Illustrative embodiments are directed to methods, apparatus and computer program products for segmentation of X-ray tomography images via multiple reconstructions. A computed tomography scan of an object is received. The computed tomography scan is processed to generate an absorption reconstruction and a phase reconstruction from the computed tomography scan. First and second sets of seeds within the phase reconstruction are labeled as corresponding to a first phase by thresholding below a first threshold and corresponding to a second phase by thresholding above a second threshold, respectively. The absorption reconstruction is segmented automatically using an algorithm based on the first set of seeds, the second set of seeds and the absorption reconstruction. A final segmentation is produced based on a combination of the absorption reconstruction and the phase reconstruction.

The disclosure provides for a method for generating an ultrasound image that includes transmitting, by a plurality of transmitters in a transducer, at least two transmit beams at different angles, where at least parts of the transmit beams cover an overlapping region, and receiving, by a plurality of sensors of the transducer, reflected signals of the transmit beams. The method further comprises calculating channel coherence for the received signals to produce one or more channel coherence images, and calculating transmit coherence for the received signals to produce one or more transmit coherence images. The information from at least one of the channel coherence images and at least one of the transmit coherence images are combined to identify moving objects. The received signals from different transmits in overlapping regions are then processed to produce a final image that is compensated for the moving objects.

Disclosed is a three-dimensional low-dose PET reconstruction method based on deep learning. The method comprises the following steps: back projecting low-dose PET raw data to the image domain to maintain enough information from the raw data; selecting an appropriate three-dimensional deep neural network structure to fit the mapping between the back projection of the low-dose PET and a standard-dose PET image; after learning from the training samples the network parameters are fixed, realizing three-dimensional PET image reconstruction starting from low-dose PET raw data, thereby obtaining a low-dose PET reconstructed image which has a lower noise and a higher resolution compared with the traditional reconstruction algorithm and image domain noise reduction processing.

The present disclosure is directed to systems and/or methods that may be used for determining scene information (for example, 3D scene information) using data obtained at least in part from a camera array. Certain embodiments may be used to create scene measurements of depth (and the probability of accuracy of that depth) using an array of cameras. One purpose of certain embodiments may be to determine the depths of elements of a scene, where the scene is observed from a camera array that may be moving through the scene. Certain embodiments may be used to determine open navigable space and to calculate the trajectories of objects that may be occupying portions of that space. In certain embodiments, the scene information may be used to generate a virtual space of voxels where the method then determines the occupancy of the voxel space by comparing a variety of measurements, including spectral response.

A processor acquires a plurality of first projection images acquired by imaging an object at a plurality of radiation source positions and acquires a lesion image indicating a lesion. The processor combines the lesion image with the plurality of first projection images on the basis of a geometrical relationship between the plurality of radiation source positions and a position of the lesion virtually disposed in the object to derive a plurality of second projection images. The processor reconstructs the plurality of second projection images to generate a tomographic image including the lesion.

In a method for magnetic resonance imaging pseudorandomly undersampled k- space imaging data is acquired with multiple receiver coils of an MRI imaging apparatus. MR image reconstruction is performed to produce a reconstructed MR image from the k-space imaging data by iteratively solving sketched approximations of an original reconstruction problem. The sketched approximations use a sketched model matrix As that is a lower-dimensional version of an original model matrix A of the original reconstruction problem. The sketched model matrix As preserves the Fourier structure of the MR reconstruction problem and reduces the number of coils actively used during reconstruction.

A plurality of image projections are acquired at faster than cardiac rate. A spatiotemporal reconstruction of cardiac frequency angiographic phenomena in three spatial dimensions is generated from two dimensional image projections using physiological coherence at cardiac frequency. Complex valued methods may be used to operate on the plurality of image projections to reconstruct a higher dimensional spatiotemporal object. From a plurality of two spatial dimensional angiographic projections, a 3D spatial reconstruction of moving pulse waves and other cardiac frequency angiographic phenomena is obtained. Reconstruction techniques for angiographic data obtained from biplane angiography devices are also provided herein.

The disclosure provides a low-dose image reconstruction method and system based on prior anatomical structure difference. The method includes: determining the weights of different parts in the low-dose image based on prior information of anatomical structure differences; constructing a generative network being taking the low-dose image as input extract features, and integrating the weights of the different parts in the feature extraction process, outputting a predicted image; constructing a determining network being taking the predicted image and standard-dose image as input, to distinguish the authenticity of the predicted image and standard-dose image as the first optimization goal, and identifying different parts of the predicted image as the second optimization goal, collaboratively training the generative network and the determining network to obtain the mapping relationship between the low-dose image and the standard-dose image; and reconstructing the low-dose image by using the obtained mapping relationship. The disclosure can obtain more accurate high-definition images.

A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

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.