# Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views

## Abstract

A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object. 5 figs.

- Inventors:

- Issue Date:

- Research Org.:
- Univ. of California (United States)

- Sponsoring Org.:
- USDOE, Washington, DC (United States)

- OSTI Identifier:
- 563708

- Patent Number(s):
- 5689629

- Application Number:
- PAN: 8-571,330

- Assignee:
- Univ. of California, Oakland, CA (United States)

- DOE Contract Number:
- W-7405-ENG-48

- Resource Type:
- Patent

- Resource Relation:
- Other Information: PBD: 18 Nov 1997

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; IMAGE PROCESSING; OPTIMIZATION; ITERATIVE METHODS; ALGORITHMS; DIMENSIONS

### Citation Formats

```
Lee, H R.
```*Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views*. United States: N. p., 1997.
Web.

```
Lee, H R.
```*Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views*. United States.

```
Lee, H R. Tue .
"Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views". United States.
```

```
@article{osti_563708,
```

title = {Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views},

author = {Lee, H R},

abstractNote = {A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object. 5 figs.},

doi = {},

journal = {},

number = ,

volume = ,

place = {United States},

year = {1997},

month = {11}

}