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.

Modeling breast biomechanics for multi-modal image analysis--successes and challenges.

The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 ± 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3–8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm3, 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of −1.4 PVBD and a correlation with R2 = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.

/pdf/validation-of-a-method-for-measuring-the-volumetric-breast-3033sce5ku.pdf

Validation of a method for measuring the volumetric breast density from digital mammograms

Breast lesion co-localisation between X-ray and MR images using finite element modelling.

Breast magnetic resonance imaging (MRI) and x-ray mammography are two image modalities widely used for the early detection and diagnosis of breast diseases in women. The combination of these modalities leads to a more accurate diagnosis and treatment of breast diseases. The aim of this paper is to review the registration between breast MRI and x-ray mammographic images using patient-specific finite element-based biomechanical models. Specifically, a biomechanical model is obtained from the patient's MRI volume and is subsequently used to mimic the mammographic acquisition. Due to the different patient positioning and movement restrictions applied in each image modality, the finite element analysis provides a realistic physics-based approach to perform the breast deformation. In contrast with other reviews, we do not only expose the overall process of compression and registration but we also include main ideas, describe challenges, and provide an overview of the used software in each step of the process. Extracting an accurate description from the MR images and preserving the stability during the finite element analysis require an accurate knowledge about the algorithms used, as well as the software and underlying physics. The wide perspective offered makes the paper suitable not only for expert researchers but also for graduate students and clinicians. We also include several medical applications in the paper, with the aim to fill the gap between the engineering and clinical performance.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.12673

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.

Automatic segmentation of breast MR images through a Markov random field statistical model.

Predicting breast tissue deformation is of great significance in various medical practice including diagnosis and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately. Clinically it is important to combine information provided by images from several modalities or at different times. However, images taken by various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. Hence, some form of spatial nonrigid transformation of image data is required so that the tissues are represented in an equivalent configuration. In this paper we propose finite element method (FEM) based strategy for correspondence identification between image features identified in two view mammography. The algorithm models breast compression during mammography and allows for correspondence recovery of 2D features found in two views and reconstruction of their 3D locations. The algorithm was tested on MRI and mammography images of triple modality phantom and limited set of mammographic images.

Correspondence recovery in 2-view mammography

Predicting breast tissue deformation is of great significance in several medical applications such as biopsy, diagnosis, and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately beforehand. Also clinically it is important for combining the information provided by images from several modalities or at different times, for the detection/diagnosis, treatment planning and guidance of interventions. Multi-modality imaging of the breast obtained by X-ray mammography, MRI is thought to be best achieved through some form of data fusion technique. However, images taken by these various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. In these cases some form of spatial transformation of image data from one geometry to another is required such that the tissues are represented in an equivalent configuration. 
We propose to use a 3D finite element model for lesion correspondence in breast imaging. The novelty of the approach lies in the following facts: (1) Finite element is the most accurate technique for modeling deformable objects such as breast. The physical soundness and mathematical rigor of finite element method ensure the accuracy and reliability of breast modeling that is essential for lesion correspondence. (2) When both MR and mammographic images are available, a subject-specific 3D breast model will be built from MRIs. If only mammography is available, a generic breast model will be used for two-view mammography reading. (3) Incremental contact simulation of breast compression allows accurate capture of breast deformation and ensures the quality of lesion correspondence. (4) Balance between efficiency and accuracy is achieved through adaptive meshing. We have done intensive research based on phantom and patient data.

/pdf/three-dimensional-finite-element-model-for-lesion-3kc6rgu61l.pdf

Three-dimensional finite element model for lesion correspondence in breast imaging

Performing regular mammographic screening and comparing corresponding mammograms taken from multiple
views or at different times are necessary for early detection and treatment evaluation of breast cancer, which is
key to successful treatment. However, mammograms taken at different times are often obtained under different
compression, orientation, or body position. A temporal pair of mammograms may vary significantly due to the
spatial disparities caused by the variety in acquisition environments, including 3D position of the breast, the
amount of pressure applied, etc. Such disparities can be corrected through the process of temporal registration.
We propose to use a 3D finite element model for temporal registration of digital mammography. In this paper,
we apply patient specific 3D breast model constructed from MRI data of the patient, for cases where lesions are
detectable in multiple mammographic views across time. The 3D location of the lesion in the breast model is
computed through a breast deformation simulation step presented in our earlier work. Lesion correspondence
is established by using a nearest neighbor approach in the uncompressed breast volume. Our experiments show
that the use of a 3D finite element model for simulating and analyzing breast deformation contributes to good
accuracy when matching suspicious regions in temporal mammograms.

Towards registration of temporal mammograms by finite element simulation of MR breast volumes

Clinically it is important to combine information provided by mammographic images from multiple views or at different times. Taking regular mammographic screening and comparing corresponding mammograms are necessary for early detection of breast cancer, which is the key to successful treatment. However, mammograms taken at different times are often obtained under different compressions, orientations or body positions. A temporal pair of mammograms may vary quite significantly due to the spatial disparities caused by the variety in acquisition environments, including the 3D position of the breast, the amount of the pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We have implemented and utilized finite element models for temporal registration of digital mammography. In our work, we applied the patient specific breast model, where patients have both mammograms and MRIs available, and generic model, where only patient mammograms are available. After we applied the temporal registration algorithm, the average error among the 14 patient datasets was 3.4 ± 0.86 mm for Euclidean distance and 4.3 ± 0.52 mm for predicted 2D lesion position. With generic model, the average error among the 14 patient datasets using the measure of Euclidean distance between the predicted lesion position in T1 and T2 was 5.0 ± 0.74 mm for Euclidean distance and 5.7 ± 0.83 mm for predicted 2D lesion position. Compared with the average lesion size (10mm∼40mm), this error is acceptable. With lesion correspondence, our finite element method can be used to suppress technical variations (e.g., mammogram positioning or compression) and to emphasize genuine alterations in the breast.

/pdf/temporal-registration-of-mammograms-by-finite-element-1f0omchutz.pdf

Temporal registration of mammograms by finite element simulation of mr breast volume deformation

The pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time-consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT-MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.

A semiautomatic segmentation method framework for pelvic bone tumors based on CT-MR multimodal images.

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