Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: A method for generating a radiation treatment plan is provided. The method may include determining a set of one or more optimization goals for radiation delivery by a therapeutic radiation delivery apparatus. The method may also include determining a plan for radiation delivery from a radiation source of the therapeutic radiation delivery apparatus. The radiation source may be capable of continuously rotating around a subject. The plan may include a plurality of radiation segments. Each radiation segment may be characterized by at least one parameter selected from a start angle, a stop angle, a two-dimensional segment shape, or a segment MU value such that the plurality of radiation segments satisfy the set of one or more optimization goals by superimposing at least two radiation segments from at least two different rotations into a target volume of the subject.

Abstract: A method for correcting PET data includes acquiring first PET data at a time interval. The method also includes acquiring a first normalization coefficient corresponding to the first PET data. The method also includes determining a scale factor based at least partially on the first normalization coefficient. The method also includes determining second PET data based on the first PET data, the scale factor, and the second normalization coefficient. The method also includes determining a first dead time correction coefficient corresponding to the second PET data. The method also includes determining third PET data based on the second PET data and the first dead time correction coefficient. The method further includes reconstructing a first image based on the third PET data.

Abstract: A method for image segmentation includes acquiring a three-dimensional (3D) image that includes a plurality of two-dimensional (2D) images arranged in a spatial order. The method also includes determining a preliminary seed point in a first 2D image of the plurality of 2D images. The method further includes determining, based on the preliminary seed point, a final seed point in a second 2D image of the plurality of 2D images, and determining, based on the final seed point, a volume of interest (VOI) in the 3D image.

Abstract: A system includes a storage device storing a set of instructions and at least one processor in communication with the storage device, wherein when executing the instructions, the at least one processor is configured to cause the system to determine a first scan area on a scanning object. The system may also acquire raw data generated by scanning the first scan area on the scanning object and generate a positioning image based on the raw data. The system may also generate a pixel value distribution curve based on the positioning image, and determine a second scan area on the scanning object based on the pixel value distribution curve. The system may also scan the second scan area on the scanning object.

Abstract: The present disclosure relates to a method, system and non-transitory computer readable medium. In some embodiments, the method includes: acquiring a plurality of image slices related to a vertebral column, the vertebral column including a plurality of vertebrae; obtaining a classifier for verterbrae identification; identifying, by a processor, one or more vertebral foramina in the plurality of image slices using the classifier; and determining, by the processor, the plurality of vertebrae based on the one or more vertebral foramina.

Abstract: A method for determining an ROI in medical imaging may include receiving first position information related to a body contour of a subject with respect to a support from a flexible device configured with a plurality of position sensors. The flexible device may be configured to conform to the body contour of the subject, and the support may be configured to support the subject. The method may also include generating a 3D model of the subject based on the first position information. The method may further include determining an ROI of the subject based on the 3D model of the subject.

Abstract: The present disclosure relates to systems and methods for positioning a subject. The method may include generating a first image of the subject disposed on a scanning board of an imaging device. The first image may include position information of the subject. The method may further include generating a second image of the subject which includes information associated with one or more organs of the subjects. Additionally, the method may include determining the position of a ROI based on the first image and the second image. The method may further include operating the imaging device to scan a target portion of the subject.

Abstract: A system may include a storage device that stores executable instructions, and at least one processor in communication with the storage device. When executing the executable instructions, the at least one processor may be configured to cause the system to obtain information relating to an object; determine a target operation mode for the object according to the information relating to the object; obtain information relating to previous operations of a plurality of candidate scanning devices from a database; select a target scanning device for the object according to the target operation mode and the information relating to the previous operations of the plurality of candidate scanning devices; and generate a schedule for operating the target scanning device to scan the object.

Abstract: The disclosure relates to systems and methods for image processing. A trained deep learning model may be determined. An input image may be acquired. A processing result may be generated by processing the input image based on the trained deep learning model. The trained deep learning model may be obtained according to a process including: acquiring a preliminary deep learning model; acquiring a sample image; generating a plurality of sub-images based on the sample image; and training the preliminary deep learning model based on the plurality of sub-images to obtain the trained deep learning model.

