Objective This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning–based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas. Materials and methods This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning–based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests. Results The 1.5-mm DLCS-SR images (κ = 0.746–0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585–0.687), 1.5-mm routine (κ = 0.449–0.487), and 3-mm DLCS-SR images (κ = 0.347–0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89–0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83–0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76–0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72–0.74; p < 0.001). Conclusion The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas? <Emphasis Type="BoldItalic">Findings</Emphasis> Deep learning–based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning–based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management. Graphical Abstract

Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning–based compressed sensing and super-resolution reconstruction

期刊论文
发表日期：

2025年4月13日

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Meng Zhang,
Chunchao Xia,
Jing Tang,
Li Yao,
Na Hu,
Jiaqi Li,
Wanlin Peng,
Sixian Hu,
Zheng Ye,
Xiaoyong Zhang,
Jin Huang,
Zhenlin Li

摘要

Objective

This study aims to assess diagnostic performance of high-resolution dynamic contrast-enhanced (DCE) MRI with deep learning–based compressed sensing and super-resolution (DLCS-SR) reconstruction for identifying microadenomas.

Materials and methods

This prospective study included 126 participants with suspected pituitary microadenomas who underwent DCE MRI between June 2023 and January 2024. Four image groups were derived from single-scan DCE MRI, which included 1.5-mm slice thickness images using DLCS-SR (1.5-mm DLCS-SR images), 1.5-mm slice thickness images with deep learning–based compressed sensing reconstruction (1.5-mm DLCS images), 1.5-mm routine images, and 3-mm slice thickness images using DLCS-SR (3-mm DLCS-SR images). Diagnostic criteria were established by incorporating laboratory findings, clinical symptoms, medical histories, previous imaging, and certain pathologic reports. Two readers assessed the diagnostic performance in identifying pituitary abnormalities and microadenomas. Diagnostic agreements were assessed using κ statistics, and intergroup comparisons for microadenoma detection were performed using the DeLong and McNemar tests.

Results

The 1.5-mm DLCS-SR images (κ = 0.746–0.848) exhibited superior diagnostic agreement, outperforming 1.5-mm DLCS (κ = 0.585–0.687), 1.5-mm routine (κ = 0.449–0.487), and 3-mm DLCS-SR images (κ = 0.347–0.369) (p < 0.001 for all). Additionally, the performance of 1.5-mm DLCS-SR images in identifying microadenomas [area under the receiver operating characteristic curve (AUC), 0.89–0.94] surpassed that of 1.5-mm DLCS (AUC, 0.83–0.87; p = 0.042 and 0.011, respectively), 1.5-mm routine (AUC, 0.76–0.78; p < 0.001), and 3-mm DLCS-SR images (AUC, 0.72–0.74; p < 0.001).

Conclusion

The findings revealed superior diagnostic performance of 1.5-mm DLCS-SR images in identifying pituitary abnormalities and microadenomas, indicating the clinical-potential of high-resolution DCE MRI.

Key Points

Question What strategies can overcome the resolution limitations of conventional dynamic contrast-enhanced (DCE) MRI, and which contribute to a high false-negative rate in diagnosing pituitary microadenomas?

Findings Deep learning–based compressed sensing and super-resolution reconstruction applied to DCE MRI achieved high resolution while improving image quality and diagnostic efficacy.

Clinical relevance DCE MRI with a 1.5-mm slice thickness and high in-plane resolution, utilizing deep learning–based compressed sensing and super-resolution reconstruction, significantly enhances diagnostic accuracy for pituitary abnormalities and microadenomas, enabling timely and effective patient management.

Graphical Abstract