AI-Based screening for thoracic aortic aneurysms in routine breast MRI - Bounias, Dimitrios; Führes, Tobit; Brock, Luise; Graber, Johanna; Kapsner, Lorenz; Liebert, Andrzej; Schreiter, Hannes; Eberle, Jessica; Hadler, Dominique; Skwierawska, Dominika; Floca, Ralf; Neher, Peter; et al. (Nature Communications, 2025)
Prognosis for thoracic aortic aneurysms is significantly worse for women than men, with a higher mortality rate observed among female patients. The increasing use of magnetic resonance breast imaging (MRI) offers a unique opportunity for simultaneous detection of both breast cancer and thoracic aortic aneurysms. We retrospectively validate a fully-automated artificial neural network (ANN) pipeline on 5057 breast MRI examinations from public (Duke University Hospital/EA1141 trial) and in-house (Erlangen University Hospital) data. The ANN, benchmarked against 3D-ground-truth segmentations, clinical reports, and a multireader panel, demonstrates high technical robustness (dice/clDice 0.88-0.91/0.97-0.99) across different vendors and field strengths. The ANN improves aneurysm detection rates by 3.5-fold compared with routine clinical readings, highlighting its potential to improve early diagnosis and patient outcomes. Notably, a higher odds ratio (OR = 2.29, CI: [0.55,9.61]) for thoracic aortic aneurysms is observed in women with breast cancer or breast cancer history, suggesting potential further benefits from integrated simultaneous assessment for cancer and aortic aneurysms.
CRIS-Team Scopus Importer:2025-06-20

Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping - Brock, Luise; Liebert, Andrzej; Schreiter, Hannes; Skwierawska, Dominika; Ehring, Chris; Eberle, Jessica; Laun, Frederik Bernd; Uder, Michael; Kapsner, Lorenz; Ohlmeyer, Sabine; Hadler, Dominique; Knoll, Florian; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
Breast magnetic resonance imaging (MRI) increasingly incorporates diffusion-weighted imaging (DWI) sequences. DWI allows to calculate apparent diffusion coefficient (ADC) maps which can supplement differentiation between malignant and benign breast lesions. However, artifacts in DWI are not infrequent, e.g., due to patient motion, pulsation or other sources, which can cause shifts between the different b-value acquisitions and affect the accuracy of the ADC map. This IRB-approved, retrospective study includes n = 141 women with breast lesions undergoing breast MRI including an ultra-high b-value DWI acquisition (b = 1500s/mm2). Thirteen different rigid and non-rigid co-registration methods facilitated by the ANTs library were evaluated. The targets for co-registration were different b-value DWI. ADC maps were calculated from the co-registered DWI images and analyzed quantitatively, measuring mean, standard deviation, and within-lesion coefficient of variance (CoV), using repeated-measures ANOVA alongside Dunnett post-hoc tests in manual segmentations of the target lesions performed on ultra-high b-value images. In addition, we evaluated between-patient CoV and area under the curve (AUC) for differentiation of benign and malignant lesions, as well as sensitivity, specificity, and accuracy to measure clinical efficacy. Amongst the n = 13 co-registration techniques, the iterative SyNCC co-registration method with ultra-high b-value DWI images as target demonstrated the most notable improvement in standard deviation and within-lesion CoV while maintaining consistent mean ADC values. However, none of the co-registration method significantly improved the differentiation of lesion types. Nonetheless, the study offers a detailed comparison of co-registration methods for DWI images in ADC map generation.
CRIS-Team Scopus Importer:2024-11-08
Keywords: ADC; Breast MRI; Co-registration

Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks - Liebert, Andrzej; Hadler, Dominique; Ehring, Chris; Schreiter, Hannes; Brock, Luise; Kapsner, Lorenz; Eberle, Jessica; Erber, Ramona; Emons, Julius; Laun, Frederik Bernd; Uder, Michael; Wenkel, Evelyn; et al. (European Radiology Experimental, 2025)
Background: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS (VirtuT2w) images from routine multiparametric breast MRI images. Methods: This IRB-approved, retrospective study included 914 breast MRI examinations from January 2017 to June 2020. The dataset was divided into training (n = 665), validation (n = 74), and test sets (n = 175). The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. Quantitative metrics were used to evaluate the different input protocols. A qualitative assessment by two radiologists was used to evaluate the VirtuT2w images of the best input protocol. Results: VirtuT2w images demonstrated the best quantitative metrics compared to original T2w-FS images for an input protocol using all of the available data. A high level of high-frequency error norm (0.87) indicated a strong blurring presence in the VirtuT2 images, which was also confirmed by qualitative reading. Radiologists correctly identified VirtuT2 images with at least 96% accuracy. Significant difference in diagnostic image quality was noted for both readers (p ≤ 0.015). Moderate inter-reader agreement was observed for edema detection on both T2w-FS images (κ = 0.49) and VirtuT2 images (κ = 0.44). Conclusion: The 2D-U-Net generated virtual T2w-FS images similar to real T2w-FS images, though blurring remains a limitation. Investigation of other architectures and using larger datasets is necessary to improve potential future clinical applicability. Relevance statement: Generating VirtuT2 images could potentially decrease the examination time of multiparametric breast MRI, but its quality needs to improve before introduction into a clinical setting. Key Points: Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks. Image blurring in virtual T2w-FS images currently limits their clinical applicability. Best quantitative performance could be achieved when using full dynamic-contrast-enhanced acquisition and DWI as input of the neural network.
CRIS-Team Scopus Importer:2025-05-09
Keywords: Artificial intelligence; Breast neoplasms; Diagnostic imaging; Magnetic resonance imaging; Neural network (computer)

