Neural network for improved performance of medical imaging systems

    公开(公告)号:US12073538B2

    公开(公告)日:2024-08-27

    申请号:US17225672

    申请日:2021-04-08

    Abstract: Existing, low quality images can be restored using reconstruction or a combination of post-reconstruction techniques to generate a real patient phantom. The real patient phantom (RPP) can then be simulated in Monte Carlo simulations of a higher performance system and a lower performance system. Alternatively, the RPP can be simulated in the higher performance system, and a real scan can be performed by an existing, lower performance system. The higher performance system can be differentiated from the lower performance system in a variety of ways, including a higher resolution time of flight measurement capability, a greater sensitivity, smaller detector crystals, or less scattering. A neural network can be trained using the images produce by the higher performance system as the target, and the images produced by the lower performance system as the input. After training, the trained neural network can be used to output input images taken in a lower performance system with higher performance system characteristics.

    Methods and systems for reconstructing a positron emission tomography image

    公开(公告)号:US12299780B2

    公开(公告)日:2025-05-13

    申请号:US17237975

    申请日:2021-04-22

    Abstract: An apparatus for reconstructing a positron emission tomography (PET) image, comprising processing circuitry configured to extract, from raw data obtained from a PET scanner, energy data and timing data associated with a plurality of annihilation events, the extracted energy data and the extracted timing data for each annihilation event corresponding to interactions between each of a pair of gamma rays generated by each annihilation event and one or more gamma ray detectors of the PET scanner, classify each annihilation event based on respective extracted energy data and respective extracted timing data, determine, for each annihilation event and based on a calculated timing resolution of the annihilation event, a width of a time-of-flight kernel, and reconstruct, by processing circuitry, the PET image based on the obtained raw data from the PET scanner and the determined width of the time-of-flight kernel associated with each annihilation event.

    Event property-dependent point spread function modeling and image reconstruction for PET

    公开(公告)号:US12159330B2

    公开(公告)日:2024-12-03

    申请号:US17674063

    申请日:2022-02-17

    Abstract: Upon receiving list-mode data by detecting radiation emitted from a radiation source positioned with a field of view of a medical imaging scanner, each photon included in the list-mode data can be classified according to one or more interaction properties, such as energy or number of crystals interacted with. Grouped pairs of photons can be generated based on the classifying, and a corresponding interaction-property-specific correction kernel (e.g., a corresponding interaction-property-specific point spread function correction kernel) can be selected for each group. Corresponding interaction-property-specific correction kernels can then be utilized to generate higher quality images.

    Method and system for PET detector efficiency normalization

    公开(公告)号:US11835669B2

    公开(公告)日:2023-12-05

    申请号:US17557710

    申请日:2021-12-21

    CPC classification number: G01T1/2985

    Abstract: A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

    Method and system for PET detector efficiency normalization

    公开(公告)号:US11249206B2

    公开(公告)日:2022-02-15

    申请号:US16866993

    申请日:2020-05-05

    Abstract: A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

    Activity-dependent, spatially-varying regularization parameter design for regularized image reconstruction

    公开(公告)号:US11049294B2

    公开(公告)日:2021-06-29

    申请号:US16149439

    申请日:2018-10-02

    Abstract: A method and apparatus is provided to iteratively reconstruct an image from gamma-ray emission data by optimizing an objective function with a spatially-varying regularization term. The image is reconstructed using a regularization term that varies spatially based on an activity-level map to spatially vary the regularization term in the objective function. For example, more smoothing (or less edge-preserving) can be imposed where the activity is lower. The activity-level map can be used to calculate a spatially-varying smoothing parameter and/or spatially-varying edge-preserving parameter. The smoothing parameter can be a regularization parameter β that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter β can depend on a sensitivity parameter. The edge-preserving parameter β can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.

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