A medical imaging apparatus includes a data acquirer configured to acquire measured data acquired by detecting an X-ray transmitted by an X-ray source to an object, and an image processor configured to acquire an initial image based on the measured data, alternately estimate region of interest (ROI)-outside measured data and ROI-inside measured data based on the measured data and the initial image, and acquire a reconstructed image based on the ROI-inside measured data.

A method for determining a spatial distribution of a material property value in an examination region of an examination object is described. With an embodiment of the method, projection scan data is acquired which has been produced with the aid of a single-energy CT scan with a defined X-ray energy spectrum from the examination region of the examination object using a defined scan projection geometry. Furthermore, a target function is established which includes a spectral forward projection of the sought spatial distribution and the acquired projection data. Finally, a spatial distribution of a material property value is determined for which the target function assumes an extremal value. A material property distribution-determining device is also described. A computer tomography system is described, moreover.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system. Execution of the machine executable instructions causes a computational system (106) to: receive (200) measured k-space data (124) acquired according to the first configuration of the magnetic resonance imaging system; receive (202) the reference magnetic resonance image data; receive (204) the synthetic magnetic resonance image data by inputting the reference magnetic resonance image data into the image generating neural network; and reconstruct (206) corrected magnetic resonance image data (132) from the measured k-space data and the synthetic magnetic resonance image data.

Methods and systems are provided for boosting the contrast levels in an image reconstructed from projection data acquired at a single energy. In one embodiment, a method comprises modifying projection data corresponding to a material based on an absorption behavior of the material at a selected energy, wherein the projection data is acquired at an energy higher than the selected energy. In this way, contrast levels may be enhanced in an image reconstructed from projection data acquired at a typical single energy as though the image were reconstructed from projection data acquired at a lower energy.

A method for image reconstruction includes irradiating an object with a beam of radiation from a radiation source, measuring an attenuated portion of the beam, estimating a density of the object, determining a predicted attenuated portion of the beam using the density estimate, and iteratively adjusting the density estimate of the object. The predicted attenuated portion and the measured attenuated portion are compared to determine a signal difference. The density estimate of each portion of the object is adjusted by scaling the density estimate using the average signal differences of rays that intersect the portion of the object. The density estimate may be repeatedly adjusted until a difference between consecutive density estimates is below a selected threshold or a predetermined number of adjustments have been completed. The attenuated portion of the beam may include a scattered portion and a transmitted portion.

A method for the reduction of motion artifacts in a helical CT reconstruction, the method including the steps of: (a) reconstructing from the raw CT data an initial estimate of the 3D attenuation distribution of the object of interest; (b) estimating a pose parameter set of the object for each projection angle; (c) undertaking a motion corrected reconstruction from the measured projections, accounting for the pose changes estimated in (b); (d) iterating steps (b)-(c) until some convergence criterion is met; and (e) making a final reconstruction of diagnostic quality using the pose estimates obtained in the previous steps.

One or more techniques and/or apparatuses described herein provide for reconstructing image data of an object under examination from measured projection data indicative of the object. The measured projection data is converted into image data using an iterative image reconstruction approach. The iterative image reconstruction approach may comprise, among other things, regularizing the image data to adjust a specified quality metric of the image data, identifying regions of the image data that represent aspects of the object that might generate inconsistencies in the measured projection data and correcting the measured projection data based upon such an identification, and/or weighting projections comprised in the measured projection data differently to reduce the influence of projections that respectively have a higher degree of inconsistency in the conversion from projection data to image data.

There is provided an image processing apparatus for reconstructing a tomographic image of a subject based on a plurality of projection images obtained by detecting radiation emitted from a plurality of different positions. The image processing apparatus reconstructs a first tomographic image from the plurality of projection images, extracts a fixed pattern occurring in the first tomographic image due to a radiation detector, and forms a second tomographic image by updating the first tomographic image using a value concerning intensity of the fixed pattern as a regularization term. The image processing apparatus outputs, as the tomographic image of the subject, the second tomographic image obtained by the update.

A calibration method for an x-ray computerized tomography system and a method of tomographic reconstruction are provided. The calibration method includes steps of measuring at least one point spread function (PSF) at each of a plurality of points, compressing each PSF, and in one or more storing operations, storing the compressed PSFs in a computer-accessible storage medium. The PSF measurements are made in a grid of calibration points in a field of view (FOV) of the system. In the measuring step, an absorber is positioned at each of the calibration points, and an x-ray projection is taken at least once at each of those absorber positions. In the method of tomographic image reconstruction, projection data from an x-ray tomographic projection system are input to an iterative image reconstruction algorithm. The algorithm retrieves and utilizes a priori system information (APSI) The APSI comprises comprising point spread functions (PSFs) of all voxels in a voxelization of the field of view that are compressed in the form of vectors of parameters. For utilization, each retrieved vector of parameters is decompressed so as to generate a discretized PSF.

A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a hybrid pre-log and post-log iterative reconstruction method. A pre-log formulation is applied to values of the projection data that are less than a threshold (e.g., X-ray intensities corresponding to high absorption trajectories). The pre-log formulation has better noise modeling and better image quality for reconstructed images, but is slow to converge. Projection data values above the threshold are processed using a post-log formulation, which has fast convergence but poorer noise handling. However, the poorer noise handling has little effect on high value projection data. Thus, the hybrid pre-log and post-log method provides improved image quality by more accurately modeling the noise of low count projection data, without sacrificing the fast convergence of the post-log method, which is applied to high-count projection data.