Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging.

Background: There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI.

Purpose: To develop a generative model for gadolinium-free contrast-enhanced breast MRI and evaluate the diagnostic utility of the generated scans.

Study Type: Retrospective.

Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification.

Objective: To develop a deep learning model for synthesizing the first phases of dynamic (FP-Dyn) sequences to supplement the lack of information in unenhanced breast MRI examinations.

Methods: In total, 97 patients with breast MRI images were collected as the training set (n = 45), the validation set (n = 31), and the test set (n = 21), respectively. An enhance border lifelike synthesize (EDLS) model was developed in the training set and used to synthesize the FP-Dyn images from the T1WI images in the validation set.