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Super-resolution MRI through Deep Learning
Qing Lyu, Chenyu You, Hongming Shan +1
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics. Due to hardware, physical and physiological limitations, acquisition of high-resol…
Visual Attention Network for Low Dose CT
Wenchao Du, Hu Chen, Peixi Liao +3
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intr…
Quantitative MRI: Absolute T1, T2 and Proton Density Parameters from Deep Learning
Qing Lyu, Ge Wang
Quantitative MRI is highly desirable in terms of intrinsic tissue parameters such as T1, T2 and proton density. This approach promises to minimize diagnostic variability and differ…
Projection Decomposition for Dual-energy Computed Tomography
Wenxiang Cong, Daniel Harrison, Yan Xi +1
Dual-energy computed tomography (CT) is to reconstruct images of an object from two projection datasets generated from two distinct x-ray source energy spectra. It can provide more…
Neural-networks-based Photon-Counting Data Correction: Pulse Pileup Effect
Ruibin Feng, David Rundle, Ge Wang
Compared with the start-of-art energy integration detectors (EIDs), photon-counting detectors (PCDs) with energy discrimination capabilities have demonstrated great potentials in v…