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GPU-based fast low-dose cone beam CT reconstruction via total variation.

Xun Jia¹, Yifei Lou, John Lewis

¹Center for Advanced Radiotherapy Technologies, Department of Radiation Oncology, University of California San Diego, La Jolla, CA, USA.

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|May 25, 2011

Summary

This study presents a fast GPU algorithm for high-quality Cone-beam CT (CBCT) reconstruction from low-dose, undersampled data. The method significantly reduces radiation exposure in image-guided radiation therapy.

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Area of Science:

Medical Physics
Image Reconstruction
Radiotherapy Technology

Background:

Radiation dose from serial Cone-beam CT (CBCT) scans is a clinical concern in image-guided radiation therapy (IGRT).
High-quality CBCT reconstruction typically requires a substantial number of projections, leading to increased patient dose.
Reducing imaging dose while maintaining diagnostic image quality is crucial for IGRT.

Purpose of the Study:

To develop a fast, GPU-based algorithm for reconstructing high-quality CBCT images from undersampled and noisy projection data.
To significantly lower the X-ray imaging dose associated with CBCT in IGRT procedures.
To validate the algorithm's performance using digital and physical phantoms, as well as a clinical patient case.

Main Methods:

Developed a GPU-accelerated forward-backward splitting algorithm to minimize an energy functional for CBCT reconstruction.
Incorporated a total variation regularization term and a multi-grid technique to enhance image quality from sparse data.
Tested the algorithm on digital phantoms, a head-and-neck patient case, and physical phantoms under low milliampere-second (mAs) conditions.

Main Results:

Satisfactory CBCT image quality was achieved using only 40 X-ray projections.
Successful reconstruction was demonstrated with dose levels as low as 0.1 mAs per projection.
Achieved an overall 36-fold dose reduction compared to conventional protocols (360 projections at 0.4 mAs/projection).
Reconstruction time was approximately 130 seconds on an NVIDIA Tesla C1060 GPU, indicating a ~100x speed improvement over similar iterative methods.

Conclusions:

The developed GPU-based algorithm enables high-quality CBCT reconstruction from significantly undersampled and low-dose projection data.
This approach offers a substantial reduction in radiation exposure for patients undergoing IGRT.
The algorithm's speed and efficiency make it suitable for clinical implementation in IGRT workflows.