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Joaquim Cardoso MSc.
Chief Researcher & Editor of the site
March 29, 2023
SOURCE:
Radiology
Gustav Müller-Franzes, Luisa Huck, Soroosh Tayebi Arasteh, Firas Khader, Tianyu Han, Volkmar Schulz, Ebba Dethlefsen, Jakob Nikolas Kather, Sven Nebelung, Teresa Nolte, Christiane Kuhl* , Daniel Truhn*
Mar 21 2023
Abstract
- Generative adversarial networks can help recover the full contrast information from simulated low-dose contrast-enhanced breast MRI examinations.
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 non mass 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.
Originally published at https://pubs.rsna.org
