Advanced Review Modeling breast biomechanics for multi-modal image analysis—successes and challenges Vijay Rajagopal, Poul M. F. Nielsen and Martyn P. Nash ∗ Biomechanical modeling of the breast is a burgeoning research field that has potential uses across a wide range of healthcare applications. This review describes recent developments regarding multi-modal breast image analysis, and outlines some of the key challenges that researchers face in introducing the models into the clinical arena. Deformable breast models have demonstrated capabilities across a wide range of breast cancer diagnoses and treatments. Specific applications include magnetic resonance (MR) image guided surgery, registration of x-ray and MR images, and breast reduction/augmentation surgery planning. Challenges lie in improving the fidelity of these models, which are presently simplistic and use many unverified parameters. Specific challenges include characterization of individual-specific mechanical properties of breast tissues, precise representation of loading and boundary constraints during different clinical procedures, and validation of modeling techniques used to represent key mechanical aspects such as the suspensory Cooper’s ligaments. Scientists must also work towards translating their research tools into the clinical setting by developing efficient tools with user-friendly interactivity. Widespread adoption of such techniques has the potential to significantly reduce the numbers of misdiagnosed breast cancers and enhance surgical planning for patient treatment .  2009 John Wiley & Sons, Inc. WIREs Syst Biol Med 2010 2 293–304 B reast cancer is a leading cause of cancer death among women worldwide. X-ray mammography is considered to be the gold-standard modality for early detection of breast cancer. With its high throughput (scans taking typically 10 s) and relatively low cost (a typical MRI costs four times that of a mammogram), it is presently the only feasible imaging modality for screening. Nevertheless, x- ray mammograms alone cannot be used to identify and characterize all cancers, partly because of their two-dimensional representation of a 3D compressed breast and also because of their limited applicability to women with dense breasts. Therefore, when a suspicious lesion is identified on a mammogram, clinicians typically acquire additional views of the ∗ Correspondence to: martyn.nash@auckland.ac.nz Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand DOI: 10.1002/wsbm.58 breast using different imaging modalities such as magnetic resonance imaging (MRI), ultrasound or positron emission tomography (PET). However, these other modalities also have drawbacks. For example, MRI scans, while providing three-dimensional views of the breast in a relatively benign manner (as opposed to harmful x-rays), have a low specificity and hence often require the use of contrast agents to detect cancers. PET scans can highlight the spread of disease and help monitor treatment response, but provide poor resolution (3–5 mm as opposed to 50 µm on an x-ray mammogram). Thus, PET images have good specificity, but poor sensitivity, especially to small tumors that are typically found in screening population. 1 These modality-specific drawbacks may be overcome by using the information from different imaging modalities in a complementary manner. Indeed, research has shown that breast cancer detection is more effective when clinicians analyze Volume 2, May/June 2010  2009 John Wiley & Sons, Inc. 293