Fifty-eighth annual meeting of the american association of physicists in medicine
WE-DE-207B-12: Scatter Correction for Dedicated Cone Beam Breast CT Based On a Forward Projection Model
The image quality of dedicated cone-beam breast CT (CBBCT) is fundamentally limited by substantial x-ray scatter contamination, resulting in cupping artifacts and contrast-loss in reconstructed images. Such effects obscure the visibility of soft-tissue lesions and calcifications, which hinders breast cancer detection and diagnosis. In this work, we propose to suppress x-ray scatter in CBBCT images using a deterministic forward projection model.
We first use the 1st-pass FDK-reconstructed CBBCT images to segment fibroglandular and adipose tissue. Attenuation coefficients are assigned to the two tissues based on the x-ray spectrum used for imaging acquisition, and is forward projected to simulate scatter-free primary projections. We estimate the scatter by subtracting the simulated primary projection from the measured projection, and then the resultant scatter map is further refined by a Fourier-domain fitting algorithm after discarding untrusted scatter information. The final scatter estimate is subtracted from the measured projection for effective scatter correction. In our implementation, the proposed scatter correction takes 0.5 seconds for each projection. The method was evaluated using the overall image spatial non-uniformity (SNU) metric and the contrast-to-noise ratio (CNR) with 5 clinical datasets of BI-RADS 4/5 subjects.
For the 5 clinical datasets, our method reduced the SNU from 7.79% to 1.68% in coronal view and from 6.71% to 3.20% in sagittal view. The average CNR is improved by a factor of 1.38 in coronal view and 1.26 in sagittal view.
The proposed scatter correction approach requires no additional scans or prior images and uses a deterministic model for efficient calculation. Evaluation with clinical datasets demonstrates the feasibility and stability of the method. These features are attractive for clinical CBBCT and make our method distinct from other approaches.
Supported partly by NIH R21EB019597, R21CA134128 and R01CA195512.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.