Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Background Virtual contrast-enhanced (vCE) imaging techniques are an emerging topic of research in breast MRI. Purpose To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of vCE breast MRI. Materials and Methods The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age:52±12 years) obtained from 2017-2020 (single site, two 3T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value=50–1500s/mm2). Three readers evaluated the vCE images with regards to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise-ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. Results The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p<.05). Non-significant effects (p>.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. Conclusion vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when a multi b-value DWI is added to morphologic T1w-/T2w-sequences as input for model training. Key Results 1. The inclusion of diffusion-weighted imaging significantly improves the conspicuity of lesions/non-mass enhancements and satisfaction with the image contrast in virtual contrast-enhanced breast MRI. 2. The quality of virtual contrast-enhanced breast MRI scans benefits from the inclusion of high-resolution morphologic T1-weighted image acquisitions. 3. Quantitative metrics such as the structural similarity index and peak signal-to-noise ratio calculated over the entire breast volume insufficiently reflect variations in lesion/non-mass enhancement’s individual characteristics. ### Competing Interest Statement S.B., A.L. and H. S. have a patent pending EPO No. 21197259.1 on methods associated with the topic of this article. ### Funding Statement This project is funded by the Bavarian State Ministry of Science and the Arts in the framework of the bidt Graduate Center for Postdocs. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The ethics committee of the Friedrich-Alexander Univeristy Erlangen-Nurnberg approved this retrospective study and waived the need for informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Original image data used in this work are not publicly available to preserve individuals privacy under the European General Data Protection Regulation. The institution handling this data is the Institute of Radiology University Hospital Erlangen. * CE : contrast enhanced vCE : virtual contrast enhanced SSIM : structural similarity index PSNR : peak signal-to-noise-ratio NRMSE : normalized root mean square error MEDSYMAC : median symmetrical accuracy NME : non-mass-enhancement b50 : diffusion weighted imaging acquisition with a b-value of 50 s/mm2 b750 : diffusion weighted imaging acquisition with a b-value of 750 s/mm2 b1500 : diffusion weighted imaging acquisition with a b-value of 1500 s/mm2