Image analysis – Applications – Biomedical applications
Reexamination Certificate
1998-02-18
2001-03-13
Au, Amelia (Department: 2723)
Image analysis
Applications
Biomedical applications
C382S241000, C382S242000, C378S004000, C250S363040
Reexamination Certificate
active
06201888
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to the field of computer image processing of computer tomography (CT) data and, more particularly to the problem of restoring noise-corrupted boundaries in CT images, and to the characterizing and displaying of this restored boundary information.
2. Background Description
U.S. Pat. No. 5,416,815, which is incorporated herein by reference in its entirety, describes computer tomography (CT) systems and the method of image reconstruction from projections.
Computed tomography images are created from projection data by a process known as “image reconstruction from projections”. If the projection data are corrupted by noise, the reconstructed images will in turn be corrupted by noise. The artifacts in the images resulting from this noise are known as “reconstruction artifacts”.
For example, in X-ray CT imaging, a significant amount of noise can be introduced into the projection data when the objects being scanned contain metal. The reconstruction artifacts manifest in the form of blooming and streaking artifacts that radiate from he regions of the image where the metal is present.
These artifacts (known as “metal-induced” reconstruction artifacts) produce severe corruption especially at the boundaries of objects in the images. This significantly limits the clinical usefulness of the images, both for diagnostic and therapeutic purposes, since an accurate knowledge about locations of object boundaries is crucial in applications such as computer-assisted surgery, and radiotherapy.
There are two basic approaches to recover information about the corrupted boundaries, a “projection-based” approach, and an “image-based” approach.
In the “projection-based” approach, projection data obtained from the CT scanner are used to recover missing boundary information. The prior art in boundary recovery from projection data includes:
m. Bergstrom, J. Litton, L. Eriksson, C. Bohm, and G. Blomqvist, “Determination of Object Contour from Projections for Attenuation Correction in Cranial Positron Emission Tomography”,
Journal of Computer Assisted Tomography,
April 11982, 6(2), pp. 365-372; Jean-Philippe Thirion and Nicholas Ayache, “Method and device for examining a body, particularly for tomography”, U.S. Pat. No. 5,421,330, (1995); N. Srinivasa, K. R. Ramakrishnan, and K. Rajgopal, “Detection of edges from projections”,
IEEE Trans. Medical Imaging,
March 1992, 11(1), pp. 76-80; Ralph Benjamin, “Method and apparatus for generating images”, U.S. Pat. No. 5,647,018, (1997).
In the “image-based” approach, the noisy CT image is first “cleaned-up” by applying a suitable noise-reduction algorithm. Since this process improves the overall quality of the image, it will improve the quality of the boundaries, which can then be determined with improved precision. Since the image-based methods are primarily concerned with improving the overall quality of the image, they are not especially well-suited for the specific task of boundary-recovery. Nevertheless, they are included here for the sake of completeness.
In general, the most appropriate noise reduction algorithms to use in the image-based approach are hose algorithms known as “metal artifact reduction” (MAR) methods. The prior art in MAR algorithms includes the following: D. D. Robertson, P. J. Weiss, E. K. Fishman, D. Magid, and P. S. Walker, “Evaluation of CT techniques for reducing artifacts in the presence of metallic orthopedic implants”,
Journal of Computer Assisted Tomography,
March-April 1988, 12(2), pp. 236-41; Hamid Soltanian-Zadeh, Joe P. Windham, and Jalal Soltanianzadeh, “CT Artifact Correction: An Image Processing Approach”,
SPIE Medical Imaging '
96, Newport Beach, Calif., February. 1996; Heang K. Tuy, “An Algorithm to Reduce Clip Artifacts in CT Images”,
SPIE Vol.
