Flattening-Based Visualization of Supine Breast MRI

Abstract

We propose two novel visualization methods optimized for supine breast images that “flatten” breast tissue, facilitating examination of larger tissue areas within each coronal slice. Breast cancer is the most frequently diagnosed cancer in women, and early lesion detection is crucial for reducing mortality. Supine breast magnetic resonance imaging (MRI) enables better lesion localization for image-guided interventions; however, traditional axial visualization is suboptimal because the tissue spreads over the chest wall, resulting in numerous fragmented slices that radiologists must scroll through during standard interpretation. Using a human-centered design approach, we incorporated user and expert feedback throughout the co-design and evaluation stages of our flattening methods. Our first proposed method, a surface-cutting approach, generates offset surfaces and flattens them independently using As-Rigid-As-Possible (ARAP) surface mesh parameterization. The second method uses a landmark-based warp to flatten the entire breast volume at once. While the surface-cutting approach is based on the parameterization of individual surfaces, the second method uses control points to realize a coherent, global deformation.Expert evaluations revealed that the surface-cutting method provides intuitive overviews and clear vascular detail, with low metric (2.1-3.5%) and area (3.7-5.8%) distortions. However, independent slice flattening can introduce depth distortions across layers. The landmark warp offers consistent slice alignment and supports direct annotations and measurements, with radiologists favoring it for its anatomical accuracy. Both methods significantly reduced the number of slices needed to review, highlighting their potential for time savings and clinical impact - an essential factor for adopting supine breast MRI.