Academic Journal
Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network
| Title: | Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network |
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| Authors: | Andrzej Liebert, Hannes Schreiter, Lorenz A Kapsner, Jessica Eberle, Chris Ehring, Dominique Hadler, Luise Brock, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt |
| Source: | Eur Radiol |
| Publisher Information: | Springer Science and Business Media LLC, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Adult, Contrast Media, Breast Neoplasms, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Diffusion Magnetic Resonance Imaging, Imaging Informatics and Artificial Intelligence, Image Interpretation, Computer-Assisted, Humans, Female, Neural Networks, Computer, Breast, Retrospective Studies, Aged |
| Description: | Objective To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI. Materials and methods The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T 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 = 50–1500 s/mm2). Three readers evaluated the vCE images with regard 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 p > 0.05) 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 multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training. Key Points Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research. Graphical Abstract |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-11142-3 |
| DOI: | 10.1101/2024.05.03.24306067 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/39455455 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....c891d39e17f6073c816ffd9eeac2b9b9 |
| Database: | OpenAIRE |
| ISSN: | 14321084 |
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| DOI: | 10.1007/s00330-024-11142-3 |