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.

A system includes an attenuation corrector configured to generate Computed Tomography-(CT-) based attenuation correction data from CT image data, a Positron Emission Tomography (PET) reconstructor configured to reconstruct first PET image data based on PET projection data and the CT-based attenuation correction data, an attenuation correction artifact mitigator configured to analyze the first PET image data for a presence of attenuation correction artifact, an inference engine configured to predict attenuation correction data based on non-attenuation corrected PET image data in response to the presence of attenuation correction artifact in the first PET image data, and an attenuation correction data updater configured to generate modified attenuation correction data based on the CT-based attenuation correction data and the predicted attenuation correction data. The PET reconstructor is further configured to reconstruct second PET image data based on the PET projection data and the modified attenuation correction data.

A method, system, and computer readable medium to perform nuclear medicine scatter correction estimation, sinogram estimation and image reconstruction from emission and attenuation correction data using deep convolutional neural networks. In one embodiment, a Deep Convolutional Neural network (DCNN) is used, although multiple neural networks can be used (e.g., for angle-specific processing). In one embodiment, a scatter sinogram is directly estimated using a DCNN from emission and attenuation correction data. In another embodiment a DCNN is used to estimate a scatter-corrected image and then the scatter sinogram is computed by a forward projection.

The present disclosure relates to reconstructing positron emission tomography (PET) images. The approach may involve receiving measured sinogram data from one or more scans by a PET scanner. The measured sinogram data may represent measured projections from the PET scanner. The approach may involve performing forward rendering to generate a rendered sinogram. Forward rendering may comprise sampling a number of positions corresponding to each crystal detector in a plurality of crystal detectors. The positions may define lines of response (LORs) between crystal pairs. The approach may involve performing inverse rendering based on the measured sinogram data and the rendered sinogram. Inverse rendering may comprise applying auto-differentiation for gradient-based optimization. The rendering may be performed iteratively to update pixel values of an emission image until a stopping criterion. A reconstructed PET image based on the updated emission image may be output following the stopping criterion being satisfied.

A method comprising receiving a plurality of images from a multi-modal imaging system; generating a plurality of filtered measurements by performing multi-modal spectral fusion of the plurality of images; and generating, using a physics-informed neural network (PINN) trained based on one or more physical principles associated with X-ray attenuation or scattering, a reconstructed object image based on the plurality of filtered measurements, wherein generating the reconstructed object image comprises (i) generating, using the PINN, a system matrix for an X-ray imaging forward model by refining one or more coefficients of the system matrix based on a physics-informed loss function, and (ii) generating, using the X-ray imaging forward model and based on the plurality of filtered measurements, the reconstructed object image.

Described embodiments provide systems and methods for generating a corrected planar scintigraphy image (CPSI) corrected for image artifacts. A computing system can obtain a plurality of planar scintigraphy images of a subject. The plurality of planar scintigraphy images may contain image artifacts caused by one or more physical processes. The computing system may generate a corrected CPSI by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images. The planar scintigraphy image reconstruction model may comprise a first non-negativity constraint and a second non-negativity constraint, and be based on a first regularization term, a second regularization term, a coupling term and a fidelity term. The computing system may present the CPSI for evaluation of a condition of the subject. Presenting the CPSI may comprise at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.