Image reconstruction from limited or incomplete data

Patent metadata

Field	Value
Publication number	US-2016247302-A1
Application number	US-201514933844-A
Country	US
Kind code	A1
Filing date	Nov 5, 2015
Priority date	Feb 13, 2006
Publication date	Aug 25, 2016
Grant date	—

Abstract

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.

First claim

1 . A method of obtaining an image of at least a part of a region of interest (ROI) using a divergent beam comprising:
generating ROI data using the divergent beam; and in order to obtain the image of the at least a part of the ROI, iteratively generating an estimated image using the ROI data and constraining variation of the estimated image.

2 . The method of claim 1 , wherein generating ROI data using the divergent beam comprises collecting partial ROI data.

3 . The method of claim 2 , wherein the partial ROI data comprises partial knowledge of a linear transform of the image.

4 . The method of claim 3 , wherein partial knowledge of a linear transform comprises divergent projections.

5 . The method of claim 2 , wherein generating an estimated image using the ROI data comprises:
accessing a first estimated image; determining a first estimated data based on the first estimated image; comparing the first estimated data with the ROI data; and generating the estimated image based, at least in part, on comparing the first estimated data with the ROI data.

6 . The method of claim 5 , wherein comparing the first estimated data with the ROI data comprises determining a difference between the first estimated data and the ROI data.

7 . The method of claim 6 , wherein generating the estimated image comprises:
generating an intermediate image based on the difference between the first estimated data and the ROI data; and combining the intermediate image with the first estimated image to generate the estimated image.

8 . The method of claim 1 , wherein constraining variation of the estimated image comprises constraining total variation of the estimated image.

9 . The method of claim 8 , wherein constraining total variation of the estimated image comprises constraining first order total variation of the estimated image.

10 . The method of claim 8 , wherein constraining total variation of the estimated image comprises constraining multiple order total variation of the estimated image.

11 . The method of claim 8 , wherein constraining total variation of the estimated image comprises constraining first order and multiple order total variation of the estimated image.

12 . The method of claim 8 , wherein constraining total variation of the estimated image generates a new estimated image, and
wherein iteratively generating an estimated image using the ROI data and constraining variation of the estimated image comprises:
generating new estimated data from the new estimated image; comparing the new estimated data with the ROI data;
generating a second iteration estimated image based on comparing the new estimated data with the ROI data; and
constraining variation of the second iteration estimated image.

13 . The method of claim 12 , wherein comparing the new estimated data with the ROI data comprises determining a difference between the new estimated data and the ROI data; and
wherein the iteration is performed until the difference between the new estimated data and the ROI data is less than a predetermined amount.

14 . The method of claim 1 , wherein the imaging comprises computed tomography.

15 . The method of claim 14 , wherein the divergent beam comprises a fan beam.

16 . A method of obtaining an image of an object using divergent x-ray beam computed tomography comprising:
collecting data less than that sufficient to reconstruct an exact image of the object; and reconstructing the image of the object via an l 1 minimization of a sparse representation of the object.

17 . The method of claim 16 , wherein the data comprises less than that sufficient to reconstruct a mathematically exact image of the object.

18 . A system for obtaining an image of at least a part of a region of interest (ROI) using a divergent beam, the system comprising logic for:
generating ROI data using the divergent beam; and in order to obtain the image of the at least a part of the ROI, iteratively generating an estimated image using the ROI data and constraining variation of the estimated image.

19 . The system of claim 18 , wherein the logic for generating ROI data using the divergent beam comprises collecting partial ROI data.

20 . The system of claim 19 , wherein the partial ROI data comprises partial knowledge of a linear transform of the image.

Assignees

• Univ Chicago

Inventors

• Pan Xiaochuan M
• Sidky Emil
• Kao Chien-Min

Classifications

• G06T12/10

  Image preprocessing, e.g. calibration, positioning of sources or scatter correction · CPC title

• G06T12/20 Primary

  Inverse problem, i.e. transformations from projection space into object space · CPC title

• G06T11/006 Primary

  Physics · mapped topic

• G06T11/005

  Physics · mapped topic

• G06T2211/424

  Iterative · CPC title