X-ray or gamma ray systems or devices – Specific application – Computerized tomography
Reexamination Certificate
2002-12-16
2004-07-27
Bruce, David V. (Department: 2882)
X-ray or gamma ray systems or devices
Specific application
Computerized tomography
C378S004000, C378S901000
Reexamination Certificate
active
06768782
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to multi-slice computed tomography (CT) imaging systems, and more particularly, to an apparatus and methods of reconstructing an image of an object for an imaging system.
2. Description of the Prior Art
A computed tomography (CT) imaging system typically includes an x-ray source that projects a fan-shaped x-ray beam through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, generally referred to as an “imaging plane”. Intensity of radiation from the beam received at the detector array is dependent upon attenuation of the x-ray beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The x-ray source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements, i.e., integral projection data, from the detector array at one gantry angle is referred to as a “view”. A “scan” of the object comprises a set of views made at different projection angles.
In an axial scan, the projection data is processed to construct an image that corresponds to a two-dimensional slice taken through the object. For discrete slices, iterative reconstruction of a full field of view may be performed in order to increase image quality. Multiple iterations are performed to approximately match a resulting reconstructed image to the acquired projection data.
Conventional methods for tomographic image reconstruction in single planes from axial mode data may be found in Avinash C. Kak and Malcolm Slaney, “Principles of Computerized Tomographic Imaging,” Classics in Applied Mathematics, 33, SIAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference, having been applied especially to X-ray CT since the 1970's. One of the earliest iterative methods for reconstruction, algebraic reconstruction technique (ART), is also discussed in Avinash C. Kak and Malcolm Slaney, “Principles of Computerized Tomographic Imaging,” Classics in Applied Mathematics, 33, SIAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference. References such as A. Delaney and Y. Bresler, “Multiresolution Tomographic Reconstruction Using Wavelets,” IEEE Transactions on Image Processing, vol. 4 no.
6
, pp. 799-813, June 1995, and B. Sahiner and A. Yagle, “Region-of-Interest Tomography Using Exponential Radial Sampling,” IEEE Transactions on Image Processing, vol. 4 no. 8, pp. 1120-1127, August 1995, the entire contents and disclosures of which are hereby incorporated by reference, use non-iterative reconstruction methods based on alternative signal representations. In references A. Dempster, N. Laird and D. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society B, vol. 1 no. 39, pp. 1-38, 1977, L. Shepp and Y. Vardi, “Maximum Likelihood Reconstruction for Emission Tomography,” IEEE Transactions on Medical Imaging, vol. MI-1, no. 2, pp. 113-122, October 1982, and K. Lange and R. Carson, “EM Reconstruction Algorithms for Emission and Transmission Tomography,” Journal of Computer Assisted Tomography, vol. 8 no. 2, pp. 306-316, April 1984, the entire contents and disclosures of which are hereby incorporated by reference, the “expectation-maximization” (EM) technique appears, in the general form, applied to emission tomography, and studied for both emission and transmission (such as X-ray CT). “Ordered subsets” methods for EM are presented in Hudson and Larkin, “Accelerated Image Reconstruction Using Ordered Subsets of Projection Data,” IEEE Transactions on Medical Imaging, vol. 13 no. 4, pp. 601-609, December 1994, the entire contents and disclosure of which is hereby incorporated by reference. The Bayesian methods of T. Hebert and R. Leahy, “A Generalized EM Algorithm for 3-D Bayesian Reconstruction from Poisson data Using Gibbs Priors,” IEEE Transactions on Medical Imaging, vol. 8 no. 2, pp. 194-202, June 1989, the entirc contents and disclosure of which is hereby incorporated by reference, are an example of “maximum a posteriori” (MAP) techniques, and K. Sauer and C. A. Bouman, “A Local Update Strategy for Iterative Reconstruction from Projections,” IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 534-548, February 1993, and C. A. Bouman and K. Sauer, “A Unified Approach to Statistical Tomography Using Coordinate Descent Optimization,” EEE Transactions on Image Processing, vol. 5, no. 3, pp. 480-492, March 1996, the entire contents and disclosures of which are hereby incorporated by reference, include MAP techniques with pixel updates. “Segmentation” of images is an imaging process, examples of which are found in H. Derin and H. Elliot, “Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, pp. 39-55, January 1987, and C. Bouman and B. Liu, “Multiple Resolution Segmentation of Textured Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-13, no. 2, pp. 99-113, February 1991, the entire contents and disclosures of which are hereby incorporated by reference, and other references cited therein.
To reduce the total scan time required for multiple slices, a “helical” scan may be performed. Helical scan techniques allow for large volumes to be scanned at a quicker rate using a single x-ray source. To perform a “helical” scan, the patient is moved along a z-axis synchronously with the rotation of the gantry, while data for a prescribed number of slices are acquired. Such a system generates a single helix from a fan beam or cone beam helical scan. The helix mapped out by the fan beam or cone beam yields projection data from which images in each prescribed slice may be reconstructed. In addition to reducing scan time, helical scanning provides other advantages such as better use of injected contrast, improved image reconstruction at arbitrary locations, and better three-dimensional images.
In order to reconstruct the image, typically, a filtered backprojection (FBP) reconstruction approach is utilized. In FBP the projection data is filtered before being backprojected onto an image matrix. The filtering mathematically reverses image blurring, restoring the image to an accurate representation of the scanned object. Although FBP provides relatively quick image reconstruction, many approximations occur due to the imaging system's accounting for geometries and defects in a single iteration, resulting in an image containing blurring and artifacts.
In CT imaging a targeted reconstruction approach is a popular technique for improvement of image quality and spatial resolution. The targeted reconstruction technique involves using a higher resolution for a reconstruction field of view (RFOV) by reconstructing only the targeted area rather than the entire FOV. Since the image matrix size is typically limited, sampling density within the RFOV can be significantly improved by limiting size of the RFOV. The targeted reconstruction technique ensures that spatial resolution of the reconstructed image is limited by scanning hardware capabilities and not by matrix size of the image.
1
For example, when the RFOV is 50 cm by 50 cm each image pixel is approximately 1 mm by 1 mm in size, versus 0.2 mm by 0.2 mm in size when the RFOV is 10 cm by 10 cm. For the RFOV of 50 cm by 50 cm, based on Nyquist sampling theory, the maximum supported spatial resolution is 5 line pairs (LP) per centimeter and for the RFOV of 10 cm by 10 cm the maximum supported spatial resolution is 25 LP/cm.
For filtered backprojection (FBP), targeted reconstruction is nearly identical to full FOV reconstruction. Projection data is weighted and filtered in a fashion similar to full FOV reconstruction. During b
Bouman Charles A.
Hsieh Jiang
Sauer Ken
Thibault Jean-Baptiste
Bruce David V.
Jagtiani + Guttag
University of Notre Dame du Lac
LandOfFree
Iterative method for region-of-interest reconstruction does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Iterative method for region-of-interest reconstruction, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Iterative method for region-of-interest reconstruction will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3256144