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Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories “minimal,” “mild,” “moderate,” and “marked.” The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories. This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa. Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815. Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.

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Diffusion tensor imaging (DTI) is a powerful MRI modality that allows the investigation of the microstructure of tissues both in vivo and noninvasively. Its reliability is strictly dependent on the performance of diffusion‐sensitizing gradients, of which spatial nonuniformity is a known issue in the case of virtually all clinical MRI scanners. The influence of diffusion gradient inhomogeneity on the accuracy of the diffusion tensor imaging was investigated by means of computer simulations supported by an MRI experiment performed at the isocenter and 15 cm away. The DTI measurements of two diffusion phantoms were simulated assuming a nonuniform diffusion‐sensitizing gradient and various levels of noise. Thereafter, the tensors were calculated by two methods: (i) assuming a spatially constant b‐matrix (standard DTI) and (ii) applying the b‐matrix spatial distribution in the DTI (BSD‐DTI) technique, a method of indicating the b‐matrix for each voxel separately using an anisotropic phantom as a standard of diffusion. The average eigenvalues and fractional anisotropy across the homogeneous region of interest were calculated and compared with the expected values. Diffusion gradient inhomogeneity leads to overestimation of the largest eigenvalue, underestimation of the smallest one and thus overestimation of fractional anisotropy. The effect is similar to that caused by noise; however, it could not be corrected by increasing SNR. The MRI measurements, performed using a 3 T clinical scanner, revealed that the split of the eigenvalues measured 15 cm away from the isocenter is significant (up to 25%). The BSD‐DTI calibration allowed the reduction of the measured fractional anisotropy of the isotropic medium from 0.174 to 0.031, suggesting that gradient inhomogeneity was the main cause of this error. For the phantom measured at the isocenter, however, the split was almost not observed; the average eigenvalues were shifted from the expected value by ~ 5%.