ABSTRACT
<p><b>OBJECTIVE</b>To investigate the effect of detector performance during digital breast tomography (DBT) projection data acquisition on reconstructed image quality.</p><p><b>METHODS</b>With reference to the traditional detector data correction method and the specific data acquisition pattern in DBT imaging, we utilized dark field correction, light field and its gain correction for processing the projection data collected by the detector. The reconstructed images were evaluated using iterative reconstruction method based on total generalized variation (TGV).</p><p><b>RESULTS</b>In physical breast phantom experiment, the proposed method resulted in a reduced Heel effect caused by nonuniform photon number. The reconstructed DBT images after correction showed obviously improved image quality especially in the details with a low contrast.</p><p><b>CONCLUSION</b>The dark field correction, light field and its gain correction process for DBT image reconstruction can improve the image quality.</p>
ABSTRACT
In clinical cerebral perfusion CT examination, repeated scanning the region of interest in the cine mode increases the radiation dose of the patients, while decreasing the radiation dose by lowering the scanning current results in poor image quality and affects the clinical diagnosis. We propose a penalized weighted least-square (PWLS) method for recovering the projection data to improve the quality of low-dose cerebral perfusion CT imaged. This method incorporates the statistical distribution characteristics of brain perfusion CT projection data and uses the statistical properties of the projection data for modeling. The PWLS method was used to recover the data, and the Gauss-Seidel (GS) method was employed for iterative solving. Adaptive weighting is introduced between the original projection data and the projection data after PWLS restoration. The experimental results on the clinical data demonstrated that the PWLS-based sinogram restoration method improved noise reduction and artifact suppression as compared with the conventional noise reduction methods, and better retained the edges and details to generate better cerebral perfusion maps.