Methods for simulating, and correcting for, doubly scattered annihilation gamma-ray photons in both time-of-flight (TOF) and non-TOF positron emission tomography scan data are disclosed.

Systems and methods are provided for performing operations including: accessing a current structural estimate of a region of interest; generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement; and generating an update to the current structural estimate of the region of interest as a function of the first simulated X-ray measurement and the first real X-ray measurement, the update being generated invariant on the current structural estimate.

A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

A method and a system for positron emission tomography (PET) imaging may be provided. Scan data of an object in a first time period of a PET scan of the object may be obtained. A reference image of the object may be also obtained. The reference image may be reconstructed based on reference scan data in a second time period of the PET scan. A target image of the object in the first time period may be generated using an image reconstruction model based on the scan data in the first time period and the reference image of the object.

Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

Systems and methods are disclosed for image processing of cone beam computed tomography (CBCT) image data, in connection with radiotherapy planning and treatments. Example operations for training a regression model for data processing include: obtaining a reference medical image of an anatomical area of a human patient; generating a set of CBCT projections from the reference medical image at each of a plurality of projection viewpoints in a CBCT projection space; generating a set of simulated scatter data to represent effects of scatter from CBCT imaging in each respective projection in the set of CBCT projections; and training the regression model using the set of CBCT projections and the set of simulated scatter data. Further operations for use of the trained regression model, and inferring scatter or scatter-corrected projections from the trained regression model, are disclosed.

A computer-implemented method of reducing scatter in an X-ray projection image of an object comprises: generating an initial X-ray projection image with an imaging beam and an X-ray detector; based on a first position in a detector array of the X-ray detector, selecting a first kernel for convolution of a first portion of the initial projection image, wherein the first position corresponds to the first portion of the initial projection image; based on a second position in the detector array of the X-ray detector, selecting a second kernel for convolution of a second portion of the initial projection image, wherein the second position corresponds to the second portion of the initial projection image; convolving the first portion with the first kernel and the second portion with the second kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

A computer-implemented method of reducing scatter in an X-ray projection image of an object, the method comprising: generating an initial X-ray projection image of an object with an imaging beam produced by an imaging system; based on a first transmission indicator for the object and on a second transmission indicator for at least one element of the imaging system, selecting a kernel for convolution of the initial projection image; convolving the initial X-ray projection image with the kernel to generate a scatter component of the initial X-ray projection image; and generating a corrected X-ray projection image by removing the scatter component from the initial X-ray projection image.

Systems and methods are disclosed for performing operations comprising: accessing a current structural estimate of a region of interest; generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement; and generating an update to the current structural estimate of the region of interest as a function of the first simulated X-ray measurement and the first real X-ray measurement, the update being generated invariant on the current structural estimate.

A method for estimating noise equivalent counts includes one or more times during a scan of an object with a positron emission tomography (PET) scanner, wherein a plurality of coincidence events are detected by a detector array of the PET scanner, performing the following actions. The actions include obtaining a total of the plurality of coincidence events, estimating random coincidence events, and estimating scatter coincidence events. The actions include estimating true coincidence events based on the total of the plurality of coincidence events, the estimated random coincidence events, and the estimated scatter coincidence events. The actions include determining a scatter fraction based on the estimated scatter events and true coincidence events. The actions include estimating the noise equivalent counts based at least on the scatter fraction, the total of the plurality of coincidence events, the estimated true coincidence events, the estimated scatter coincidence events, and the estimated random coincidence events.