Okolie, Augustine http://orcid.org/0000-0002-8684-1358
Dirrichs, Timm
Huck, Luisa Charlotte
Nebelung, Sven
Arasteh, Soroosh Tayebi
Nolte, Teresa
Han, Tianyu
Kuhl, Christiane Katharina
Truhn, Daniel
Article History
Received: 25 August 2023
Revised: 27 April 2024
Accepted: 3 June 2024
First Online: 1 August 2024
Compliance with ethical standards
:
: The scientific guarantor of this publication is Dr. Med. Daniel Truhn.
: The authors of this manuscript declare no conflict of interest.
: Dr. Augustine Okolie, an author with an extensive background in statistical methodologies, acted as the statistical guarantor for this study.
: Only if the study is on human subjects: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: We analyzed 9751 MRI exams from 5086 patients between 2010 and 2019. Conversely, a prior study published by Müller-Franzes et al [] investigated using GANs to generate contrast-enhanced breast MRIs from non-enhanced or low-contrast images. <i>Overlap:</i> Both studies used the same cohort of 9751 breast MRI examinations from 5086 patients spanning from January 2010 to December 2019. <i>Distinctive Features:</i> Objective and Approach: Our study (“Accelerating Breast MRI Acquisition with Generative AI Models”) centers on the application of score-based diffusion models to accelerate breast MRI reconstruction. In contrast the study by Müller-Franzes et al [] investigates the use of generative adversarial networks (GANs) to produce synthetic contrast-enhanced MRI images either from unenhanced photos or virtual low-contrast-enhanced images. Research Question: Our study questions how much the scan times can be reduced using score-based diffusion models without compromising the quality of breast MRI images. The previously published paper investigates whether GANs can recreate contrast-enhanced breast MRI scans from unenhanced and virtually low-contrast-enhanced images. Outcome Metrics: In our study, the focus is on ratings of reconstructed images at different undersampling factors. The previously published paper measured the ability of radiologists to distinguish between real and synthesized contrast-enhanced images and compared the appearance and conspicuity of enhancing lesions on real vs. synthesized images. Clinical Implications: Our study suggests the possibility of substantially reducing MRI scan times without compromising image quality. The previously published paper emphasized the potential for breast MRI with a reduced contrast agent dose.
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