1652
Medical Imaging VI: Image processing
(1992); G. H. Glover and N. J. Pelc, “An algorithm for the reduction of metal clip artifacts in CT reconstruct ions”,
Medical Physics,
8(6), November/December 1981, pp. 799-807; T. Hinderling, P. Ruegsegger, M. Anliker, and C. Dietschi, “Computed Tomography reconstruction from hollow projections: an application to in vivo valuation of artificial hip joints”,
Journal of Computer Assisted Tomography,
February 1979, 3(1), pp. 52-57; W. A. Kalender, R. Hebel, and J. Ebersberger, “Reduction of CT artifacts caused by metallic implants”,
Radiology,
August. 1987, 164(2), pp. 576-7; E. Klotz, W. A. Kalender, R. Sokiranski, and D. Felsenberg, “Algorithms for the reduction of CT artifacts caused by metallic implants”,
Medical Imaging IV: PACS System Design and Evaluation,
vol. 1234, Newport Beach, Calif., February 1990, pp. 642-650; R. M. Lewitt and B. H. T. Bates, “Image reconstruction from projections: IV: Projection completion methods (computational examples)”, Optik 50, 1978, pp. 269-278; B. E. Oppenheim, “Reconstruction tomography from incomplete projections”,
Reconstruction Tomography in Diagnostic and Nuclear Medicine,
Ter-Pogossian (editor), University Park Press, Baltimore, 1977, pp. 155-183; and G. Wang, D. L. Snyder, A. O'Sullivan, and M. W. Vannier, “Iterative deblurring for CT metal artifact reduction”,
IEEE Trans. Medical
Imaging, October 1996, 14(5), pp. 657-664.
The following are the limitations of the prior art. The existing projection-based boundary-recovery methods are designed to work with high-quality projection data, that is projection data that are (i) highly-sampled, (ii) of high-resolution, and (iii) contain an insignificant amount of noise. Unlike the present invention, they are not designed to handle noisy projection data, or sparsely-sampled or low-resolution projection data.
Therefore, these methods are not suitable for embodiment in practical, real-world systems that can be routinely used in hospitals and medical clinics, and that rely only on data that are readily available from standard medical CT scanners.
The image-based methods, as already mentioned, are not especially well-suited for the specific task of boundary-recovery. Since they are not expressly designed to solve this problem.
DEFINITIONS
The basic concepts described in the present invention are better understood with review of the following definitions.
PIXEL: A picture element. The basic element in a two-dimensional (2D) digital picture.
IMAGE: A rectangular 2D digital picture. Each pixel in the image is identified by a pair of integers (x,y), where x and y are, respectively, the column and row locations of the pixel. (The terms “slice” and “image” are used interchangeably in describing the present invention).
SLICE: See IMAGE.
IMAGE SEGMENTATION: The process of identifying objects of interest in an image.
EDGE: An edge is a point in an image at the transition between two regions having markedly disparate pixel values.
SEGMENTED OBJECT: A object of interest in an image identified by the process of segmentation.
RAY: This refers to a single X-ray beam that travels from the CT scanner X-ray tube to a detector cell on the CT scanner detector array.
RAYSUM: This refers to the basic unit of projection data collected by a CT scanner. It is the value associated with a single X-ray beam. It is a measure of the total attenuation of the beam ray as it travels from its source through the object being scanned to the CT detector array.
VIEW: A view consists of a complete set of rays produced by a CT scanner with the X-ray tube in a fixed position.
DERIVATIVE PROJECTION DATA: Given a set of projection data P, a derivative set of projection data D is a set of projection data derived from P that has less information content. Typical examples of sets of derivative projection data are sets produced when P is sub-sampled, when its resolution is reduced, or when it is otherwise filtered.
SINOGRAM: In its standard mode of operation, a CT scanner collects a set S of views while the CT table is held in a fixed position. The views in the set S are acquired at different positions of the X-ray tube as it rotates around the table. A sinogram consists of all the p
Au Amelia
Dastouri Mehrdad
International Business Machines - Corporation
McGuireWoods LLP
Percello Louis J.
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