Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images

• Published in: Radiology Vol. 307; no. 3; p. e222211
• Main Authors: Müller-Franzes, Gustav, Huck, Luisa, Tayebi Arasteh, Soroosh, Khader, Firas, Han, Tianyu, Schulz, Volkmar, Dethlefsen, Ebba, Kather, Jakob Nikolas, Nebelung, Sven, Nolte, Teresa, Kuhl, Christiane, Truhn, Daniel
• Format: Journal Article
• Language: English
• Published: United States 01.05.2023
• Subjects: Breast; Contrast Media; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Middle Aged; Retrospective Studies
• ISSN: 0033-8419; 1527-1315
• DOI: 10.1148/radiol.222211

Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; < .001), with the noninferiority margin met by synthetic images from approach A ( < .001) but not B ( > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 See also the editorial by Bahl in this issue.