Abstract: A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.

Abstract: The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

Abstract: The present disclosure relates to systems and methods for image processing. The systems may acquire imaging data. The systems may generate multiple intermediate images based on the imaging data by performing image optimization of multiple image optimization dimensions using a plurality of trained machine learning models. The systems may generate a target image based on the intermediate images.

Abstract: The present disclosure provides a positron emission tomography (PET) system and an image reconstruction method thereof. The PET system may include a plurality of annular detector units arranged along an axial direction. Each of the detector units may generate a plurality of single event counts. The PET system may further include a plurality of coincidence logic circuits connected to one or more of the detector units. The coincidence logic circuits may be configured to count coincidence events. Single event data generated by each of the detector units may be transmitted to the corresponding coincidence logic circuit. The plurality of coincidence logic circuits may synchronically generate coincidence counts relating to the plurality of detector units.

Abstract: The present disclosure relates to systems and methods for image processing. The methods may include obtaining imaging data of a subject, generating a first image based on the imaging data, and generating at least two intermediate images based on the first image. At least one of the at least two intermediate images may be generated based on a machine learning model. And the at least two intermediate images may include a first intermediate image and a second intermediate image. The first intermediate image may include feature information of the first image, and the second intermediate image may have lower noise than the first image. The methods may further include generating, based on the first intermediate image and at least one of the first image or the second intermediate image, a target image of the subject.

Abstract: The present disclosure discloses a photoelectric detector, a preparation method thereof, a display panel and a display device. The photoelectric detector includes a base, and a thin film transistor (TFT) and a photosensitive PIN device on the base, wherein the PIN device includes an I-type region that does not overlap with an orthographic projection of the TFT on the base; a first etching barrier layer covering a top surface of the I-type region; a first heavily doped region in contact with a side surface on a side, proximate to the TFT, of the I-type region; and a second heavily doped region in contact with a side surface on a side, away from the TFT, of the I-type region, the doping types of the first heavily doped region and the second heavily doped region being different from each other.

Abstract: The present disclosure provides a positron emission tomography (PET) system and an image reconstruction method thereof. The PET system may include a plurality of annular detector units arranged along an axial direction. Each of the detector units may generate a plurality of single event counts. The PET system may further include a plurality of coincidence logic circuits connected to one or more of the detector units. The coincidence logic circuits may be configured to count coincidence events. Single event data generated by each of the detector units may be transmitted to the corresponding coincidence logic circuit. The plurality of coincidence logic circuits may synchronically generate coincidence counts relating to the plurality of detector units.

Abstract: The present disclosure provides a system and method for image reconstruction and processing. The method may include obtaining image data of an object acquired by an imaging device. The method may include determining one or more regions of interest (ROIs) of the object. The method may also include determining, based on each ROI of the one or more ROIs, a target portion of the image data corresponding to the ROI among the image data. The method may further include reconstructing, based on the target portion of the image data, one or more images of the ROI.

Abstract: The present disclosure provides a thin film transistor, a pixel structure, a display device, and a manufacturing method. The thin film transistor includes: a gate on the substrate; a gate insulating layer covering the gate and the substrate; a first support portion and a second support portion, which are provided on the gate insulating layer covering the substrate and located on both sides of the gate, wherein the first support portion is not connected to the second support portion; a semiconductor layer on the first support portion, the second support portion, and the gate insulating layer covering the gate; and a source and a drain respectively connected to the semiconductor layer. The first support portion and the second support portion are respectively configured to support the semiconductor layer.

Abstract: A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Abstract: A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

Abstract: The present disclosure provides a thin film transistor, a pixel structure, a display device, and a manufacturing method. The thin film transistor includes: a gate on the substrate; a gate insulating layer covering the gate and the substrate; a first support portion and a second support portion, which are provided on the gate insulating layer covering the substrate and located on both sides of the gate, wherein the first support portion is not connected to the second support portion; a semiconductor layer on the first support portion, the second support portion, and the gate insulating layer covering the gate; and a source and a drain respectively connected to the semiconductor layer. The first support portion and the second support portion are respectively configured to support the semiconductor layer.

Abstract: The present disclosure discloses a photoelectric detector, a preparation method thereof, a display panel and a display device. The photoelectric detector includes a base, and a thin film transistor (TFT) and a photosensitive PIN device on the base, wherein the PIN device includes an I-type region that does not overlap with an orthographic projection of the TFT on the base; a first etching barrier layer covering a top surface of the I-type region; a first heavily doped region in contact with a side surface on a side, proximate to the TFT, of the I-type region; and a second heavily doped region in contact with a side surface on a side, away from the TFT, of the I-type region, the doping types of the first heavily doped region and the second heavily doped region being different from each other.

Abstract: The present disclosure is related to systems and methods for reconstructing a positron emission tomography (PET) image. The method includes obtaining PET data of a subject. The PET data may correspond to a plurality of voxels in a reconstructed image domain. The method includes obtaining a motion signal of the subject. The method includes obtaining motion amplitude data. The motion amplitude data may indicate a motion range for each voxel of the plurality of voxels. The method includes determining gating data based at least in part on the motion amplitude data. The gating data may include useful percentage counts each of which corresponds to at least one voxel of the plurality of voxels. The method includes gating the PET data based on the gating data and the motion signal. The method includes reconstructing a PET image of the subject based on the gated PET data.

Abstract: The present disclosure provides a positron emission tomography (PET) system and an image reconstruction method thereof. The PET system may include a plurality of annular detector units arranged along an axial direction. Each of the detector units may generate a plurality of single event counts. The PET system may further include a plurality of coincidence logic circuits connected to one or more of the detector units. The coincidence logic circuits may be configured to count coincidence events. Single event data generated by each of the detector units may be transmitted to the corresponding coincidence logic circuit. The plurality of coincidence logic circuits may synchronically generate coincidence counts relating to the plurality of detector units.

Abstract: The present disclosure provides a system and method for medical imaging. The method may include obtaining a preliminary image and scanning data of a subject acquired using a scanner. The method may also include determining a regularization parameter for a regularization item of an objective function based at least in part on the scanning data, wherein the regularization parameter includes at least two of a first component characterizing quality of the scanning data, a second component characterizing the scanner, or a third component characterizing a feature of the subject. The method may further include generating an image of the subject by reconstructing the preliminary image based on the objective function.

Abstract: The present disclosure provides a positron emission tomography (PET) system and an image reconstruction method thereof. The PET system may include a plurality of annular detector units arranged along an axial direction. Each of the detector units may generate a plurality of single event counts. The PET system may further include a plurality of coincidence logic circuits connected to one or more of the detector units. The coincidence logic circuits may be configured to count coincidence events. Single event data generated by each of the detector units may be transmitted to the corresponding coincidence logic circuit. The plurality of coincidence logic circuits may synchronically generate coincidence counts relating to the plurality of detector units.

Abstract: System for image correction in PET is provided. The system may acquire a PET image and a CT image of a subject. The system may generate, based on the PET image and the CT image, an attenuation-corrected PET image of the subject by application of an attenuation correction model. The attenuation correction model may be a trained cascaded neural network including a trained first model and at least one trained second model downstream to the trained first model. During the application of the attenuation correction model, an input of each of the at least one trained second model may include the PET image, the CT image, and an output image of a previous trained model that is upstream and connected to the trained second model.

Abstract: The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

Abstract: The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.