Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312505780> ?p ?o ?g. }
- W4312505780 endingPage "12" @default.
- W4312505780 startingPage "1" @default.
- W4312505780 abstract "Dual-energy computed tomography (DECT) can simultaneously provide the anatomical structure and material-specific information of the scanned object, having many applications in industry and medicine. Different from conventional CT, DECT acquires two attenuation measurements of the same object at two different X-ray spectra, resulting in apparent redundant information. This article exploits this kind of redundancy to develop the self-prior information enhanced deep iterative reconstruction (SPIE-DIR) algorithm for limited-angle DECT. Unlike the routine practice in model-based deep learning (DL) algorithms, the SPIE-DIR method simultaneously performs constraints in the projection, residual, and image domains, corresponding to three modules: projection inpainting, residual correction, and image refinement. During this stage, the prior image and prior projection derived from two complementary limited-angle scans are used to improve the algorithm performance. Besides, to avoid the blurring effect caused by minimizing the Euclidean distance, the Wasserstein generative adversarial network with gradient penalty is adopted to enhance the visual perception of the generated results. Experiments on the simulated data and real rat data have demonstrated that the proposed SPIE-DIR algorithm has the potential to obtain high-quality DECT images from two limited-angle scans. Furthermore, visual and quantitative assessments have shown the promising performance of SPIE-DIR in artifact removal, structural fidelity, CT number preservation, and visual perception enhancement." @default.
- W4312505780 created "2023-01-05" @default.
- W4312505780 creator A5001434376 @default.
- W4312505780 creator A5033344283 @default.
- W4312505780 creator A5040249442 @default.
- W4312505780 creator A5041840380 @default.
- W4312505780 creator A5062289182 @default.
- W4312505780 creator A5062682047 @default.
- W4312505780 creator A5065509394 @default.
- W4312505780 creator A5087825515 @default.
- W4312505780 date "2023-01-01" @default.
- W4312505780 modified "2023-10-14" @default.
- W4312505780 title "SPIE-DIR: Self-Prior Information Enhanced Deep Iterative Reconstruction Using Two Complementary Limited-Angle Scans for DECT" @default.
- W4312505780 cites W1926920987 @default.
- W4312505780 cites W1964728954 @default.
- W4312505780 cites W1996180067 @default.
- W4312505780 cites W2034291124 @default.
- W4312505780 cites W2041998362 @default.
- W4312505780 cites W2082264159 @default.
- W4312505780 cites W2153148720 @default.
- W4312505780 cites W2157812230 @default.
- W4312505780 cites W2171697262 @default.
- W4312505780 cites W2293166549 @default.
- W4312505780 cites W2322628404 @default.
- W4312505780 cites W2514903487 @default.
- W4312505780 cites W2521861490 @default.
- W4312505780 cites W2524887677 @default.
- W4312505780 cites W2584483805 @default.
- W4312505780 cites W2611467245 @default.
- W4312505780 cites W2743780012 @default.
- W4312505780 cites W2757145531 @default.
- W4312505780 cites W2790924120 @default.
- W4312505780 cites W2793176558 @default.
- W4312505780 cites W2793419304 @default.
- W4312505780 cites W2796256498 @default.
- W4312505780 cites W2803086176 @default.
- W4312505780 cites W2803224943 @default.
- W4312505780 cites W2912941975 @default.
- W4312505780 cites W2946539594 @default.
- W4312505780 cites W2963392702 @default.
- W4312505780 cites W2963446712 @default.
- W4312505780 cites W2965761874 @default.
- W4312505780 cites W2979647534 @default.
- W4312505780 cites W2991279275 @default.
- W4312505780 cites W2995983474 @default.
- W4312505780 cites W2997053073 @default.
- W4312505780 cites W2997534433 @default.
- W4312505780 cites W3011252275 @default.
- W4312505780 cites W3037686233 @default.
- W4312505780 cites W3047424341 @default.
- W4312505780 cites W3088729822 @default.
- W4312505780 cites W3089678347 @default.
- W4312505780 cites W3093199996 @default.
- W4312505780 cites W3093553540 @default.
- W4312505780 cites W3093900383 @default.
- W4312505780 cites W3098281398 @default.
- W4312505780 cites W3099147628 @default.
- W4312505780 cites W3101114211 @default.
- W4312505780 cites W3103586216 @default.
- W4312505780 cites W3104004244 @default.
- W4312505780 cites W3112965401 @default.
- W4312505780 cites W3118448056 @default.
- W4312505780 cites W3129640775 @default.
- W4312505780 cites W3131131418 @default.
- W4312505780 cites W3132065658 @default.
- W4312505780 cites W3133908690 @default.
- W4312505780 cites W3134762862 @default.
- W4312505780 cites W3146850382 @default.
- W4312505780 cites W3163294221 @default.
- W4312505780 cites W3172797549 @default.
- W4312505780 cites W3178655650 @default.
- W4312505780 cites W3180514382 @default.
- W4312505780 cites W3182558064 @default.
- W4312505780 cites W3186799329 @default.
- W4312505780 cites W3191007476 @default.
- W4312505780 cites W3193594030 @default.
- W4312505780 cites W3193690029 @default.
- W4312505780 cites W3194465724 @default.
- W4312505780 cites W3198380580 @default.
- W4312505780 cites W3199139569 @default.
- W4312505780 cites W3203397703 @default.
- W4312505780 cites W3203678351 @default.
- W4312505780 cites W3208671773 @default.
- W4312505780 cites W3216759837 @default.
- W4312505780 cites W4200229112 @default.
- W4312505780 cites W4210704094 @default.
- W4312505780 cites W4214827844 @default.
- W4312505780 cites W4221034033 @default.
- W4312505780 cites W4223618436 @default.
- W4312505780 cites W4226400001 @default.
- W4312505780 cites W4254142313 @default.
- W4312505780 cites W4283269789 @default.
- W4312505780 cites W4306407394 @default.
- W4312505780 cites W2992663638 @default.
- W4312505780 doi "https://doi.org/10.1109/tim.2022.3227549" @default.
- W4312505780 hasPublicationYear "2023" @default.
- W4312505780 type Work @default.
- W4312505780 citedByCount "1" @default.