The invention discloses a limited-angle CT reconstruction method based on Anisotropic Total Variation. According to the method, through an image reconstruction model using low dose and sparse-view-angle CT images, a fast iterative reconstruction algorithm is combined with an Anisotropic Total Variation method. The problems that in an existing limited-angle CT reconstruction method are effectively solved, such as partial boundary ambiguity, slow convergence speed and unable to accurately solve. In the process of solving the model, the slope filter is introduced in the Filtered Back-Projection to preprocess the iterative equation, and the Alternating Projection Proximal is used to solve the iterative equation, and the iteration is repeated until the termination condition is satisfied; the experimental comparison with the existing reconstruction methods shows that the invention can achieve better reconstruction effect.

In a medical image generation method, projection data of a scanned object is acquired during rotation of a radiographic source, and a scout image of the scanned object is generated in one scanning direction using corresponding projection data in two opposite scanning directions in the projection data. The scanning direction is used to represent a relative position relationship between the radiographic source and the scanned object. Using projection data in two opposite directions to generate a scout image in one direction during rotary scanning of a radiographic source can significantly shorten the generation time of the scout image.

Systems and methods are provided for reconstructing a three-dimensional result image data set from computed tomography from a plurality of two-dimensional images that create an image of an object undergoing examination from a particular imaging angle, The imaging angles of all the images lie within a restricted angular range. A three-dimensional artifact-reduced image data set is provided based on the two-dimensional images using an algorithm for reducing artifacts that are the result of a restriction of the angular range. The result image data set is reconstructed using a reconstruction algorithm that processes both the artifact-reduced image data set and the two-dimensional images as input data.

A system and method for improved image acquisition of multiple pulsed X-ray source-in-motion tomosynthesis imaging apparatus by generating the electrocardiogram (ECG) waveform data using an ECG device. Once a representative cardiac cycle is determined, system will acquire images only at rest period of heart beat. Real time ECG waveform is used as ECG synchronization for image improvement. The imaging apparatus avoids ECG peak pulse for better chest, lung and breast imaging under influence of cardiac periodical motion. As a result, smoother data acquisition, much higher data quality can be achieved. The multiple pulsed X-ray source-in-motion tomosynthesis machine is with distributed multiple X-ray sources that is spanned at wide scan angle. At rest period of one heartbeat, multiple X-ray exposures are acquired from X-ray sources at different angles. The machine itself has capability to acquire as many as 60 actual projection images within about two seconds.

The invention provides, in some aspects, a system for implementing a rule derived basis to display volumetric image sets. In various embodiments of the invention, the selection of the images to be displayed, the generation of the 3-D volumetric image from measured 2-D images including the rendering parameters and styles, the choice of viewing directions and 2-D projection images based on the viewing directions, the layout of the projection images, and the formation of a video can be determined using a rule derived basis. In an embodiment of the present invention, the user is presented with sequential images making up a video displayed based on their preferences without having to first manually adjust parameters. The present invention allows for novel ways of viewing such images to detect microcalcifications and obstructions when reviewing Digital Breast Tomosynthesis and other volumetric mammography images.

A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Methods, apparatus and systems for deep learning based image reconstruction are disclosed herein. An example at least one computer-readable storage medium includes instructions that, when executed, cause at least one processor to at least: obtain a plurality of two-dimensional (2D) tomosynthesis projection images of an organ by rotating an x-ray emitter to a plurality of orientations relative to the organ and emitting a first level of x-ray energization from the emitter for each projection image of the plurality of 2D tomosynthesis projection images; reconstruct a three-dimensional (3D) volume of the organ from the plurality of 2D tomosynthesis projection images; obtain an x-ray image of the organ with a second level of x-ray energization; generate a synthetic 2D image generation algorithm from the reconstructed 3D volume based on a similarity metric between the synthetic 2D image and the x-ray image; and deploy a model instantiating the synthetic 2D image generation algorithm.

The invention includes a method including the steps of obtaining a plurality of images, each of the images in the plurality having at least one corresponding region, generating a merged image, the merged image also having the corresponding region. The step of generating includes selecting an image source from the plurality of images to source image data for the corresponding region in the merged image by comparing attributes of the corresponding regions of the plurality of images to identify the image source having preferred attributes.

An image display apparatus includes a depth map creating unit that creates, on the basis of a two-dimensional radiation image and a plurality of tomographic images for the same subject, a plurality of depth maps in which each position on the two-dimensional radiation image and depth information indicating a depth directional position of a tomographic plane corresponding to each position are associated with each other while changing a correspondence relationship between each position on the two-dimensional radiation image and the depth information.

A system for generating 2D slices of a 3D image of a target volume is provided. The system receives a target sinogram collected during a computed tomography scan of the target volume. The system inputs the target sinogram to a convolutional neural network (CNN) to generate predicted 2D slices of the 3D image. The CNN is trained using training 2D slices of training 3D images. The system initializes 2D slices to the predicted 2D slices. The system reconstructs 2D slices of the 3D image from the target sinogram and the initialized 2D slices.