40 results about "Synthetic projection" patented technology

A method and system for acquiring, processing, storing, and displaying x-ray mammograms Mp tomosynthesis images Tr representative of breast slices, and x-ray tomosynthesis projection images Tp taken at different angles to a breast, where the Tr images are reconstructed from Tp images

A CT scanner system provides projection-like images of a patient volume. After a CT scan is obtained and a three-dimensional model of the patient is created, any synthetic view can be generated by choosing any array of projection lines, e.g. between a point and a surface (a flat plane, curved plane, spherical, etc) or between two surfaces (parallel or not) and summing across the projection lines. The synthetic projections can mimic certain traditional views, such as a ceph scan, Water's view, Caldwell's projection, etc or can provide a new view that is impossible or impractical with traditional x-ray equipment, such as a perfect parallel projection, or a projection that does not pass all the way through the patient.

The present invention relates to an apparatus for producing a corrected image of a region of interest from acquired projection data (60), wherein an uncorrected intermediate image (74) is reconstructed. The uncorrected intermediate image (74) is corrected and image elements of the corrected intermediate image (85) are classified. Image elements of the corrected 5 intermediate image (85) that are of a high density or low density class are replaced by image elements having values depending on values of the low density class to generate a synthetic image (90). Synthetic projection data (96) are generated by forward projecting the synthetic image (90), and acquired projection data contributing to the high density class are replaced by corresponding synthetic projection data (96) to generate corrected projection data (112). 10 The corrected projection data (112) are used for reconstructing the corrected image.

A projector includes an illuminator; a first image formation unit including a first optical unit having a light modulation element that modulates the light from the illuminator, the first image formation unit using the first optical unit to output first image light; a second image formation unit including a second optical unit having a light modulation element that modulates the light from the illuminator, the second image formation unit using the second optical unit to output second image light; a polarization combining system that combines the first image light outputted from the first image formation unit and the second image light outputted from the second image formation unit; a projection system that projects the image light combined by the polarization combining system; projection position adjusters that optically adjust the projection positions of the light modulation elements on a projection surface; and a pixel value generator that generates the pixel value for each pixel contained in the light modulation elements, the projection positions of which have been adjusted by the projection position adjusters, based on the projection position of the light from the pixel.

A method and system for converting low-dose tomosynthesis projection images or reconstructed slices images with noise into higher quality, less noise, higher-dose-like tomosynthesis reconstructed slices, using of a trainable nonlinear regression (TNR) model with a patch-input-pixel-output scheme called a pixel-based TNR (PTNR). An image patch is extracted from an input raw projection views (images) of a breast acquired at a reduced x-ray radiation dose (lower-dose), and pixel values in the patch are entered into the PTNR as input. The output of the PTNR is a single pixel that corresponds to a center pixel of the input image patch. The PTNR is trained with matched pairs of raw projection views (images together with corresponding desired x-ray radiation dose raw projection views (images) (higher-dose). Through the training, the PTNR learns to convert low-dose raw projection images to high-dose-like raw projection images. Once trained, the trained PTNR does not require the higher-dose raw projection images anymore. When a new reduced x-ray radiation dose (low dose) raw projection images is entered, the trained PTNR outputs a pixel value similar to its desired pixel value, in other words, it outputs high-dose-like raw projection images where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. Then, from the “high-dose-like” projection views (images), “high-dose-like” 3D tomosynthesis slices are reconstructed by using a tomosynthesis reconstruction algorithm. With the “virtual high-dose” tomosynthesis reconstruction slices, the detectability of lesions and clinically important findings such as masses and microcalcifications can be improved.

A method and related apparatus (VS) for synthetic projection images, in particular synthetic 2D mammograms (S) formed from a 3D image volume T made up of slices (SL). It is proposed to compute a forward projection (FP) using a weighted average function that is implemented by a filter (FL). The filter function (FL) is configured such that that voxels in a slice with maximum sharpness are assigned highest weights thereby avoiding blurring by averaging with structurally less relevant slices.

In a mammography method and apparatus to generate a tomosynthetic x-ray image of a breast of a patient, two tomosynthesis scans are successively implemented with different x-ray energies. In one of the scans, a synthetic projection image is generated from at least two projection images acquired in this scan, this synthetic projection image corresponding to a projection at a projection angle at which a projection image has been acquired in the other scan. A difference image, used to reconstruct the tomosynthesis x-ray image, is generated from this synthetic projection image and the projection image acquired in the other scan. Alternatively, in each scan a synthetic projection image is generated from at least two projection images acquired in that scan. Each synthetic projection image represents a projection at the same projection angle. A difference image, used to reconstruct the tomosynthesis x-ray image, is generated from these two synthetic projection images.

Methods and systems for medical imaging including synthesizing virtual projections from acquired real projections and generating reconstruction models and images based on the synthesized virtual projections and acquired real projections. For example, first x-ray imaging data is generated from a detected first x-ray emission at a first angular location and second x-ray imaging data is generated from a detected second x-ray emission at a second angular location. Based on at least the first x-ray imaging data and the second x-ray imaging data, third x-ray imaging data for a third angular location relative to the breast may be synthesized. An image of the breast may be displayed or generated from the third x-ray imaging data.

A tomogram of a body is provided. Projection-image data obtained by a radiation-based projection method is used for providing the tomogram. Initial voxel data are first specified for a plurality of voxels of the body. Synthetic projection-image data are generated based upon a projection rule modeling a course of the projection method. Projection-error data is determined by comparing the synthetic projection-image data with the real projection-image data. The projection-error data are imaged on the basis of a back-projection rule dependent on the projection rule so that voxel-error data are produced. Correction data is generated from the voxel-error data by a gradient-based optimizing algorithm, wherein corrected voxel data are generated using the correction data.

The radiographic apparatus according to the present invention is characterized in that the image processing unit includes: an acquisition unit that acquires projection data of the first energy spectrum and projection data of the second energy spectrum; A first image of projection data of an energy spectrum is synthesized with a second image based on projection data of the second energy spectrum according to prescribed synthesis conditions to generate a composite image; and a display unit displays the generated composite image.

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 discloses an error compensation method for neutron tomography rotation axis deflection angle. The method is based on the projection geometric symmetry principle of parallel beam neutron rays, and obtains by superimposing projection data within a 360-degree rotation range of a sample. A composite projected image with a unique symmetry axis, the symmetry axis of the composite projected image is the projection line of the sample rotation axis; the histogram of the pixel value gradient angle of the composite projected image is further calculated, and the histogram distribution is an even function. Find the symmetry center of the even function, and the gradient angle corresponding to the symmetry center position is the yaw angle of the rotation axis. By introducing the measured yaw angle as a correction parameter into the reconstruction algorithm, the error compensation for the yaw angle of the rotation axis can be realized, so as to ensure the consistency of the definition of each reconstructed slice of the sample and effectively improve the reconstruction accuracy.