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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).

You Zhang¹, Hua-Chieh Shao¹, Tinsu Pan²

¹Advanced Imaging and Informatics in Radiation Therapy (AIRT) Laboratory, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, United States of America.

Physics in Medicine and Biology

|January 13, 2023

Summary

This summary is machine-generated.

We developed a new method called Simultaneous Spatial and Temporal Implicit Neural Representation (STINR) for reconstructing dynamic cone-beam CT (CBCT) images. STINR accurately tracks tumor motion and improves image quality for image-guided radiation therapy.

Keywords:

cone-beam CT dynamic imaging image reconstruction implicit neural representation motion modeling principal component analysis

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Area of Science:

Medical Imaging
Radiotherapy
Computational Imaging

Background:

Dynamic cone-beam CT (CBCT) is crucial for image-guided radiation therapy, enabling tumor motion tracking and dose accumulation.
Reconstructing dynamic CBCT is challenging due to limited projection data, posing a spatiotemporal inverse problem.

Purpose of the Study:

To develop and evaluate a novel method for accurate dynamic CBCT reconstruction.
To address the challenges of limited data and complex motion in dynamic CBCT.

Main Methods:

Developed Simultaneous Spatial and Temporal Implicit Neural Representation (STINR) using multi-layer perceptrons (MLPs) for spatial and temporal mapping.
Integrated Principal Component Analysis (PCA)-based motion models to reduce temporal complexity.
Validated STINR using XCAT phantoms and patient 4D-CBCT data with various motion scenarios.

Main Results:

STINR achieved higher image reconstruction and motion tracking accuracy compared to traditional PCA and polynomial-fitting methods.
Lung target motion was tracked with an average center-of-mass error of 1-2 mm.
Reconstructed dynamic CBCTs showed relative errors around 10%.

Conclusions:

STINR provides a general framework for accurate dynamic CBCT reconstruction in image-guided radiotherapy.
This one-shot learning method is robust, does not require pre-training, and offers natural super-resolution.
The STINR framework is adaptable to other imaging modalities.