Previous work ͓Burgess et al., Med. Phys. 28, 419-437 ͑2001͔͒ has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent ͑␤͒ of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography ͑bCT͒ images, which can be thought of as thin sections through a three-dimensional ͑3D͒ volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be ␤ section = ␤ proj − 1. They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess' findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from 0.07 to 0.45 cyc/ mm, where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess' published data for projection mammography and for the limited set of mammography data available from the authors' patient sample. Exponents from the segmented bCT images ͑average value: 2.06͒ were systematically slightly higher than bCT images, with substantial correlation between the two ͑r = 0.84͒.

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Purpose: To compare the statistical properties of the anatomical noise present in breast images. Images from a dedicated breast CT scanner are compared with mammographic projection images. Method and Materials: A dedicated breast CT (bCT) scanner was used to image the breasts of volunteers and patients. Twenty‐five of the available 105 patient datasets were selected for analysis. Noise power spectra (NPS) calculations were performed on the datasets for the right breast of the 25 patients in order to examine the statistical nature of the anatomical background of the breast. The projection images acquired at 80 kVp during the cone beam breast CT acquisition were analyzed using NPS calculations. Approximately 50 projection images and 50 bCT slice images were analyzed per patient. Three regions of interest (ROIs) were used per image. The ROIs were randomly distributed within the boundary of the breast. The 2D NPS was calculated for each ROI and the average 2D NPS was computed and averaged radially yielding a 1D NPS. A power law expression of the form αf−β was computed from the results of the projection and bCT images, and the β values were compared. Theoretical development suggests that the β for bCT should be one less than that of the projection images, i.e. β [bCT] = β [proj] − 1. Results: The β values for the bCT slice images were consistently less than those for the projection images. The difference ranged from 0.90 to 2.15, with the average difference being 1.34. The β values for projection images were consistent with the 2.8 value described by Burgess (2001). Conclusion: Lower β values from the NPS results of the bCT images could quantitatively indicate potential improvement in detection ability. More work is needed to conclusively say if this is the case.

Purpose: To evaluate changes in noise equivalent quanta (NEQ) as technique factors are varied in CT, and to evaluate NPS, MTF, and NEQ as standard quantitative metrics for CT image quality assessment. Method and Materials: The modulation transfer function [MTF(f)] of a GE lightspeed16, clinical CT scanner was measured. A 13μm, nickel‐chromium wire was scanned at 120kVp and 400mAs to obtain a point spread function (PSF). Three reconstruction filters were evaluated. The measured PSF was integrated to obtain the line spread function (LSF), and the MTF(f) was then computed. Ten CT scans of a 20 cm diameter water‐filled pipe were also acquired to measure the noise power spectra [NPS(f)] on the same scanner. The mAs was varied from 10 to 400mAs at constant kVp, and the kVp was varied from 80 to 140kVp at constant mAs. Images using each of the reconstruction filters were evaluated. Using N × N regions of interest (ROI) the NPS(f) was computed by calculating the 2D FFT of each ROI and averaging all of the magnitude, squared FFT results. A minimum of 64 ROI were used per volume, and different ROI placement distributions were evaluated. The 2D results were averaged radially to obtain the 1D NPS(f). The NEQ(f) was computed by dividing the MTF2(f) by the NPS(f). Results: The MTF(f) measurement is straightforward and the NPS(f) metric for CT images demonstrates behavior consistent with trends in photon fluence and apodizing filter behavior. The NPS(f) and NEQ(f) curves demonstrated sensitive changes with kVp, mAs, and slice thickness. Conclusion: While NPS(f) and NEQ(f) normalization issues exist due to the normalization of CT images into Hounsfield Units, these metrics are sensitive to changes in technique and slice thickness changes and therefore are strong candidates for routine quantitative assessment of CT image quality.