Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Sci Data. 2024 Apr 20;11(1):404. doi: 10.1038/s41597-024-03252-w.
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
磁共振成像（MRI）在将人工智能（AI）集成到图像采集和重建中取得了显著进展。在这些任务中，训练 AI 模型需要使用原始 k 空间数据，但公共 MRI 数据集大多仅限于 DICOM 图像。为了解决这个限制，fastMRI 计划发布了脑部和膝盖 k 空间数据集，此后这些数据集得到了广泛应用。2023 年 5 月，fastMRI 计划扩展到包括来自临床人群的双参数（T2 和扩散加权）前列腺 MRI 数据。双参数 MRI 在前列腺癌的诊断和管理中起着至关重要的作用。成像方法的进步，如从加速采集重建欠采样数据，可以提高前列腺 MRI 的成本效益和可及性。该数据集提供了 312 名患者的 1560 个容积共 47468 个层面的用于前列腺癌可能性的原始 k 空间数据、重建图像以及层面、体积和检查水平注释。该数据集促进了用于前列腺图像重建的 AI 和算法开发，最终目标是增强前列腺癌的诊断。
