A method and apparatus is provided to generate a multiresolution image having at least two regions with different pixel pitches. The multiresolution image is reconstructed using projection data having various pixel pitches corresponding to the pixel pitches of the multiresolution image. By using a higher resolution inside regions of interest (ROIs) in both the image and projection domains and lower resolution outside the ROIs, fast image reconstruction can be performed while avoiding truncation artifacts, which result imaging is limited to an ROI excluding attenuation regions. Further, those regions of greater clinical relevance and greater structural variance within the reconstructed images can be selected to be within the ROIs to improve the clinical benefit of the multiresolution image. The multiresolution image can be reconstructed using an iterative reconstruction method in which the high- and low-resolution regions are uniquely evaluated.

The invention relates to a method for identifying a change in truncation of an examination object between first projection photographs of the examination object generated by a CT apparatus, and second projection photographs of the examination object generated by the CT apparatus, comprising the following steps to be carried out by a control facility. Receiving a first dataset, comprising the first projection photographs of the examination object; receiving a second dataset, comprising the second projection photographs of the examination object; correlating the first dataset with the second dataset; establishing the change in truncation of the examination object between the first projection photographs and the second projection photographs when satisfaction of at least one predefined change criterion between the datasets is captured; and outputting a predetermined output signal when the change in truncation between the first projection photographs and the second projection photographs is established.

PET/MR images are compensated with simplified adaptive algorithms for truncated parts of the body. The compensation adapts to a specific location of truncation of the body or organ in the MR image, and to attributes of the truncation in the truncated body part. Anatomical structures in a PET image that do not require any compensation are masked using a MR image with a smaller field of view. The organs that are not masked are then classified as types of anatomical structures, the orientation of the anatomical structures, and type of truncation. Structure specific algorithms are used to compensate for a truncated anatomical structure. The compensation is validated for correctness and the ROI is filled in where there is missing voxel data. Attenuation maps are generated from the compensated ROI.

A multimodal imaging apparatus, comprising a rotatable gantry system positioned at least partially around a patient support, a first source of radiation coupled to the rotatable gantry system, the first source of radiation configured for imaging radiation, a second source of radiation coupled to the rotatable gantry system, the second source of radiation configured for at least one of imaging radiation or therapeutic radiation, wherein the second source of radiation has an energy level more than the first source of radiation, and a second radiation detector coupled to the rotatable gantry system and positioned to receive radiation from the second source of radiation, and a processor configured to combine first measured projection data based on the radiation detected by the first detector with second measured projection data based on the radiation detected by the second detector, and reconstruct an image based on the combined data, wherein the reconstructing comprises at least one of correcting the second measured projection data using the first measured projection data, correcting the first measured projection data using the second projection data, and distinguishing different materials imaged in the combined data using the first measured projection data and the second measured projection.

Described herein are systems and methods for automated completion, combination, and completion by combination of sinograms. In certain embodiments, sinogram completion is based on a photographic (e.g. spectral or optical) acquisition and a CT acquisition (e.g., micro CT). In other embodiments, sinogram completion is based on two CT acquisitions. The sinogram to be completed may be truncated due to a detector crop (e.g., a center-based crop or an offset based crop). The sinogram to be completed may be truncated due to a subvolume crop (e.g., based on low resolution image projected onto sinogram).

A method for multi-dimensional image processing obtains a multi-dimensional image having a plurality of data elements, each data element having a corresponding data value. The method forms a reduced noise image by repeated iterations of a process that adjusts the data value for one or more data elements p of the obtained image by: for each data element k in a group of data elements in the image, calculating a weighting factor for the data element k as a function of the difference between an estimated data value for data element p and the corresponding data value of data element k, updating the estimated data value for the data element p according to the combined calculated weighting factors and the data value of the data element k; and displaying, storing, or transmitting the reduced noise image.

Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent dataoptionally collected at a higher throughputto ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

A method relates to the use of deep learning techniques, which may be implemented using trained neural networks (50), to estimate various types of missing projection or other unreconstructed data. Similarly, the method may also be employed to replace or correct corrupted or erroneous projection data as opposed to estimating missing projection data.

A system and method for performing reconstruction of an image of an object from tomographic data collected by a scanner having stationary x-ray sources and stationary x-ray detectors. The system and method utilize a weighting function based upon a source availability map to establish a no-view differentiation condition, thereby reducing artifacts in the reconstructed image of the object.

When generating a measurement plan for measuring X-ray CT that performs X-ray irradiation while rotating a test object, and in doing so acquires projection image data, reconstructs volume data from the projection image data, and measures a targeted measurement location in the volume data, the present invention calculates required measurement accuracy and a measurement field of view range based on tolerance information included in CAD data of the test object and a measurement location on the test object defined by a measurement operator ahead of time, and automatically generates, from this information, an optimized measurement plan that minimizes the number of measurements.