Cone-beam Breast Computed Tomography Research Articles

X-ray Cone-Beam Breast Computed Tomography: Phantom Studies

We present the design and preliminary imaging evaluation of a bench-top apparatus for X-ray cone-beam breast computed tomography (CBBCT), assembled for technical evaluation and for laboratory tests of various optimization techniques for CBBCT. The prototype is characterized by the computer control of the X-ray tube (W anode, 35-80 kVp, 0.25 mA, 50 n?m focal spot size), CsI:Tl CMOS flat panel detector (12t12ncm2 area, 50n?m pitch, up to 9 fps at 4ntn 4 binning), motorized translation and rotation stages with eight degrees of freedom, and fan-beam or cone-beam backprojection software. First cone-beam tomographic images of two 14-cm diameter hemiellipsoidal PMMA breast phantoms are presented.

Composite modulation transfer function evaluation of a cone beam computed tomography breast imaging system

Several factors during the scanning process, image reconstruction and geometry of an imaging system, influence the spatial resolution of a computed tomography imaging system. In this work, the spatial resolution of a state of the art flat panel detector-based cone beam computed tomography breast imaging system is evaluated. First, scattering, exposure level, voltage, voxel size, pixel size, back-projection filter, reconstruction algorithm, and number of projections are varied to evaluate their effect on spatial resolution. Second, its uniformity throughout the whole field of view is evaluated as a function of radius along the x-y plane and as a function of z at the center of rotation. The results of the study suggest that the modulation transfer function is mainly influenced by the pixel, back-projection filter, and number of projections used. The evaluation of spatial resolution throughout the field of view also suggests that this imaging system does have a 3-D quasi-isotropic spatial resolution in a cylindrical region of radius equal to 40 mm centered at the axis of rotation. Overall, this study provides a useful tool to determine the optimal parameters for the best possible use of this cone beam computed tomography breast imaging system.

Noise power properties of a cone‐beam CT system for breast cancer detection

The noise power properties of a cone-beam computed tomography (CT) system dedicated for breast cancer detection were investigated. Uniform polyethylene cylinders of various diameters were scanned under different system acquisition conditions. Noise power spectra were calculated from difference data generated by subtraction between two identical scans. Multidimensional noise power spectra (NPS) were used as the metric to evaluate the noise properties of the breast CT (bCT) under different system acquisition and reconstruction conditions. A comprehensive investigation of the noise properties was performed in regard to system acquisition parameters including kVp, mA, number of cone-beam projection images used, cone angle, and object size. The influence on reconstruction parameters including interpolation method, reconstruction filter, field of view, matrix size, and slice thickness were also studied. Under certain conditions, the zero-dimensional NPS (image variance) was used as a quantitative index to compare the influence from different scan parameters, especially the radiation dose. If the total scan dose is changed by linearly changing the total number of projection images while the dose per frame is kept constant, the noise power has a linear relationship with the reciprocal of the total dose. If the total scan dose is changed by linearly changing the dose per frame while the total number of projection images is kept constant, the noise power has a quadratic relationship with the reciprocal of the total dose. With the same amount of total dose, using fewer projection images results in lower image noise power in the CT image. Quantitative results from this noise power analysis provide guidance for the bCT system operation, optimization, and data reconstruction.

A post‐reconstruction method to correct cupping artifacts in cone beam breast computed tomography

In cone beam breast computed tomography (CT), scattered radiation leads to nonuniform biasing of CT numbers known as a cupping artifact. Besides being visual distractions, cupping artifacts appear as background nonuniformities, which impair efficient gray scale windowing and pose a problem in threshold based volume visualization/segmentation. To overcome this problem, we have developed a background nonuniformity correction method specifically designed for cone beam breast CT. With this technique, the cupping artifact is modeled as an additive background signal profile in the reconstructed breast images. Due to the largely circularly symmetric shape of a typical breast, the additive background signal profile was also assumed to be circularly symmetric. The radial variation of the background signals was estimated by measuring the spatial variation of adipose tissue signals in front view breast images. To extract adipose tissue signals in an automated manner, a signal sampling scheme in polar coordinates and a background trend fitting algorithm were implemented. The background fits compared with targeted adipose tissue signal value (constant throughout the breast volume) to get an additive correction value for each tissue voxel. To test the accuracy, we applied the technique to cone beam CT images of mastectomy specimens. After correction, the images demonstrated significantly improved signal uniformity in both front and side view slices. The reduction of both intraslice and interslice variations in adipose tissue CT numbers supported our observations.

SU-FF-I-01: 3D Computer-Aided Detection of Masses and Micro-Calcifications From Cone Beam CT Scans: A Breast Phantom Study

Purpose: Cone Beam Breast Computed Tomography (CBBCT) has emerged as a promising new modality for detection and diagnosis of breast cancer. Compared to conventional X‐ray mammography, CBBCT provides much greater detail of breast tissue in three‐dimensions (3D) as well as improved patient comfort. However, these benefits come at the cost of hundreds of image slices for each scan. Here, a validation study was performed using CBBCT data of a breast phantom with known nodules and micro‐calcifications. A novel 3D computer‐aided detection algorithm based on 3D template‐matching was applied to this dataset to evaluate the performance of CAD‐CBBCT. Method and Materials: The CBBCT dataset of the breast phantom consisted of 371 slices with isotropic resolution of 0.27 mm. The central 250 contiguous slices were used in our analysis. The selected imaging volume contained 8 masses with diameter ranging from 0.5–10mm (appearing on 9–37 pixels), and 13 micro‐calcifications with diameter ranging from 0.2–0.5 mm (appearing on 3–5 pixels). Our CAD algorithm models masses and micro‐calcifications as spheres (templates) of varying sizes and searches for structures similar to the templates throughout the entire imaging volume. Templates of 9 different sizes were applied. The end result is a single index (the normalized cross correlation coefficient) for each mass and calcification candidate. An optimal correlation threshold for each template size was then selected to best identify the nodules and micro‐calcifications. Results: 8 out of 8 masses down to 0.5 mm and 12 out of 13 micro‐calcifications down to 0.2 mm were successfully detected, with only 1 false positive finding. The processing time for each template was ∼80 seconds (on a Mac Pro with 2.66GHz CPU, 5G memory). Conclusion: CBBCT and 3D CAD were able to detect in a breast imaging phantom very small nodules and micro‐calcifications with high sensitivity and specificity.

Why should breast tumour detection go three dimensional?

Although x-ray mammography is widely developed for breast tumour detection, it suffers from spatial superposition in its two-dimensional (2D) representation of a three-dimensional (3D) breast structure. Accordingly, 3D breast imaging, such as cone-beam computed tomography (CT), arises at the historic moment. In this paper, we theoretically elucidate the spatial superposition effect associated with x-ray mammography on breast tumour detection. This explanation is based on the line integral of x-ray traversing a composite breast model. As a result, we can characterize the difficulty of detecting small tumours in terms of local intensity contrast in x-ray images. In comparison, we also introduce cone-beam CT breast imaging for 3D breast volume representation, which offers advantages for breast mass segmentation and measurement. The discussion is demonstrated with an experiment with a breast surgical specimen. In conclusion, we strongly believe that 3D volumetric representation allows for more accurate breast tumour detection.