Multimodal Breast Parenchymal Patterns Correlation Using a Patient-Specific Biomechanical Model

• Published in: IEEE Transactions on Medical Imaging Vol. 37; no. 3; pp. 712 - 723
• Main Authors: Garcia, Eloy, Diez, Yago, Diaz, Oliver, Llado, Xavier, Gubern-Merida, Albert, Marti, Robert, Marti, Joan, Oliver, Arnau
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2018; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Algorithms; Biological system modeling; Biomechanical Phenomena; Biomechanical Phenomena - physiology; Biomechanics; Breast; Breast - diagnostic imaging; Breast - physiology; Breast cancer; Breasts; Computer simulation; Correlation; cross-modality; Density; Female; Humans; Magnetic Resonance Imaging; Magnetic Resonance Imaging - methods; Mammography; Mammography - methods; Mechanical properties; Middle Aged; Multimodal Imaging; Multimodal Imaging - methods; Optimization; Parameter modification; parenchymal patterns; Radboud University Medical Center; Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences; Resampling; Similarity; subject-specific biomechanical models; Surface morphology; Tissues; Two dimensional models
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2017.2749685

In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, which registers MRI volumes to X-ray mammograms using a subject-specific biomechanical model of the breast. The optimization step modifies the position, orientation, and elastic parameters of the breast model to perform the alignment between the images. When the model reaches an optimal solution, the MRI glandular tissue is projected and compared with the one obtained from the corresponding mammograms. To reduce the loss of information during the ray-casting, we introduce a new approach that avoids resampling the MRI volume. In the results, we focus our efforts on evaluating the agreement of the distributions of glandular tissue, the degree of structural similarity, and the correlation between the real and synthetic density maps. Our approach obtained a high-structural agreement regardless the glandularity of the breast, whilst the similarity of the glandular tissue distributions and correlation between both images increase in denser breasts. Furthermore, the synthetic images show continuity with respect to large structures in the density maps.