-of-view diffusion weighted imaging for the prostate and its influence on the distribution characteristics of apparent diffusion coefficient
- Journal of Chongqing Medical University Vol. 51, Issue 10, Pages: 1513-1520(2026)
- 作者机构：
  
  1.重庆市红十字会医院/江北区人民医院放射科，重庆 400020
  2.重庆医科大学附属第一医院放射科，重庆 400016
  3.达州市中心医院放射科，达州 635000
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.003931
  CLC： R114
- Received：13 April 2025，
  
  Revised：2025-07-13，
  
  Accepted：12 August 2025，
  
  Published：28 October 2026
- 稿件说明：
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万露,张志伟,陈翼等.深度学习重建优化前列腺小视野DWI图像质量及其对表观扩散系数分布特性的影响研究[J].重庆医科大学学报,2026,51(10):1513-1520.

Wan Lu,Zhang Zhiwei,Chen Yi,et al.Value of deep learning-based reconstruction in optimizing the image quality of focus-field-of-view diffusion weighted imaging for the prostate and its influence on the distribution characteristics of apparent diffusion coefficient[J].Journal of Chongqing Medical University,2026,51(10):1513-1520.
万露,张志伟,陈翼等.深度学习重建优化前列腺小视野DWI图像质量及其对表观扩散系数分布特性的影响研究[J].重庆医科大学学报,2026,51(10):1513-1520. DOI： 10.13406/j.cnki.cyxb.003931.

Wan Lu,Zhang Zhiwei,Chen Yi,et al.Value of deep learning-based reconstruction in optimizing the image quality of focus-field-of-view diffusion weighted imaging for the prostate and its influence on the distribution characteristics of apparent diffusion coefficient[J].Journal of Chongqing Medical University,2026,51(10):1513-1520. DOI： 10.13406/j.cnki.cyxb.003931.

摘要

目的

探讨深度学习重建（deep learning-reconstruction，DLR）优化前列腺小视野DWI（focus field-of-view diffusion-weighted imaging，FDWI）图像质量及其对表观扩散系数（apparent diffusion coefficients，ADC）分布特性的影响研究。

方法

回顾性纳入60例前列腺磁共振检查的患者影像学资料，对比DLR、常规重建FDWI病变区域、正常区域图像的信噪比（signal-to-noise ratio，SNR）、对比噪声比（contrast-to-noise ratio，CNR）和ADC。让2位医生采用双盲法对2种重建FDWI序列的噪声、磁敏感伪影和整体图像质量分别使用5分法评分，对比2位医生评分一致性；将ADC值高阶统计量偏度与峰度进行统计分析，验证其提升图像质量的同时保护数据分布完整性的技术优势。

结果

DL-FDWI序列病变区域和正常区域的SNR：［17.49（11.13，22.54）、17.34（11.44，26.56）］及CNR：［10.86（8.06，15.70）、9.32（5.82，13.11）］均明显高于常规-FDWI序列SNR：［12.88（10.56，18.14）、14.59（9.65，19.27）］及CNR：［8.87（7.11，11.64）、7.82（5.00，11.41）］，差异均具有统计学意义（

0.001）。2位医生在噪声和整体图像质量方面DL-FDWI评分均高于常规-FDWI，差异均有统计学意义（

0.001），在磁敏感伪影方面DL-FDWI序列和常规-FDWI图像评分（

=0.637和

=0.655）差异无统计学意义。2位医生噪声评分Kappa值为0.924，强相关性；磁敏感伪影和整体图像质量评分Kappa值分别为0.778，0.736，有相关性。ADC值（×10⁻³ mm²/s）：病变区域：DL［0.86（0.69，1.06）］ vs. 常规［0.86（0.69，1.06）］；正常区域：DL［1.52（1.40，1.71）］ vs. 常规［1.52（1.39，1.71）］，偏度：病变区域：DL［-0.04（-0.67，0.53）］ vs. 常规［-0.06（-0.35，0.26）］；正常区域：DL［-0.03（-0.24，0.30）］ vs. 常规［-0.17（-0.47，0.34）］，峰度：病变区域：DL［-0.52（-0.82，0.25）］ vs. 常规［-0.44（-0.83，0.12）］；正常区域：DL［-0.19±0.79］ vs. 常规［-0.23±0.78］，DL-FDWI与常规-FDWI图像在以上测量结果中差异均无统计学意义（

