Phantom and Preclinical Studies for Image Improvement in Clinical CT

A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach RSEMD that operates on digital imaging and communications in medicine images to improve the quality of clinical CT images is presented. The RSEMD method was tested on Catphan 500 and anthropomorphic 4-D XCAT phantoms to determine the improvements in signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The method was applied to preclinical CT images previously reconstructed by conventional software. To test the potential improvement in clinically relevant CT images we employed the 4-D XCAT phantom to simulate a small, low contrast lesion placed in the liver. In all of the phantom studies, the images proved to have higher resolution and lower noise as compared with filtered back projection. In general, the iterative deblurring restoration reaches the highest SNR and CNR values after approximately 20 iterations with an improvement factor of about 1.5 for both CNR and SNR in noisy CT images. We also found improvements in preclinical and clinical CT images after the application of RSEMD. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms which start in projection space and then follow in image domain. The RSEMD method can be applied to suboptimal routine-dose clinical CT images to improve the image quality to diagnostically acceptable levels.