Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method.

BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

Hybrid Optimization Method (HOM) Reconstruction with limited angle in Dual Energy Breast CT.

Limited angle Breast Computed Tomography uses lower energy and low projection angle to detect early breast cancer or other malignant tissues in the breast. The sensitivity of breast CT can be improved by applying dual energy technology. The general challenge which hampers full exploration of dual energy imaging is noise accumulation as a result of spectral overlaps from two different images. The author proposed hybrid optimization method (HOM) which leverages on fast convergence of simultaneous algebraic reconstruction techniques (SART) and good de-noising and artefacts removal of dictionary learning (DL) to minimizes noise in each image of dual energy and then apply decomposition on the noiseless dual data. The HOM algorithm is formulated as optimization problem which find good atoms from the dictionary obtained and dictionary atom are learned from training data set. The reconstructed images which are noise-free are then decomposed using DECT algorithm into two material basis. 2D phantom known as mbat-phantom consisting of two material basis (microcalcification and normal breast tissue) were simulated to test the algorithm. Noisy projection data were also simulated under the same condition by adding poison noise. The performance of the method was evaluated by estimating some image quality indices on reconstructed images and decomposed images. The proposed method shows the highest average structural similarity index map (SSIM) of 0.9987 and 0.9921 and peak signal to-noise ratio (PSNR) of 49.24 and 46.96 for reconstructed image without noise and noisy image respectively. Also, there is a reduction in average standard deviation (STD) error of decomposed image. Our method performs excellently in streak artefact removal and noise suppression which is capable of reconstructing faithful image in presence of noisy data.