Modeling, validation and application of a mathematical tissue-equivalent breast phantom for linear slot-scanning digital mammography

Development of a computational tool for estimating computed tomography dose parameters

BACKGROUND

Computed Tomographic (CT) imaging procedures have been reported as the main source of radiation in diagnostic procedures compared to other modalities. To provide the optimal quality of CT images at the minimum radiation risk to the patient, periodic inspections and calibration tests for CT equipment are required. These tests involve a series of measurements that are time consuming and may require specific skills and highly-trained personnel.

OBJECTIVE

This study aims to develop a new computational tool to estimate the dose of CT radiation outputs and assist in the calibration of CT scanners. It may also provide an educational resource by which radiological practitioners can learn the influence of technique factors on both patient radiation dose and the produced image quality.

METHODS

The computational tool was developed using MATLAB in order to estimate the CT radiation dose parameters for different technique factors. The CT radiation dose parameters were estimated from the calibrated energy spectrum of the x-ray tube for a CT scanner.

RESULTS

The estimated dose parameters and the measured values utilising an Adult CT Head Dose Phantom showed linear correlations for different tube voltages (80 kVp, 100 kVp, 120 kVp, and 140 kVp), with R2 nearly equal to 1 (0.99). The maximum differences between the estimated and measured CTDIvol were under 5 %. For 80 kVp and low tube currents (50 mA, 100 mA), the maximum differences were under 10%.

CONCLUSIONS

The prototyped computational model provides a tool for the simulation of a machine-specific spectrum and CT dose parameters using a single dose measurement.

Testing a dual-modality system that combines full-field digital mammography and automated breast ultrasound

PURPOSE

The aim of this study was to test a novel dual-modality imaging system that combines full-field digital mammography (FFDM) and automated breast ultrasound (ABUS) in a single platform. Our Aceso system, named after the Greek goddess of healing, was specifically designed for the early detection of cancer in women with dense breast tissue.

MATERIALS AND METHODS

Aceso was first tested using two industry standards: a Contrast Detail Mammography (CDMAM) phantom as endorsed by European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services was used to assess the FFDM images; and the CIRS 040GSE ultrasound phantom was imaged to evaluate the quality of the ABUS images. In addition, 58 women participated in a clinical trial: 51 were healthy volunteers aged between 40 and 65, while 7 were patients referred by the breast clinic, 6 of whom had biopsy-proven breast cancer.

RESULTS

The CDMAM tests showed that the FFDM results were "acceptable" but fell short of "achievable" which was attributed to the low dose used. The ABUS images had good depth penetration (80 mm) and adequate axial resolution (0.5 mm), but the lateral resolution of 2 mm was judged to be too coarse. In a 42-year-old volunteer with extremely dense breast tissue, the ABUS modality detected a lesion (a benign cyst) that was mammographically occult in the FFDM image. For a 73-year-old patient with fatty breasts, a malignant lesion was successfully detected and co-registered in the FFDM and ABUS images. On average, each woman spent less than 11 min in the acquisition room.

CONCLUSIONS

While there is room for improvement in the quality of both the FFDM and ABUS images, Aceso has demonstrated its ability to acquire clinically meaningful images for a range of women with varying breast densities and, therefore, has potential as a screening device.

Monte-Carlo simulation of a slot-scanning digital mammography system for tomosynthesis

BACKGROUND

Digital breast tomosynthesis (DBT) reconstructs planar slices of the breast based on two-dimensional angular projections. Early studies and clinical trials show that DBT is an improvement over full field digital mammography (FFDM) because it provides the radiologist with better image quality and more information.

OBJECTIVE

This paper presents a simulation system to model the performance of a slot-scanning FFDM and DBT system.

METHODS

A tissue-equivalent three dimensional (3D) breast phantom was constructed, validated for slot-scanning digital mammography and used in simulating digital breast tomosynthesis. The simulation system was validated by comparing images acquired with a slot-scanning mammography machine with simulated phantom images, using the edge-test method and image quality metrics modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE). Different two-dimensional (2D) projections of the 3D phantom were simulated and the phantom was reconstructed using filtered backprojection.

RESULTS

Image quality metrics showed equivalence between simulated and real images.

CONCLUSIONS

The simulation tool is suitable for slot-scanning FFDM and DBT and may be used for the design and comparison of mammography systems.

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

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 understanding of DBT system optimization and validate published design principles.

METHODS

The authors developed a virtual trial framework for task-specific DBT assessment that uses digital phantoms, open-source x-ray transport codes, and a projection-space, spatial-domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly-varying anatomical backgrounds. Detectability was measured by the area under the receiver-operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability-limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages.

RESULTS

The large number of variables in a virtual DBT study make it difficult to directly compare different authors' results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm(3) voxels (larger 500 μm(2) detector pixels were used to avoid voxel-edge artifacts): 1. For raw, unfiltered projection images in the anatomical-variability-limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum- and electronic-noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy-limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic-noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT.

CONCLUSIONS

The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task-specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.