Flattening-based visualization of supine breast MRI

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. Expert evaluations revealed that the surface-cutting method provides intuitive overviews and clear vascular detail, with low metric (2–2.5%) and area (3.7–4.4%) 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 MRI. • Novel Visualization Methods: Introduced two new methods for optimizing the visualization of supine breast MRI by flattening breast tissue, allowing for easier and more comprehensive review of larger tissue areas. • Surface-Cutting Approach: Developed a surface-cutting method using As-Rigid-As-Possible (ARAP) mesh parameterization, providing clear anatomical overviews with minimal distortion (2–4.4%). • Landmark-Based Warp Approach: Proposed a landmark-based warp method for uniform flattening of the entire breast volume, offering consistent slice alignment and improved anatomical accuracy for annotation and measurement. • Expert Evaluations: Expert feedback revealed the landmark-based approach as the preferred method for its anatomical precision, while both methods showed potential for time-saving benefits in clinical settings. • Impact on Clinical Practice: Both methods significantly reduced the number of slices needed for review, showcasing their potential for enhancing diagnostic workflow and reducing the time required for MRI interpretation.

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Publication Details

Journal
Computers & Graphics
Published
2025-09-16
DOI
https://doi.org/10.1016/j.cag.2025.104395
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.72
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article

Flattening-based visualization of supine breast MRI

J. Alain Kummer, Renata G. Raidou, Elmar Laistler, Lena Nohava et al.
2 citations
Computers & Graphics
Radiomics and Machine Learning in Medical Imaging
2.72
article

Flattening-based visualization of supine breast MRI

J. Alain Kummer, Renata G. Raidou, Elmar Laistler, Lena Nohava, Katja Bühler
article en
2 citations

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. Expert evaluations revealed that the surface-cutting method provides intuitive overviews and clear vascular detail, with low metric (2–2.5%) and area (3.7–4.4%) 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 MRI. • Novel Visualization Methods: Introduced two new methods for optimizing the visualization of supine breast MRI by flattening breast tissue, allowing for easier and more comprehensive review of larger tissue areas. • Surface-Cutting Approach: Developed a surface-cutting method using As-Rigid-As-Possible (ARAP) mesh parameterization, providing clear anatomical overviews with minimal distortion (2–4.4%). • Landmark-Based Warp Approach: Proposed a landmark-based warp method for uniform flattening of the entire breast volume, offering consistent slice alignment and improved anatomical accuracy for annotation and measurement. • Expert Evaluations: Expert feedback revealed the landmark-based approach as the preferred method for its anatomical precision, while both methods showed potential for time-saving benefits in clinical settings. • Impact on Clinical Practice: Both methods significantly reduced the number of slices needed for review, showcasing their potential for enhancing diagnostic workflow and reducing the time required for MRI interpretation.

Computers & GraphicsVol. 133
VRVis GmbH (Austria) (AT), TU Wien (AT), Christian Doppler Laboratory for Thermoelectricity (AT), Medical University of Vienna (AT)
Openalex Percentile: Top 9%
Radiomics and Machine Learning in Medical Imaging
2.72
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