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

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

/pdf/breast-tissue-classification-in-digital-tomosynthesis-images-55ul0kpphh.pdf

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

There are many challenges clinicians are faced with when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians by providing a physics based approach to addressing some of these challenges. This review describes the state of the art in the field, from aiding co-location of information between various medical imaging modalities used to identify tumours; to providing the ability to predict the location of these tumors during different biopsy or surgical procedures; to aiding temporal registration of follow-up medical images used to review the progress of suspicious lesions and therefore evaluate effectiveness of breast cancer treatments; to aiding implant selection for breast augmentation procedures and the subsequent prediction of the resulting appearance following such procedures. Significant technical challenges remain in terms of improving the accuracy of such biomechanical models. These include the precise determination and application of loading and boundary constraints applied during different clinical procedures, and accurate characterization of individual-specific mechanical properties of the different breast tissues. In addition to these more technical challenges, a number of practical challenges exist when translating biomechanical models from research based environments into clinical workflows, which demand general applicability, and ease and speed of use. This review outlines such challenges and provides an overview of the steps researchers are taking to address them. Once these challenges have been met, there is potential for extending the use of biomechanics to simulate more complex clinical procedures, from modeling needle insertions into breast tissue during real-time biopsy procedures, to simulating and predicting the outcome of different surgical procedures such as tumorectomies. Clinical adoption of such state-of-the-art modeling techniques has significant potential for reducing the number of misdiagnosed breast cancers while also helping improve clinical treatment of patients.

Patient-Specific Modeling of Breast Biomechanics with Applications to Breast Cancer Detection and Treatment

Clinicians face many challenges when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians in addressing some of these challenges using a physics-based approach. This includes helping to colocate information between various medical imaging modalities that are used to identify tumors, and providing the ability to predict the location of these tumors during different treatment procedures. This review describes the state of the art in the field of modeling breast biomechanics, highlighting methods that have been used for constructing biomechanical models of the breast, and the constitutive relations that are typically used to describe the behavior of its different tissues. Examples of how these models have been used to address clinical challenges are also presented. Clinical adoption of such modeling techniques has significant potential for reducing instances of misdiagnosed breast cancers, while also helping to improve clinical treatment of patients.

Clinical Applications of Breast Biomechanics

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.

/pdf/an-enhanced-visualization-of-dbt-imaging-using-blind-182q50bmgw.pdf

An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization

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

Breast tomosynthesis is an emerging state-of-the-art three-dimensional (3D) imaging technology that demonstrates
significant early promise in screening and diagnosing breast cancer. However, this kind of image has significant out-of-plane
artifacts due to its limited tomography nature, which affects the image quality and further would interrupt
interpretation. In this paper, we develop a robust deblurring method to remove or suppress blurry artifacts by applying
three-dimensional (3D) nonlinear anisotropic diffusion filtering method. Differential equation of 3D anisotropic
diffusion filtering is discretized using explicit and implicit numerical methods, respectively, combined by first (fixed
grey value) and second (adiabatic) boundary conditions under ten nearest neighbor grids configuration of finite
difference scheme. The discretized diffusion equation is applied in the breast volume reconstructed from the entire
tomosynthetic images of breast. The proposed diffusion filtering method is evaluated qualitatively and quantitatively on
clinical tomosynthesis images. Results indicate that the proposed diffusion filtering method is very powerful in
suppressing the blurry artifacts, and the results also indicate that implicit numerical algorithm with fixed value boundary
condition has better performance in enhancing the contrast of tomosynthesis image, demonstrating the effectiveness of
the proposed filtering method in deblurring the out-of-plane artifacts.

Deblurring of tomosynthesis images using 3D anisotropic diffusion filtering

Robust methods for precise segmentation of breast region or volume from breast X-ray images, including mammogram and tomosynthetic image, is crucial for applications of these medical images. However, this task is challenging because the acquired images not only are inherent noisy and inhomogeneous, but there are also connected or overlapped artifacts, or noises on the images as well, due to local volume effect of tissues, parametric resolutions and other physical limitations of the imaging device. This paper proposes and develops robust fuzzy c-means (FCM) segmentation methods for segmentation of breast region on breast x-ray images, including mammography and tomosynthesis, respectively. We develop spatial information- and kernel function- based FCM methods to differentiate breast area or breast volume. Spatial information based FCM method incorporates neighborhood pixels' intensities into segmentation because neighbored pixels on an image are highly correlated. Kernel based FCM algorithm is developed by transforming pixel intensity using kernel functions to better improve segmentation performance. The proposed segmentation methods are implemented on mammograms and tomosynthetic images and compared with conventional FCM results. Experiment results demonstrate the proposed segmentation methods are much better compared with traditional FCM method, and are more robust to noises. The developed kernel and spatial based FCM method will be applied for differentiation of breast density and abnormal regions within the breast region to examine its performance in reducing false positive segmentations.

Robust segmentation using kernel and spatial based fuzzy c-means methods on breast x-ray images

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

Deblurring of tomosynthesis images using 3D anisotropic diffusion filtering

Robust segmentation using kernel and spatial based fuzzy c-means methods on breast x-ray images