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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

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CT IMAGE RECONSTRUCTION ON A LOW DIMENSIONAL MANIFOLD.

Wenxiang Cong¹, Ge Wang¹, Qingsong Yang¹

¹Biomedical Imaging Center, Department of Biomedical Engineering Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Inverse Problems and Imaging (Springfield, Mo.)

|November 21, 2022

Summary

This study introduces a low-dimensional manifold model (LDMM) for X-ray computed tomography (CT) image reconstruction. The LDMM enhances image detail and resolution, outperforming traditional methods.

Keywords:

CT image reconstruction Primary: 68U10 Secondary: 65K10, 65K05 filtered backprojection (FBP)low dimensional manifold model (LDMM)simultaneous algebraic reconstruction technique (SART)total variation (TV)

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Area of Science:

Medical Imaging
Image Processing
Computational Science

Background:

Natural image patch manifolds exhibit low-dimensional structures rich in information.
Existing methods for X-ray computed tomography (CT) image reconstruction can be limited in detail and resolution.

Purpose of the Study:

To apply the low-dimensional manifold model (LDMM) for regularizing X-ray CT image reconstruction.
To enhance spatial and contrast resolution in CT images.

Main Methods:

The low-dimensional manifold model (LDMM) was applied to regularize CT image reconstruction.
Performance was evaluated using simulated and clinical experimental data.
Comparative analysis was conducted against simultaneous algebraic reconstruction technique (SART) with total variation (TV) regularization.

Main Results:

The LDMM-based method successfully recovered detailed structural information.
Significant enhancements in spatial and contrast resolution were observed.
The proposed method demonstrated superior accuracy, fidelity, and contrast resolution compared to SART-TV.

Conclusions:

The LDMM provides an effective regularization approach for X-ray CT image reconstruction.
This method significantly improves image quality, offering high fidelity and contrast resolution.
The LDMM-based technique shows promise for advanced medical imaging applications.