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公开(公告)号：US20220327664A1
公开(公告)日：2022-10-13
申请号：US17225395
申请日：2021-04-08
摘要： A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.
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公开(公告)号：US20230048231A1
公开(公告)日：2023-02-16
申请号：US17444881
申请日：2021-08-11
发明人： Rajesh Langoju , Utkarsh Agrawal , Risa Shigemasa , Bipul Das , Yasuhiro Imai , Jiang Hsieh
摘要： Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.
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公开(公告)号：US20240062331A1
公开(公告)日：2024-02-22
申请号：US17821058
申请日：2022-08-19
发明人： Rajesh Langoju , Prasad Sudhakara Murthy , Utkarsh Agrawal , Risa Shigemasa , Bhushan Patil , Bipul Das , Yasuhiro Imai
CPC分类号： G06T3/4046 , G06T7/0012 , G06T5/002 , G06T7/11 , G06N3/08
摘要： Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.
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公开(公告)号：US11823354B2
公开(公告)日：2023-11-21
申请号：US17225395
申请日：2021-04-08
CPC分类号： G06T5/002 , G06N3/08 , G06T11/008 , G16H30/20 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2211/408
摘要： A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.
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公开(公告)号：US20230029188A1
公开(公告)日：2023-01-26
申请号：US17385600
申请日：2021-07-26
发明人： Rajesh Langoju , Utkarsh Agrawal , Bhushan Patil , Vanika Singhal , Bipul Das , Jiang Hsieh
摘要： The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.
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