Synthetic breast phantoms from patient based eigenbreasts

Abstract

Purpose The limited number of 3D patient-based breast phantoms available could be augmented by synthetic breast phantoms in order to facilitate virtual clinical trials using model observers for breast imaging optimization and evaluation. Methods These synthetic breast phantoms were developed using Principal Component Analysis (PCA) to reduce the number of dimensions needed to describe a training set of images. PCA decomposed a training set of M breast CT volumes (with millions of voxels each) into an M-1-dimensional space of eigenvectors, which we call eigenbreasts. Each of the training breast phantoms was compactly represented by the mean image plus a weighted sum of eigenbreasts. The distribution of weights observed from training was then sampled to create new synthesized breast phantoms. Results The resulting synthesized breast phantoms demonstrated a high degree of realism, as supported by an observer study. Two out of three experienced physicist observers were unable to distinguish between the synthesized breast phantoms and the patient-based phantoms. The fibroglandular density and noise power law exponent of the synthesized breast phantoms agreed well with the training data. Conclusions Our method extends our series of digital breast phantoms based on breast CT data, providing the capability to generate new, statistically varying ensembles consisting of tens of thousands of virtual subjects. This work represents an important step towards conducting future virtual trials for task-based assessment of breast imaging, where it is vital to have a large ensemble of realistic phantoms for statistical power as well as clinical relevance.

Dataset of patient‐derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography

“…In a study at Duke University, 41,55 a population of 224 breast phantoms was produced based on in vivo BCT data for imaging research acquired at University of California Davis. There is a difference in the number of produced phantoms because in the work of Erickson they used 184 unilateral (left or right) breast data from 184 different subjects, and 40 BCT volumes from 20 subjects.…”

Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

“…Recent efforts have established that computational methods can model many of the characteristics of breast anatomy and pathology 10,11 as well as the physics of imaging. 12,13 In addition, image interpretation algorithms have been shown to track the performance of human readers for specific visual tasks.…”

“…In the last decade, powerful, efficient, and open-source radiation imaging system simulation tools have become freely available, allowing for a greater understanding of design choices. Recent efforts have established that computational methods can model many of the characteristics of breast anatomy and pathology as well as the physics of imaging . In addition, image interpretation algorithms have been shown to track the performance of human readers for specific visual tasks .…”

Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering—A Topical Review

“…Segmentation of CT images allows for accurate representation of large scale breast anatomy, but CT image resolution does not currently allow for segmentation of fine structures such as ligaments that are visible in higher resolution mammographic and DBT systems. The size of the virtual population is also limited to the number of CT data sets available, though some efforts have been made to solve this problem with eigenfunction analysis [194]. Procedural generation on the other hand allows for a virtual patient population of arbitrary size at an arbitrary voxel resolution, but current phantoms generated in this manner have limitations in terms of realism.…”