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Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statist
In breast cancer screening, the radiation dose must be kept to the minimum necessary to achieve the desired diagnostic objective, thus minimizing risks associated with cancer induction. However, decreasing the radiation dose also degrades the image quality. In this work we restore digital breast tomosynthesis (DBT) projections acquired at low radiation doses with the goal of achieving a quality co
This work presents a data-driven model to simulate the mechanical behavior of the breast tissues in real time. The aim of this model is to speed up some multimodal registration algorithms, as well as some image-guided interventions. Ten virtual breast phantoms were used in this work. Their deformation during a mammography was performed off-line using the finite element method. Three machine learni
This paper proposes a new method of simulating dose reduction in digital breast tomosynthesis, starting from a clinical image acquired with a standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the method accounts for pixel crosstalk, which causes the noise to be frequency-dependent, thus increasing the simulation accuracy. For
Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such
A modification to our previous simulation of breast anatomy is proposed to improve the quality of simulated x-ray projections images. The image quality is affected by the voxel size of the simulation. Large voxels can cause notable spatial quantization artifacts; small voxels extend the generation time and increase the memory requirements. An improvement in image quality is achievable without redu
This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues
Due to the limited number of views and limited angular span in digital breast tomosynthesis (DBT), the acquisition geometry design is an important factor that affects the image quality. Therefore, intensive studies have been conducted regarding the optimization of the acquisition geometry. However, different reconstruction algorithms were used in most of the reported studies. Because each type of
The exact asymptotic computational complexity for a problem of indexing cells on a uniform grid intersecting with a union of C(1) surfaces has been proven. The computational complexity of the recursive partition indexing algorithm, utilized for simulation of septated tissues, is derived and the algorithm is demonstrated as being asymptotically optimal.
Digital breast tomosynthesis (DBT) is a promising technique to overcome the tissue superposition limitations found in planar 2D x-ray mammography. However, as most DBT systems do not employ an anti-scatter grid, the levels of scattered radiation recorded within the image receptor are significantly higher than that observed in planar 2D x-ray mammography. Knowledge of this field is necessary as par
Early detection of risk of breast cancer is of upmost importance for effective treatment. In the field of medical image analysis, automatic methods have been developed to discover features of ductal trees that are correlated with radiological findings regarding breast cancer. In this study, a data mining approach is proposed that captures a new set of geometrical properties of ductal trees. The ex
Purpose: Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our und
Purpose: The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical t
Purpose: Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibrog
Purpose:To correlate the parenchymal texture features at digital breast tomosynthesis(DBT)and digital mammography with breast percent density(PD), an established breast cancer risk factor, in a screening population of women. Materials and Methods: This HIPAA-compliant study was approved by the institutional review board. Bilateral DBT images and digital mammograms from 71 women (mean age, 54 years
Purpose: We present a novel algorithm for computer simulation of breast anatomy for generation of anthropomorphic software breast phantoms. A realistic breast simulation is necessary for preclinical validation of volumetric imaging modalities.Methods: The anthropomorphic software breast phantom simulates the skin, regions of adipose and fibroglandular tissue, and the matrix of Cooper's ligaments a
Purpose: Develop a technique to fabricate a 3D anthropomorphic breast phantom with known ground truth for image quality assessment of 2D and 3D breast x-ray imaging systems. Methods: The phantom design is based on an existing computer model that can generate breast voxel phantoms of varying composition, size, and shape. The physical phantom is produced in two steps. First, the portion of the voxel
Purpose: To evaluate inter- and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images. Materials and Methods: This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammog
We propose a multistep approach for representing and classifying tree-like structures in medical images. Tree-like structures are frequently encountered in biomedical contexts; examples are the bronchial system, the vascular topology, and the breast ductal network. We use tree encoding techniques, such as the depth-first string encoding and the Prüfer encoding, to obtain a symbolic string represen
Rationale and Objectives: Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture vis
