Stationary in-vivo grating-enabled micro-CT architecture (sigma)

    公开(公告)号:US11382574B2

    公开(公告)日:2022-07-12

    申请号:US16761543

    申请日:2018-11-06

    Abstract: A stationary in-vivo grating-enabled micro-CT (computed tomography) architecture (SIGMA) system includes CT scanner control circuitry and a number of imaging chains. Each imaging chain includes an x-ray source array, a phase grating, an analyzer grating and a detector array. Each imaging chain is stationary and each x-ray source array includes a plurality of x-ray source elements. Each imaging chain has a centerline, the centerlines of the number of imaging chains intersect at a center point and a first angle between the centerlines of a first adjacent pair of imaging chains equals a second angle between the centerlines of a second adjacent pair of imaging chains. A plurality of selected x-ray source elements of a first x-ray source array is configured to emit a plurality of x-ray beams in a multiplexing fashion.

    Rapid filtration methods for dual-energy X-ray CT

    公开(公告)号:US11337663B2

    公开(公告)日:2022-05-24

    申请号:US16092393

    申请日:2017-04-06

    Abstract: Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

    Training a CNN with pseudo ground truth for CT artifact reduction

    公开(公告)号:US11120551B2

    公开(公告)日:2021-09-14

    申请号:US16201186

    申请日:2018-11-27

    Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

    DETECTION SCHEME FOR X-RAY SMALL ANGLE SCATTERING

    公开(公告)号:US20210080409A1

    公开(公告)日:2021-03-18

    申请号:US16955939

    申请日:2018-12-20

    Abstract: A detection scheme for x-ray small angle scattering is described. An x-ray small angle scattering apparatus may include a first grating and a complementary second grating. The first grating includes a plurality of first grating cells. The complementarity second grating includes a plurality of second grating cells. The second grating is positioned relative to the first grating. A configuration of the first grating, a configuration of the second grating and the relative positioning of the gratings are configured to pass one or more small angle scattered photons and to block one or more Compton scattered photons and one or more main x-ray photons.

    3-D CONVOLUTIONAL AUTOENCODER FOR LOW-DOSE CT VIA TRANSFER LEARNING FROM A 2-D TRAINED NETWORK

    公开(公告)号:US20200349449A1

    公开(公告)日:2020-11-05

    申请号:US16964388

    申请日:2019-01-24

    Abstract: A 3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network is described, A machine learning method for low dose computed tomography (LDCT) image correction is provided. The method includes training, by a training circuitry, a neural network (NN) based, at least in part, on two-dimensional (2-D) training data. The 2-D training data includes a plurality of 2-D training image pairs. Each 2-D image pair includes one training input image and one corresponding target output image. The training includes adjusting at least one of a plurality of 2-D weights based, at least in part, on an objective function. The method further includes refining, by the training circuitry, the NN based, at least in part, on three-dimensional (3-D) training data. The 3-D training data includes a plurality of 3-D training image pairs. Each 3-D training image pair includes a plurality of adjacent 2-D training input images and at least one corresponding target output image. The refining includes adjusting at least one of a plurality of 3-D weights based, at least in part, on the plurality of 2-D weights and based, at least in part, on the objective function. The plurality of 2-D weights includes the at least one adjusted 2-D weight.

    TRAINING A CNN WITH PSEUDO GROUND TRUTH FOR CT ARTIFACT REDUCTION

    公开(公告)号:US20190164288A1

    公开(公告)日:2019-05-30

    申请号:US16201186

    申请日:2018-11-27

    Abstract: Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

    X-RAY DISSECTOGRAPHY
    19.
    发明申请

    公开(公告)号:US20250022210A1

    公开(公告)日:2025-01-16

    申请号:US18714185

    申请日:2022-11-29

    Inventor: Ge Wang Chuang Niu

    Abstract: In one embodiment, there is provided a dissectography module for dissecting a two-dimensional (2D) radiograph. The dissectography module includes an input module, an intermediate module, and an output module. The input module is configured to receive a number K of 2D input radiographs, and to generate at least one three-dimensional (3D) input feature set, and K 2D input feature sets based, at least in part, on the K 2D input radiographs. The intermediate module is configured to generate a 3D intermediate feature set based, at least in part, on the at least one 3D input feature set. The output module is configured to generate output image data based, at least in part, on the K 2D input feature sets, and the 3D intermediate feature set. Dissecting corresponds to extracting a region of interest from the 2D input radiographs while suppressing one or more other structure(s).

    X-optogenetics / U-optogenetics
    20.
    发明授权

    公开(公告)号:US12172030B2

    公开(公告)日:2024-12-24

    申请号:US16943026

    申请日:2020-07-30

    Abstract: Methods and systems for performing optogenetics using X-rays or ultrasound waves are provided. Visible-light-emitting nanophosphors can be provided to a sample, and X-ray stimulation can be used to stimulate the nanophosphors to emit visible light. Alternatively, ultrasonic waves can be provided to the sample to cause sonoluminescence, also resulting in emission of visible light, and this can be aided by the use of a chemiluminescent agent present in the sample. The emitted light can trigger changes in proteins that modulate membrane potentials in neuronal cells.

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