A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation.Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy.Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck (p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest (p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis (p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively.Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy

Neural network-based CT image quality enhancement

Iako su CT snimke pogodne za prikaz koštanih struktura, slobodnih zglobnih tijela te topografskog odnosa koštanih i mekotkivnih struktura, njihov rastu´ci broj pove´cava potencijalni rizik od zraˇcenja i postaje razlog zabrinutosti javnosti. Da bi se smanjio potencijalni rizik, CT s niskom dozom zraˇcenja privlaˇci sve ve´cu pozornost, ali smanjivanje koliˇcine zraˇcenja znatno pogoršava kvalitetu CT snimke. U sklopu ovog diplomskog rada prouˇcene su state of the art metode te su implementirane dvije konvolucijske neuronske mreže za unaprje ¯ divanje kvalitete CT snimki. Opisan je postupak predobrade kojim se skup podataka prilagodio svakoj od mreža, opisan je proces uˇcenja te su uspore ¯ deni rezultati njihove evaluacije. Budu´ci da je konvolucijska mreža za unaprje ¯ divanje kvalitete CT snimki koja se temelji na U-net arhitekturi ostvarila bolje rezultate u svakom aspektu evaluacije, predlaže se njeno korištenje za rješavanje zadanog problema. Usporedbom rezultata dobivenih kvantitativnom evaluacijom predložene mreže s rezultatima state of the art metoda može se zakljuˇciti kako im je predložena mreža konkurentnaAlthough CT scans are suitable for displaying bone structures, joints, and the topographic relationship of bone and soft tissue structures, their growing number increases the potential risk of radiation and is a cause for public concern. To reduce the potential risk, low-dose CT is gaining increasing attention, but reducing the amount of radiation significantly degrades the quality of the CT scan. As part of this graduate thesis, state of the art methods were studied and two convolutional neural networks were implemented to improve the quality of CT images. Both data preprocessing, which was used to adapt the dataset to each network, and the learning process were described, and the evaluation results of both networks were compared. As the convolutional network for CT image enhancement based on the U-net architecture has achieved better results in every aspect of the evaluation, its use is proposed as a solution for the given problem. Comparing the results obtained by quantitative evaluation of the proposed network with the results of the state of the art methods, one can conclude that the results closely match

Improving Low-Dose CT Image Using Residual Convolutional Network

International audienceLow-dose CT is an effective solution to alleviate radiation risk to patients, it also introduces additional noise and streak artifacts. In order to maintain a high image quality for low-dose scanned CT data, we propose a post-processing method based on deep learning and using 2-D and 3-D residual convolutional networks. Experimental results and comparisons with other competing methods show that the proposed approach can effectively reduce the low-dose noise and artifacts while preserving tissue details. It is also pointed out that the 3-D model can achieve better performance in both edge-preservation and noise-artifact suppression. Factors that may influence the model performance, such as model width, depth, and dropout, are also examined