A Quantification Method for Breast Tissue Thickness and Iodine Concentration Using Photon-Counting Detector

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

Purpose

The 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.

Methods

A 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).

Results

As 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.

Conclusions

The 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.

Susceptibility of iodine concentration map of dual-energy contrast-enhanced digital mammography for quantitative and tumor enhancement assessment

Background Dual-energy (DE) contrast-enhanced digital mammography (DE-CEDM) provides additional information on tumor angiogenesis. Purpose To investigate the susceptibility of reconstructing color-coded iodine concentration maps on the basis of quantitative calibrations of the iodine concentration and contrast-to-noise ratio (CNR) in DE-CEDM applications. Material and Methods A custom-made phantom filled with iodine concentrations in the range of 0.1-10 mg/cm² was used in calibrations. All DE images were acquired using the GE Senographe Essential system. From DE subtraction images, the image contrast and CNR were obtained, and the quantitative relationship between these two metrics and the iodine concentration at each phantom thickness was investigated. The quantitative CNR calibration curves were applied to reconstruct color-coded iodine maps on a pixel-by-pixel basis. Results Both the mean contrast and mean CNR increased linearly with the iodine concentration. The iodine concentration estimated from the iodine map reconstructed from quantitative CNR calibrations was highly consistent with the desired iodine concentration (R²= 0.989), and smaller relative errors (in the range of 3.0-19.5%) were observed with iodine concentrations not less than 1 mg/cm². Conclusion An iodine concentration map could be reconstructed based on the linear relationship between the CNR and iodine concentration. From the color-coded iodine concentration map, the contrast medium enhancement phenomenon could be further estimated quantitatively, and tumor enhancement patterns could be easily observed.