How to best match voxels - evaluating different sequential co-registration strategies for ultra-high b-value DWI in multiparametric breast MRI - Brock, Luise; Liebert, Andrzej; Schreiter, Hannes; Ehring, Chris; Eberle, Jessica; Laun, Frederik Bernd; Uder, Michael; Kapsner, Lorenz; Ohlmeyer, Sabine; Knoll, Florian; Bickelhaupt, Sebastian (2024 ISMRM & ISMRT Annual Meeting & Exhibition, 2024)

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network - Liebert, Andrzej; Schreiter, Hannes; Kapsner, Lorenz; Eberle, Jessica; Ehring, Chris; Hadler, Dominique; Brock, Luise; Erber, Ramona; Emons, Julius; Laun, Frederik Bernd; Uder, Michael; Wenkel, Evelyn; et al. (European Radiology, 2024)
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 < 0.05). Non-significant effects (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: (Figure presented.)
CRIS-Team Scopus Importer:2024-11-08
Keywords: Artificial intelligence; Breast imaging; Magnetic resonance imaging; Neural network

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model - Kapsner, Lorenz; Folle, Lukas; Hadler, Dominique; Eberle, Jessica; Balbach, Eva; Liebert, Andrzej; Ganslandt, Thomas; Wenkel, Evelyn; Ohlmeyer, Sabine; Uder, Michael; Bickelhaupt, Sebastian (Scientific Reports, 2024)
The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall’s W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or ‘textbook representations’ of lesions.
CRIS-Team Scopus Importer:2024-03-29

Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration - Liebert, Andrzej; Das, Badhan Kumar; Kapsner, Lorenz; Eberle, Jessica; Skwierawska, Dominika; Folle, Lukas; Schreiter, Hannes; Laun, Frederik Bernd; Ohlmeyer, Sabine; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian (European Radiology, 2023)
Objectives: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. Materials and methods: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). Results: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. Conclusion: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. Clinical relevance statement: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. Key Points: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Artifact; Breast; Forecasting; Magnetic resonance imaging; Neural networks (computer)

Virtual Dynamic Contrast Enhanced Breast MRI Using 2D U-Net Architectures - Schreiter, Hannes; Eberle, Jessica; Kapsner, Lorenz; Hadler, Dominique; Ohlmeyer, Sabine; Erber, Ramona; Emons, Julius; Laun, Frederik Bernd; Uder, Michael; Wenkel, Evelyn; Bickelhaupt, Sebastian; Liebert, Andrzej (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025)
Breast Magnetic Resonance Imaging (MRI) examinations routinely include contrast-agent based dynamic contrast-enhanced (DCE) acquisitions. Expanding the accessibility and personalization of breast MRI might be supported amongst others by advancing non-contrast-enhanced MRI, such as virtual dynamic contrast-enhanced techniques (vDCE) utilizing neural networks. This IRB-approved retrospective study includes n = 540 breast MRI examinations acquired on a single 3T MRI scanner. Two 2D U-Net architectures were trained using non-contrast-enhanced MRI acquisitions including T1w, T2w and multi-b-value diffusion weighted imaging acquisitions as inputs and either a single (SCO-Net) or multiple (MCO-Net) time points of a DCE series as ground truth. The neural networks predicted a vDCE series corresponding to five consecutive DCE time points. Across all time points, no significant differences in structural similarity index (SSIM) could be found between the SCO-Net and MCO-Net, both achieving a mean SSIM of 0.86. For peak-signal-to-noise-ratio and normalized root-mean-square error, significantly better results could be observed for the MCO-Net reaching scores of 24.42dB and 0.087 respectively. Comparison of manual segmentations of findings on DCE and vDCE images reached a DICE score of 0.61 and an intersection over union (IoU) of 0.47 without significant differences between SCO-Net and MCO-Net. These findings suggest a technical feasibility of generating vDCE image series from unenhanced input acquisitions using neural networks. However, the analysis does not allow drawing any conclusion on the clinical assessment of lesion specific curve kinetics, which need to be assessed prior determining on the feasibility of deriving diagnostically meaningful enhancement characteristics in individual lesions.
CRIS-Team Scopus Importer:2025-03-14
Keywords: Breast MRI; Contrast-enhanced MRI; Deep Learning