An apparatus includes a control system that defines a test part having multiple features of multiple feature types. The control system controls an additive manufacturing (AM) machine to print multiple copies of the test part, with each copy being printed according to a respective set of values used as printing parameters. A measurement system obtains a computed tomography (CT) image of each of the copies of the test part. An analysis system, for each of the plurality of feature types, analyzes the CT images to identify a selected set of values for the printing parameters. The analysis system identifies a portion of the CT image related to a first feature and assesses its density based on an average grayscale value. The AM machine is then controlled to print production parts according to, for each feature type of the production parts, the selected set of values for the printing parameters.

Tomographic images are obtained by processing a tilt series of 2D images by aligning and combining images withing a group of neighbor images. The tilt series generally includes sparsely sampled images. Images of the tilt series at tilt angles associated with the sparsely sample images are selected as reference frames, grouped with neighbor images, and the group of images aligned. The aligned images are combined to produce replacement frames and a replacement frame tilt series that can be used for tomographic reconstruction.

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.

The present disclosure provides a system for MRI. The system may obtain a plurality of auxiliary signals and a plurality of imaging signals collected by applying an MRI pulse sequence simultaneously to a plurality of slice locations of a subject. For each of at least one target slice location of the plurality of slice locations, the system may generate at least one target image of the target slice location based on the plurality of auxiliary signals and the plurality of imaging signals. During the application of the MRI pulse sequence, phase modulation may be applied to at least one of the plurality of slice locations so that the plurality of slice locations have different phases during the readout of at least one of the plurality of imaging signals.

A system for PET image reconstruction is provided. The system may obtain PET data of a subject. The PET data may be associated with a plurality of coincidence events, which includes scattering events. The system may also generate a preliminary scatter sinogram relating to the scattering events based on the PET data. The system may also generate a target scatter sinogram relating to the scattering events by applying a scatter sinogram generator based on the preliminary scatter sinogram. The target scatter sinogram may have a higher image quality than the preliminary scatter sinogram. The system may further reconstruct a target PET image of the subject based on the PET data and the target scatter sinogram.

This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention. Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time.

A method for calibrating defective channels of a CT device involves in a step S10, acquiring original data collected by the CT device; in a step S20, capturing to-be-recovered areas from the original data, wherein the to-be-recovered areas contain the defective channels of the CT device; in a step S30, inputting data of the to-be-recovered areas to a neural network for training so as to generate training results; and in a step S40, using the training results to repair the to-be-recovered areas. The method eliminates effects of artifacts caused by defective channels on image reconstruction.

A 3-D imaging system including a computer determining a plurality of coherence factors measuring an intensity contrast between a first intensity of a first region of an interference comprising constructive interference between a sample wavefront and a reference wavefront, and a second intensity of a second region of the interference comprising destructive interference between the sample wavefront and the reference wavefront, wherein the interference between a reference wavefront and a reflection from different locations on a surface of an object. From the coherence factors, the computer determines height data comprising heights of the surface with respect to an x-y plane perpendicular to the heights and as a function of the locations in the x-y plane. The height data is useful for generating a three dimensional topological image of the surface.

A method for processing breast tissue image data includes processing image data of a patient's breast tissue to generate a high-dimensional grid depicting one or more high-dimensional objects in the patient's breast tissue; determining a probability or confidence of each of the one or more high-dimensional objects depicted in the high-dimensional grid; and modifying one or more aspects of at least one of the one or more high-dimensional objects based at least in part on its respective determined probability or confidence to thereby generate a lower-dimensional format version of the one or more high-dimensional objects. The method may further include displaying the lower-dimensional format version of the one or more high-dimensional objects in a synthesized image of the patient's breast tissue.

Systems and methods for reconstructing medical images are disclosed. Measurement data, such as magnetic resonance (MR) data and positron emission tomography (PET) data, is received from an image scanning system. Attenuation maps are generated based on the PET data and a determined background level of radiation of the image scanning system. The background level of radiation can be caused by the radioactive decay of crystal material of the image scanning system. MR images are reconstructed based on the MR data. Further, a neural network, such as a deep learning neural network, is trained with the attenuation maps and the reconstructed MR images to determine attenuation map based on a reconstructed MR image. The trained neural network can be applied to MR data received for a patient to determine a corresponding attenuation map. A final image is generated based on PET data received for the patient and the determined attenuation map.