The disclosure relates to a system and method for determining and pre-fetching projection data in image reconstruction. The method may include: determining a sequence of a plurality of pixels including a first pixel and a second pixel relating to the first pixel; determining a first geometry calculation used for at least one processor to access a first set of projection data relating to the first pixel from a first storage; determining a second geometry calculation based on the first geometry calculation; determining a first data template relating to the first pixel and a second data template relating to the second pixel based on the second geometry calculation; and pre-fetching a second set of projection data based on the first data template and the second data template, from a storage.

The present disclosure provides a system and method for CT image reconstruction. The method may include combining an analytic image reconstruction technique with an iterative reconstruction algorithm of CT images. The image reconstruction may be performed on or near a region of interest.

A system includes a storage device storing a set instructions and a processor in communication with the storage device, wherein when executing the set of instructions, the processor is configured to cause the system to obtain raw data. The processor may also be configured to cause the system to determine one or more reconstruction-related algorithms and determine one or more containers for the one or more reconstruction-related algorithms. Each of the one or more containers may correspond to at least one of the one or more reconstruction-related algorithms. The system may also be configured determine a reconstruction flow based on the one or more containers and process the raw data according to the reconstruction flow to generate a target image.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.

A x-ray micro tomography system provides the ability to proscriptively determine regularization parameters for iterative reconstruction of a sample, from projection data of the sample. This allows a less experienced operator to determine the regularization parameters with adequate precision.

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 provided for computed tomography (CT) imaging. In one embodiment, a method comprises adaptively blending at least two input image volumes with different spatially-variant noise characteristics to generate an output image volume with uniform noise throughout the output image volume. In this way, images may be reconstructed from projection data with data redundancy without introducing image artifacts from stitching images or variance in image noise due to the data redundancy.

A method including receiving, from a C-arm device, a plurality of fluoroscopic images of a lung, wherein each fluoroscopic image is obtained with the C-arm device positioned at a particular pose of a plurality of poses traversed by the C-arm device while the C-arm device is moved through a range of motion including a range of rotation, the range of rotation encompassing a sweep angle between 45 degrees and 120 degrees; generating an enhanced tomographic image of the lung, by utilizing: a trained machine learning model and the plurality of fluoroscopic images; and outputting a representation of the enhanced tomographic image, wherein, when tested by a method in which: the lung includes a lesion smaller than 30 millimeters, and the representation is an axial slice showing a boundary of the lesion, the lesion has a contrast-to-noise value of at least 5 as compared to a background of the representation.

Systems, methods, and media for complex input data configurations for imaging applications. Complex data optimization can be provided to improve accuracy of models (e.g., neural networks) used to reconstruct medical images from raw sensor data, for example. Complex data optimization can include applying raw sensor data to an input layer of a neural network to generate an input vector ordered such that real components and imaginary components of samples in the raw sensor data are adjacent. The input vector can then be applied to convolutional layer of the neural network.

Systems and methods of enhanced display and viewing of three dimensional (3D) tomographic data acquired in tomosynthesis or tomography. A set of projection data is acquired with an image acquisition system and used to reconstruct enhanced 3D volume renderings that are viewed with motion, advanced image processing or stereotactically to assist in medical diagnosis. Various enhancements are provided for further processing the images, thereby providing additional features and benefits during image viewing.