Patient‐based 4D digital breast phantom for perfusion contrast‐enhanced breast CT imaging

Abstract

PurposeThe purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging.MethodsA 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement).ResultsAs an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected.ConclusionsThe proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT.

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials

“…Alternative methods clearly exist, such as hybrid VCTs 42,43 in which lesions are added to clinical images, but this presupposes that the novel system already exists. Similarly, alterative phantoms which simulate breast anatomy exist 44–47 . In both of these cases, we believe that the methods presented here (voxel replacement and voxel addition) would still be beneficial.…”

“…Similarly, alterative phantoms which simulate breast anatomy exist. [44][45][46][47] In both of these cases, we believe that the methods presented here (voxel replacement and voxel addition) would still be beneficial.…”

Development of 3D patient-based super-resolution digital breast phantoms using machine learning

“…Breast CT can produce isotropic 3D images of the breast, being optimized, in terms of acquisition geometry and spatial and contrast resolution, for breast imaging requirements (Lindfors et al 2008, Kalender et al 2017). However, research and development in breast CT is ongoing, especially for the optimization of dynamic contrast enhanced (DCE) breast CT (Prionas et al 2010, He et al 2016, Caballo et al 2018a), which might have the potential to replace MRI for certain clinical applications. To aid in this optimization work, computer simulations, both deterministic and stochastic, of the image acquisition process are an invaluable tool to allow for the improvement and validation of an acquisition system before it is built or for further optimization of an existing system.…”

“…To aid in this optimization work, computer simulations, both deterministic and stochastic, of the image acquisition process are an invaluable tool to allow for the improvement and validation of an acquisition system before it is built or for further optimization of an existing system. For the former case, the methods described in this present work can be applied to a 4D phantom we previously developed (Caballo et al 2018a) for simulating perfusion contrast-enhanced breast CT imaging. For this, working with patient-based phantoms deriving from a clinical breast CT system is essential to evaluate the effect of the iodinated contrast medium within the whole breast volume, in order to optimize the acquisition settings based on a wide population.…”

“…Others, based on empirical data, are based on the acquisition of images of breast specimens, therefore improving the realism but limiting the inter-patient variability (Hoeschen et al 2005). Finally, others are based on patient-images acquired with breast CT (Caballo et al 2018a, Li et al 2009, Hsu et al 2013). These phantoms have the advantage of including the realism and variability of actual patient data, but their accuracy is limited by the spatial resolution of the breast CT device.…”