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前列腺快速MRI:一个公共的,双参数MRI数据集,以推进前列腺癌成像的机器学习

Radhika Tibrewala^1,2,3, Tarun Dutt⁴, Angela Tong^5,4

¹Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA. radhika.tibrewala@nyulangone.org.

Scientific data

|April 20, 2024

概括

快速MRI倡议现在提供原始前列腺磁共振成像 (MRI) k空间数据. 这一数据集支持人工智能 (AI) 开发,以改善前列腺癌诊断和成像.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

磁共振成像 (MRI) 与人工智能 (AI) 的整合正在推动图像采集和重建.
公共MRI数据集往往缺乏AI模型培训所必需的原始k空间数据.
快速MRI倡议此前已经发布了大脑和膝盖k-space数据集.

研究的目的:

通过发布双参数前列腺MRI k空间数据来扩大快速MRI倡议.
促进人工智能和前列腺MRI重建的算法开发.
通过改进成像来提高前列腺癌的诊断和管理.

主要方法:

快速MRI数据集被扩展到包括双参数 (T2和扩散权重) 前列腺MRI数据.
该数据集包含来自312名患者的47468个切片的原始k空间数据,重建的图像和注释.
数据包括前列腺癌可能性的切片,体积和考试级别注释.

主要成果:

现在可以获得双参数前列腺MRI原始k空间数据的全面数据集.
该数据集支持人工智能驱动的低采样MRI数据的重建.
这个资源有助于开发具有成本效益和可访问的前列腺MRI.

结论:

新的快速MRI前列腺数据集对于在医学成像中推进AI至关重要.
它将加速前列腺MRI重建算法的开发.
最终,这有助于改善前列腺癌诊断和患者管理.

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Magnetic Resonance Imaging

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