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基于深度学习的垂体高分辨率 3D MRI 在垂体腺瘤围手术期评估中的应用价值。

Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.

机构信息

Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan.

Department of Radiology, University of Occupational and Environmental Health, School of Medicine, Kitakyushu, Japan.

出版信息

Neuroradiology. 2024 Jun;66(6):937-945. doi: 10.1007/s00234-024-03315-0. Epub 2024 Feb 19.

DOI:10.1007/s00234-024-03315-0
PMID:38374411
Abstract

PURPOSE

To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction of pituitary adenoma and parasellar regions on contrast-enhanced MRI.

METHODS

We evaluated 24 patients with pituitary adenoma or residual tumor using CUBE with and without DLR, 1-mm slice thickness 2D T1WI (1-mm 2D T1WI) with DLR, and 3D spoiled gradient echo sequence (SPGR) as contrast-enhanced MRI. Depiction scores of pituitary adenoma and parasellar regions were assigned by two neuroradiologists, and contrast-to-noise ratio (CNR) was calculated.

RESULTS

CUBE with DLR showed significantly higher scores for depicting pituitary adenoma or residual tumor compared to CUBE without DLR, 1-mm 2D T1WI with DLR, and SPGR (p < 0.01). The depiction score for delineation of the boundary between adenoma and the cavernous sinus was higher for CUBE with DLR than for 1-mm 2D T1WI with DLR (p = 0.01), but the difference was not significant when compared to SPGR (p = 0.20). CUBE with DLR had better interobserver agreement for evaluating adenomas than 1-mm 2D T1WI with DLR (Kappa values, 0.75 vs. 0.41). The CNR of the adenoma to the brain parenchyma increased to a ratio of 3.6 (obtained by dividing 13.7, CNR of CUBE with DLR, by 3.8, that without DLR, p < 0.01). CUBE with DLR had a significantly higher CNR than SPGR, but not 1-mm 2D T1WI with DLR.

CONCLUSION

On the contrast-enhanced MRI, compared to CUBE without DLR, 1-mm 2D T1WI with DLR and SPGR, CUBE with DLR improves the depiction of pituitary adenoma and parasellar regions.

摘要

目的

评估基于深度学习重建(DLR)的 T1 加权 3D 快速自旋回波序列(CUBE)对对比增强 MRI 中垂体瘤和鞍旁区域的显示能力。

方法

我们评估了 24 例垂体瘤或残留肿瘤患者,使用 CUBE 联合和不联合 DLR、1mm 层厚 2D T1WI(1mm 2D T1WI 联合 DLR)和 3D 扰相梯度回波序列(SPGR)作为对比增强 MRI。两名神经放射科医生对垂体瘤和鞍旁区域的显示评分进行了评估,并计算了对比噪声比(CNR)。

结果

CUBE 联合 DLR 显示垂体瘤或残留肿瘤的评分明显高于 CUBE 不联合 DLR、1mm 2D T1WI 联合 DLR 和 SPGR(p<0.01)。CUBE 联合 DLR 对区分肿瘤与海绵窦边界的评分高于 1mm 2D T1WI 联合 DLR(p=0.01),但与 SPGR 相比差异无统计学意义(p=0.20)。CUBE 联合 DLR 对评估腺瘤的观察者间一致性优于 1mm 2D T1WI 联合 DLR(Kappa 值,0.75 与 0.41)。肿瘤与脑实质的 CNR 增加至 3.6(CUBE 联合 DLR 的 CNR 为 13.7,CUBE 不联合 DLR 的 CNR 为 3.8,两者相除,p<0.01)。CUBE 联合 DLR 的 CNR 明显高于 SPGR,但与 1mm 2D T1WI 联合 DLR 相比无差异。

结论

在对比增强 MRI 中,与 CUBE 不联合 DLR、1mm 2D T1WI 联合 DLR 和 SPGR 相比,CUBE 联合 DLR 可改善垂体瘤和鞍旁区域的显示。

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Nat Rev Endocrinol. 2023 Dec;19(12):722-740. doi: 10.1038/s41574-023-00886-5. Epub 2023 Sep 5.
2
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3
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J Imaging Inform Med. 2025 Apr 14. doi: 10.1007/s10278-025-01400-1.
4
Determining the normal range of the dimensions and volume of the pituitary gland of children using a 3D magnetic resonance imaging (MRI) protocol in Imam Hossein Hospital of Isfahan in 2021 to 2024.2021年至2024年期间,在伊斯法罕伊玛目侯赛因医院使用3D磁共振成像(MRI)方案确定儿童垂体的尺寸和体积正常范围。
Am J Neurodegener Dis. 2025 Feb 25;14(1):42-50. doi: 10.62347/CXAQ5541. eCollection 2025.
5
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Korean J Radiol. 2025 Feb;26(2):180-192. doi: 10.3348/kjr.2024.0701.
基于深度学习重建的垂体薄层 MRI 对垂体瘤侵袭海绵窦的术前预测:一项前瞻性研究。
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4
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5
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Magn Reson Med Sci. 2020 Aug 3;19(3):195-206. doi: 10.2463/mrms.mp.2019-0018. Epub 2019 Sep 4.
7
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Endocr J. 2019 Mar 28;66(3):259-264. doi: 10.1507/endocrj.EJ18-0458. Epub 2019 Feb 14.
8
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.基于卷积神经网络多通道残差学习的3D磁共振图像去噪
Jpn J Radiol. 2018 Sep;36(9):566-574. doi: 10.1007/s11604-018-0758-8. Epub 2018 Jul 7.
9
Deep Learning: A Primer for Radiologists.深度学习:放射科医生入门。
Radiographics. 2017 Nov-Dec;37(7):2113-2131. doi: 10.1148/rg.2017170077.
10
Comparison of MRI techniques for detecting microadenomas in Cushing's disease.比较磁共振成像技术在库欣病微腺瘤检测中的应用。
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