4D-Fueled AI with DCE-MRI Improves Breast Lesion Characterization
Excerpt from the Article:
Radiologists can classify breast lesions more accurately if they use artificial intelligence algorithms fueled by 4D data captured with dynamic contrasted-enhanced MRI (DCE-MRI), new research has found.
In a study published on Feb. 24 in Radiology: Artificial Intelligence, investigators from the University of Chicago outlined their method for fusing the 4D volumetric and temporal details gleaned from DCE-MRI with 2D deep-learning breast lesions analysis. Using maximum intensity projection (MIP) of lesion features that are pulled from four dynamic time points during a DCE-MRI via a deep-learning algorithm can provide better lesion classification, they said.
“Incorporating 4D information in DCE-MRI by feature MIP in deep transfer learning demonstrated superior classification performance compared with using MIP images as input in the task of distinguishing between benign and malignant breast lesions,” said the team led by doctoral student Qiyuan Hu, noting that their method outperforms the existing MIP strategy that only uses one post-contrast subtraction image.
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