The State University of New York

Jerome Zhengrong Liang

Mark R Wax

Zigang Wang

Arie E Kaufman

Ming Wan

Ingmar Bitter

Dongqing Chen

William H Moore

Hongbing Lu

Jianhua Ma

Yuxiang Xing

Hao Zhang

Hao Han

Yongfeng Gao

Qin Lin

Kaufman Arie

An exemplary system, method, and computer-accessible medium for generating computed tomography ('CT') image(s) of a subject(s) can be provided which can include receiving low dose CT imaging information for the subject(s), where the low dose CT imaging information can be based on a radiation dose of less than about 50 mAs, receiving a priori CT image data, and generating the CT image(s) based on the low dose CT imaging information and the a priori CT image data. A further CT images(s) can be generated based on the CT image(s) and the a priori CT image data. The a priori CT image data can include a set of Markov Random Field (MRF) coefficients derived from high dose or full dose CT image data.

System, method and computer-accessible medium for ultralow dose computed tomography image reconstruction

Provided is a method for compensating for tissue motion during magnetic resonance (MR) imaging, and an apparatus for use thereof. The method includes acquiring a plurality of short-time MR scan images; selecting a reference scan image from the acquired plurality of short-time MR scan images; defining a set of transformation images based on the acquired plurality of short-time MR scan images other than the selected reference scan image; registering the reference scan image and the defined set of transformation images; calculating an average of aligned, registered images of the defined set of transformation images; and generating a motion-corrected image based on the calculated average.

Magnetic resonance imaging method

Disclosed is a method for performing X-ray Computed Tomography scanning, the method including acquiring a plurality of images of an object, obtaining an initial image from the plurality of images, calculating NonLocal weight of the initial image, utilizing a current image estimation and registered prior image, performing a successive over-relaxation optimization to yield a new image estimation with an intensity of the new image estimation equal or greater than zero, performing a cycle update, generating an image of the object utilizing the new image estimation obtained from the optimization, and outputting a resultant image.

Iterative reconstruction for X-ray computed tomography using prior-image induced nonlocal regularization

A method for reconstructing an image from emission data includes generating a compressed point-spread function matrix, generating an accumulated attenuation factor; and performing at least one image projection operation on an image matrix of the emission data using the compressed point-spread function matrix and the accumulated attenuation factor. The image projection operation can include rotating an image matrix and an exponential attenuation map to align with a selected viewing angle. An accumulated attenuation image is then generated from the rotated image matrix and rotated exponential attenuation map and a projection image is generated for each voxel by multiplying the accumulated attenuation image and point spread function matrix for each voxel. The rotating and multiplying operations can be performed on a graphics processing unit, which may be found in a commercially available video processing card, which are specifically designed to efficiently perform such operations.

System and method for acceleration of image reconstruction

A method for electronically cleansing a virtual object formed from acquired image data converted to a plurality of volume elements is provided. The present method allows individual volume elements, or voxels, to represent more than one material type. The method includes defining a partial volume image model for volume elements representing a plurality of material types based, at least in part, on the measured intensity value of the volume element. The material mixture for each of the volume elements representing a plurality of material types can be estimated using the observed intensity values and the defined partial volume image model. The volume elements representing a plurality of material types can then be classified in accordance with the estimated material mixture. For electronic colon cleansing, the method includes removing at least one classification of volume elements when displaying the virtual object.

Electronic colon cleansing method for virtual colonoscopy

A computer-assisted detection method is provided for detecting suspicious locations of lesions in the volumetric medical images. The method includes steps of features extraction and fusion. The first step is computing gradient feature for extraction of the layer of Partial Volume Effect (LPVE) between different tissues that related to specific organs. The LPVE will combine with the result of voxel classification to fulfill the task of tissue classification. After tissue classification, the contour of tissue boundary is determined. The gradient feature is also used to determine the direction that intensity changes. This direction that intensity changes most dramatically serves as the normal vector for voxel on the contour of the tissue boundary. The second step is to determine a local surface patch on the contour for each voxel on the contour. A local landmark system is then created on that patch and the so-called Euclidean Distance Transform Vector (EDTV) is computed based on those landmarks. The EDTV is the basic shape feature for lesion detection whose development and invasion results abnormal shape change on the tissue boundary. A vector classification algorithm for pattern recognition based on EDTVs is also provided. The voxel on the contour of tissue boundary can be grouped into areas based on similar pattern to form lesion patch and local lesion volume. That area will further be analyzed for estimation of the likelihood of lesion.

Computer assisted detection of lesions in volumetric medical images

Methods for generating a centerline or skeleton structure within a 3D virtual object are provided. A first method defines a centerline based on distance from boundary values along points of the defined centerline. A second method uses a distance from boundary field to assign costs to voxels in the virtual object and defines a minimum cost spanning tree based on assigned costs. The centerline is defined along the minimum cost spanning tree. Branches along the centerline are identified and added to the centerline to define a skeleton.

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Stony Brook, NY, United States of America

Jerome Zhengrong Liang