Abstract: A PET detecting module may include a scintillator array configured to receive a radiation ray and generate optical signals in response to the received radiation ray. The scintillator array may have a plurality of rows of scintillators arranged in a first direction and a plurality of columns of scintillators arranged in a second direction. A first group of light guides may be arranged on a top surface of the scintillator array along the first direction. The light guide count of the first group of light guides may be less than the row count of the plurality of rows of scintillators. A second group of light guides may be arranged on a bottom surface of the scintillator array. The light guide count of the second group of light guides may be less than the column count of the plurality of columns of scintillators.

Abstract: A method and a system for image reconstruction are provided. The method may include acquiring raw image data, wherein the raw image data may include a plurality of frequency domain undersampled image data samples. The method may include generating a first reconstruction result based on the raw image data using a first reconstruction method, and generating a second reconstruction result based on the raw image data using a second reconstruction method. The method may further include fusing the first reconstruction result and the second reconstruction result, and generating a reconstructed image based on a result of the fusion.

Abstract: A method for assessing the time of flight (TOF) performance of a positron emission tomography (PET) scanner is provided. The method may include obtaining raw data relating to radiation originating from an object from a PET scan by a PET scanner, the raw data including TOF information. The method may also include generating, based on a back projection algorithm, a first back projection image by reconstructing the raw data including the TOF information. The method may further include generating, based on the back projection algorithm, a second back projection image by reconstructing the raw data excluding the TOF information. The method may further include comparing the first back projection image with the second back projection image. The method may also include assessing, based on the comparison, the TOF performance of the PET scanner.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: Systems and methods for processing colon image data are provided. Image data related to a first ROI may be obtained, wherein the first ROI may include a soft tissue represented by a plurality of voxels, and each voxel may have a voxel value. A first virtual scene may be visualized based on the image data, wherein the first virtual scene may reveal at least one portion of the first ROI. A collision detection may be performed between at least one portion of the first ROI and a virtual object in the first virtual scene. A feedback force may be determined from at least one portion of the first ROI based on the collision detection. At least one of the plurality of voxels corresponding to a second ROI may be determined based on the feedback force, wherein the second ROI may relate to the soft tissue in the first ROI.

Abstract: A detector module is provided. The detector module may include a plurality of detector sub-modules. Each of the plurality of detector sub-modules may include a detection layer, at least one data acquisition circuitry, a frame for supporting the detection layer, and a positioning element for assembling the plurality of detector sub-modules. The frame may include a plurality of heat transfer fins that are thermally connected with the at least one data acquisition circuitry for dissipating heat produced by the at least one data acquisition circuitry.

Abstract: A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

Abstract: Methods and systems for image segmentation are provided. Image data may be acquired, wherein the image data may include a plurality of ribs. A rib region containing at least a portion of the plurality of ribs may be determined. At least one rib of the plurality of ribs may be selected as a target rib based on the rib region. At least one rib-probability-map relating to the target rib may be generated based on an artificial intelligence algorithm. A starting point of the target rib may be determined based on the image data, wherein the starting point may indicate a starting position for tracking the target rib. At least one portion of the target rib may be tracked based on the starting point and the at least one rib-probability-map. A segmented rib may be obtained by segmenting the at least one portion of the target rib.

Abstract: Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

Abstract: A method for determining a CT focal point includes determining a first intensity of first radiation incident on a first detector unit of a scanner, wherein the scanner may include a non-uniform anti-scatter grid (ASG) and a radiation source, and the non-uniform ASG may be configured according to a first focal point of the radiation source. The method also includes determining a second intensity of second radiation incident on a second detector unit of the scanner, wherein the first radiation and the second radiation are emitted from the radiation source with a second focal point. The method further includes determining a displacement of the second focal point from the first focal point based on the first intensity and the second intensity.