  Purpose: We propose a deep learning-based computer-aided detection (CADe)
method to detect breast lesions in ultrafast DCE-MRI sequences. This method
uses both the three-dimensional spatial information and temporal information
obtained from the early-phase of the dynamic acquisition. Methods: The proposed
CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1
weighted sequences, which are preprocessed for motion compensation, temporal
normalization, and are cropped before passing into the model. The model is
optimized to enable the detection of relatively small breast lesions in a
screening setting, focusing on detection of lesions that are harder to
differentiate from confounding structures inside the breast. Results: The
method was developed based on a dataset consisting of 489 ultrafast MRI studies
obtained from 462 patients containing a total of 572 lesions (365 malignant,
207 benign) and achieved a detection rate, sensitivity, and detection rate of
benign lesions of 0.90 (0.876-0.934), 0.95 (0.934-0.980), and 0.81
(0.751-0.871) at 4 false positives per normal breast with 10-fold
cross-testing, respectively. Conclusions: The deep learning architecture used
for the proposed CADe application can efficiently detect benign and malignant
lesions on ultrafast DCE-MRI. Furthermore, utilizing the less visible hard-to
detect-lesions in training improves the learning process and, subsequently,
detection of malignant breast lesions.
