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

Deep Learning based T2 distribution estimation for bone marrow characterization in inflammation and aging - Brock, Luise; Ben-Atya, Hadas; Saar, Galit; Folle, Lukas; Maier, Andreas; Freiman, Moti; Vandoorne, Katrien (European Molecular Imaging Meeting (EMIM), 2023)

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

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions - Pistel, Mona; Brock, Luise; Laun, Frederik Bernd; Erber, Ramona; Weiland, Elisabeth; Uder, Michael; Wenkel, Evelyn; Ohlmeyer, Sabine; Bickelhaupt, Sebastian (Diagnostics, 2024)
Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.
CRIS-Team Scopus Importer:2024-07-19
Keywords: breast lesions; diffusion magnetic resonance imaging; radiomic stability

T2 Distribution Analysis of Inflamed Bone Marrow Compartments in MR Images with Quantitative T2-mapping - Brock, Luise; Ben-Atya, Hadas; Saar, Galit; Folle, Lukas; Maier, Andreas; Vandoorne, Katrien; Freiman, Moti (2024 ISMRM & ISMRT Annual Meeting & Exhibition, 2024)