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在PCOS患者中,通过深度学习重建高分辨率MRI提高毛囊计数的重复性

Renjie Yang¹, Yujie Zou², Liang Li¹

¹Department of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.

Scientific reports

|January 8, 2025

概括

对SSFSEMRI图像的深度学习重建改善了用于诊断多囊性卵巢综合征 (PCOS) 的卵泡检测. 与传统方法相比,这种增强的成像技术在毛囊计数中提供了更好的重复性.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

多囊性卵巢综合征 (PCOS) 诊断依赖于卵泡数量,这通常是经阴道超声波相比MRI低估的.
传统的MRI毛囊计数面临着运动工件和有限的空间分辨率的挑战,影响重复性.

研究的目的:

为了评估深度学习 (DL) 的性能,重建了SSFSE T2加权的MRI图像,用于检测PCOS患者的卵巢卵泡.
将DL重建的SSFSE (SSFSE-DL) 图像的诊断准确性和可重复性与传统的SSFSE (SSFSE-C) 和PROPELLER MRI序列进行比较.

主要方法:

这项前性研究涉及22名PCOS患者.
使用PROPELLER和SSFSE T2加权序列进行高分辨率的卵巢MRI,抑制运动工件.
应用DL重建SSFSE图像以减轻噪声.
评估了定性指数 (人工物,噪音,卵泡明显性) 和每卵巢卵泡数量的重复性 (FNPO) 评估.

主要成果:

与SSFSE-C和PROPELLER相比,SSFSE-DL图像在所有定性指数 (模糊工件,主观噪音,毛囊明显性) 中表现出更好的表现.
DL重建显著提高了FNPO评估的可重复性.
在SSFSE-DL和PROPELLER图像之间,主观噪声水平是可比的.

结论:

高分辨率SSFSEMRI的深度学习重建显示出作为卵巢卵泡识别可靠方法的巨大潜力.
这种技术提供了更好的诊断准确性和可重复性,在临床实践中促进更可靠的PCOS诊断.

关键词:

深度学习是一种深度学习.高分辨率的高分辨率解决方案磁共振成像技术磁共振成像技术卵巢卵巢卵巢卵巢多囊性卵巢综合征多囊性卵巢综合征

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