Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
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. P 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; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion: Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose.
Details
Original language | English |
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Article number | e222211 |
Journal | Radiology |
Volume | 307 |
Issue number | 3 |
Publication status | Published - May 2023 |
Peer-reviewed | Yes |
External IDs
PubMed | 36943080 |
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Keywords
ASJC Scopus subject areas
Keywords
- Humans, Middle Aged, Contrast Media, Retrospective Studies, Magnetic Resonance Imaging/methods, Breast, Machine Learning