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Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

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Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.

Sungwon Yoon¹, Angel R Pineda, Rebecca Fahrig

¹Department of Radiology, Stanford University, Stanford, California 94305, USA. swyoon@stanford.edu

Medical Physics

|June 10, 2010

Summary

A new iterative tomographic reconstruction algorithm simultaneously segments and reconstructs images, improving image quality. This method enhances reconstructions from limited projection data by enforcing prior information.

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Area of Science:

Medical imaging
Image reconstruction
Computational imaging

Background:

Tomographic reconstruction is crucial for medical imaging.
Limited projection data poses challenges for image quality.
Simultaneous segmentation and reconstruction can improve accuracy.

Purpose of the Study:

To develop an iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs.
To apply this algorithm to sparse projection data.
To evaluate its performance against conventional methods.

Main Methods:

Utilized a two-phase level set method for segmentation.
Integrated segmentation with iterative tomographic reconstruction.
Employed a penalized likelihood function with prior information (piecewise smoothness, bounded intensities) to handle sparse data.
Implemented gradient projection conjugate gradient for intensity updates.

Main Results:

Achieved 6%-13% improvement in normalized root mean square error compared to conventional methods.
Demonstrated effectiveness on both simulated and real fan-beam projection data.
The algorithm successfully segmented regions, preserved edges, and smoothed noise.

Conclusions:

The proposed simultaneous segmentation and reconstruction algorithm enhances image quality.
It effectively segments the reconstruction domain and preserves important image features.
The framework offers flexibility for incorporating various prior constraints.