0.05

）。

结论

DLR在不改变ADC值分布特性的前提下，明显提升FDWI图像SNR与CNR，其“提质保真”特性为基于ADC值分布的前列腺癌异质性分析提供了可靠的影像学基础。

Abstract

Objective

To investigate the value of deep learning-based reconstruction（DLR） in optimizing the image quality of focus-field-of-view diffusion weighted imaging（FDWI） of the prostate and its influence on the distribution characteristics of apparent diffusion coefficients（ADC）.

Methods

A retrospective analysis was performed for the imaging data of 60 patients undergoing prostate MRI，and signal-to-noise ratio（SNR），contrast-to-noise ratio（CNR），and ADC of the lesions and normal regions were compared between DLR-optimized FDWI（DL-FDWI） and conventionally reconstructed（FDWI）. Based on a double-blind design，two radiologists independently determined the scores of noise，susceptibility artifacts，and overall image quality of the two reconstructed FDWI sequences using a 5-point scoring system，and inter-rater agreement was assessed. Higher-order statistical metrics（skewness and kurtosis） of ADC were analyzed to validate the technical advantage of DL-FDWI in protecting data integrity while improving image quality.

Results

For both lesion and normal regions，the SNR［17.49（11.13，22.54） and 17.34（11.44，26.56）］ and CNR［10.86（8.06，15.70） and 9.32（5.82，13.11）］ of the DL-FDWI sequences were significantly higher than those of the conventional-FDWI sequences［SNR：12.88（10.56，18.14） and 14.59（9.65，19.27）；CNR： 8.87（7.11，11.64） and 7.82（5.00，11.41）］，with all differences being statistically significant（

0.001）.Both radiologists assigned significantly higher scores to the DL-FDWI images compared to conventional-FDWI images for noise and overall image quality（

0.001 for both）. However，there were no statistically significant differences in the scores for susceptibility artifacts between the DL-FDWI and conventional-FDWI images（

=0.637 and

=0.655 for the two radiologists，respectively）.Inter-reader agreement，assessed by Cohen’s kappa coefficient，was almost perfect for noise scores（k=0.924）. Agreement for susceptibility artifact scores and overall image quality scores was substantial，with kappa values of 0.778 and 0.736，respectively.ADC values（×10⁻³ mm²/s）：Lesion region： DL［0.86（0.69，1.06）］ vs conventional ［0.86（0.69，1.06）］，Normal region： DL［1.52（1.40，1.71）］ vs conventional ［1.52（1.39，1.71）］，Skewness：Lesion region：DL［-0.04（-0.67，0.53）］ vs conventional［-0.06（-0.35，0.26）］，Normal region： DL ［-0.03（-0.24，0.30）］ vs conventional ［-0.17（-0.47，0.34）］，Kurtosis：Lesion region： DL［-0.52（-0.82，0.25）］ vs conventional ［-0.44（-0.83，0.12）］，Normal region： DL ［-0.19±0.79］ vs conventional ［-0.23±0.78］.No statistically significant differences（

0.05） were observed in any of these ADC values，skewness，or kurtosis measurements between DL-FDWI and conventional-FDWI images.

Conclusion

DLR significantly enhances the SNR and CNR of FDWI images without altering the distribution characteristics of ADC，and its features of “quality enhancement and fidelity preservation” provide a reliable foundation for the ADC-based heterogeneity analysis of prostate cancer.

关键词

Keywords

references

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