University of Chicago

Arch Development Corporation

Canon Medical Systems Corporation

Other

Koninklijke Philips Corporation N.v.

Xiaochuan Pan

Chien-min Kao

Emil Sidky

Dan Xia

Zheng Zhang

Yu Zou

Lifeng Yu

Maryellen L Giger

Howard J Halpern

Charles A Pelizzari

Martin King

Seungryong Cho

Joseph Manak

Yu-Bing Chang

Buxin Chen

Jinghan Ye

Chi-Hua Tung

Jingwu Yao

Chin-Tu Chen

Amy Perkins

Patrick Jean La Riviere

A multi-spectral tomography imaging system includes one or more source devices configured to direct beams of radiation in multiple spectra to a region of interest (ROI), and one or more detectors configured to receive the beams of radiation. The system includes a processor configured to cause movement in at least one of the components such that a first beam of radiation with a first spectrum is directed to the ROI for less than 360 degrees of movement of the ROI. The processor is also configured to process data detected by the one or more detectors, where the data results at least in part from the first beam of radiation with the first spectrum that is directed to the ROI for less than the 360 degrees of movement of the ROI. The processor is further configured to generate an image of the ROI based on the processed data.

System and method for low-dose multi-spectral X-ray tomography

In an emission imaging method, emission imaging data are acquired for a subject using an emission imaging scanner () including radiation detectors (). The emission imaging data are reconstructed to generate a reconstructed image by executing a constrained optimization program including a measure of data fidelity between the acquired emission imaging data an a reconstruct-image transformed by a data model of the imaging scanner to emission imaging data. During the reconstructing, each iteration of the constrained optimization program is constrained by an image variability constraint. The reconstructed image is displayed the reconstructed image on a display device. The emission imaging may be positron emission tomography (PET) imaging data, optionally acquired using a sparse detector array. The image variability constraint may be a constraint that an image total variation (image TV) of a latent image defined using a Gaussian blurring matrix be less than a maximum value.

Optimization-based reconstruction with an image-total-variation constraint in PET

An apparatus and method are provided for computed tomography (CT) imaging to reduce artifacts due to objects outside the field of view (FOV) of a reconstructed image. The artifacts are suppressed by using an iterative reconstruction method to minimize a cost function that includes a de-emphasis operator. The de-emphasis operator operates in the data domain, and minimizes the contributions of data inconsistencies arising from attenuation due to objects outside the FOV. This can be achieved by penalizing images that manifest indicia of artifacts due to outside objects especially those outside objects have high-attenuation densities and minimizing components of the data inconsistency likely attributable to the outside object.

Method and apparatus to reduce artifacts in a computed-tomography (CT) image by iterative reconstruction (IR) using a cost function with a de-emphasis operator

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.

Multiresolution iterative reconstruction for region of interest imaging in X-ray cone-beam computed tomography

A system and method are provided for reconstructing images from limited or incomplete data, such as few view data or limited angle data or truncated data generated from divergent beams. The method and apparatus may iteratively constrain the variation of an estimated image in order to reconstruct the image. To reconstruct an image, a first estimated image may be generated. Estimated data may be generated from the first estimated image, and compared with the actual data. The comparison of the estimated data with the actual data may include determining a difference between the estimated and actual data. The comparison may then be used to generate a new estimated image. For example, the first estimated image may be combined with an image generated from the difference data to generate a new estimated image. To generate the image for the next iteration, the variation of the new estimated image may be constrained.

Image reconstruction from limited or incomplete data

A method and apparatus for reconstruction of a region of interest for an object is provided. The reconstruction of the object may be based on chords which may fill a part, all, or more than all of the region of interest. Using chords for reconstruction may allow for reducing data acquired and/or processing for reconstructing a substantially exact image of the ROI. Moreover, various methodologies may be used in reconstructing the image, such as backprojection-filtration, and modified filtration backprojection.

Imaging system

A system and method are provided for reconstructing images from limited or incomplete data, such as few view data or limited angle data or truncated data (including exterior and interior data) generated from divergent beams. In one aspect of the invention, the method and apparatus iteratively constrains the variation of an estimated image in order to reconstruct the image. As one example, a divergent beam maybe used to generate data ('actual data'). As discussed above, the actual data may be less than sufficient to exactly reconstruct the image by conventional techniques, such as FBP. In order to reconstruct an image, a first estimated image may be generated. Estimated data may be generated from the first estimated image, and compared with the actual data. The comparison of the estimated data with the actual data may include determining a difference between the estimated and actual data. The comparison may then be used to generate a new estimated image. For example, the first estimated image may be combined with an image generated from the difference data to generate a new estimated image. In order to generate the image for the next iteration, the variation of the new estimated image may be constrained. For example, the variation of the new estimated image may be at least partly constrained in order to lessen or reducing the total variation of the image.

A method and apparatus for reconstruction of a region of interest (ROI) for an object using an imaging system is provided. The imaging system may substantially exactly reconstruct the ROI with a straight line trajectory. In the straight line trajectory, the ROI is not bounded or encircled by the actual trajectory of the source (e.g., no chords that are composed from two points on the source trajectory intersect or fill the ROI to be imaged). However, the ROI may be substantially reconstructed by using 'virtual' chords to reconstruct the ROI. The virtual chords are such that no point on the trajectory is included in the virtual chord (such as one that is parallel to the straight line trajectory). These virtual chords may intersect and fill the ROI, thus enabling substantially exact reconstruction. Further, in reconstructing the image, the straight line trajectory may be assumed to be infinite in length.

Imaging system performing substantially exact reconstruction and using non-traditional trajectories

Imaging system for generating a substantially exact reconstruction of a region of interest

A method and apparatus are provided for reconstructing a tomographic image from fan-beam or cone-beam data. The method includes the steps of collecting fan-beam or cone-beam data over an image space, converting the fan-beam or cone-beam data to parallel-beam data with respect to a rotation angle within the image space, performing a shift variant filtration of the parallel-beam data within the image space and converting the processed data to images through backprojection or other means.

Algorithm for image reconstruction and image noise analysis in computed tomography

A method and apparatus are provided for reconstructing images from data obtained from scintillation events occurring within a projection space of a depth-of-interaction positron emission tomography system. The method includes the steps of identifying a segment of each depth-of-interaction detector of respective pairs of depth-of-interaction detectors detecting the scintillation-events of the data obtained within the projection space and estimating a set of sinograms from the data based upon a set of depth-independent point spread functions of the identified segments of the respective pairs of depth-of-interaction detectors.

Growing community of inventors

Chicago, IL, United States of America

Xiaochuan Pan