Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique

Abstract

Dedicated breast computed tomography (CT) imaging possesses the potential for improved lesion detection over conventional mammograms, especially for women with dense breasts. The breast CT images are acquired with a glandular dose comparable to that of standard two-view mammography for a single breast. Due to dose constraints, the reconstructed volume has a non-negligible quantum noise when thin section CT slices are visualized. It is thus desirable to reduce noise in the reconstructed breast volume without loss of spatial resolution. In this study, partial diffusion equation (PDE) based denoising techniques specifically for breast CT were applied at different steps along the reconstruction process and it was found that denoising performed better when applied to the projection data rather than reconstructed data. Simulation results from the contrast detail phantom show that the PDE technique outperforms Wiener denoising as well as adaptive trimmed mean filter. The PDE technique increases its performance advantage relative to Wiener techniques when the photon fluence is reduced. With the PDE technique, the sensitivity for lesion detection using the contrast detail phantom drops by less than 7% when the dose is cut down to 40% of the two-view mammography. For subjective evaluation, the PDE technique was applied to two human subject breast data sets acquired on a prototype breast CT system. The denoised images had appealing visual characteristics with much lower noise levels and improved tissue textures while maintaining sharpness of the original reconstructed volume.

Figure 1

Illustration of a dedicated breast CT system. The x-ray tube and flat-panel detector rotate together around the breast, which is the only region to be illuminated.

Figure 2

Illustration of possible steps where image-denoising module may be applied with respect to reconstruction module for dedicated breast CT data. In this study, the denoising techniques are applied at steps 2–4.

Figure 3

One-dimensional line integral profiles across the breast on a projection image. The dashed and continuous plots correspond to noise free case and the case with I₀=2.0e3, respectively. The variance of line integral is larger at the center of breast region and gets lower toward the periphery.

Figure 4

Step comparison at I₀=2.5e4. (a) is a coronal slice of simulated breast with contrast detail phantoms, and (b) shows the same slice with added noise to the projection images. After noise removal, (d) PDE_tomo applied prior to reconstruction generates better images than (c) PDE_3Dpost after reconstruction.

Figure 5

Step comparison at I₀=1.0e4. (b) is noisier than Fig. 4b. As in Fig. 4: (d) PDE_tomo applied at step 2 is better than (c) PDE_3Dpost applied at step 4.

Figure 6

Noise-resolution plot for PDE_tomo, ATM, and Wiener filters at I₀=2.5e4. For a high contrast test object, PDE_tomo and ATM have a lower noise level and higher resolution than Wiener filtering. ATM is the best among the three based on the noise-resolution plot.

Figure 7

One-dimensional profiles of the reconstruction through the center of the high contrast object using ATM filter and PDE_tomo. It is shown that by using ATM filter there is a cupping effect at the lesion center, which is absent from PDE_tomo and Wiener processed ones. The rectangle is where the noise level is computed for noise-resolution plot.

Figure 8

Human subject result No. 1. Top row shows original reconstruction, coronal slices from normal breast. Bottom row shows same slices with PDE_tomo denoising, resulting in remarkably reduced noise levels.

Figure 9

Human subject result No. 2. Top row shows original reconstruction, coronal slices from normal breast. Bottom row shows same slices with PDE_tomo denoising, resulting in markedly reduced noise levels.