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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

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Ultra-fast digital tomosynthesis reconstruction using general-purpose GPU programming for image-guided radiation

Justin C Park¹, Sung Ho Park, Jin Sung Kim

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

Technology in Cancer Research & Treatment

|July 7, 2011

Summary

This study demonstrates an ultra-fast digital tomosynthesis (DTS) reconstruction technique using a graphics processing unit (GPU). The GPU-based method significantly accelerates image reconstruction, enabling real-time processing for clinical applications.

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Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Digital tomosynthesis (DTS) is a valuable imaging technique.
Traditional reconstruction algorithms can be computationally intensive, limiting real-time applications.
Graphics processing units (GPUs) offer parallel processing capabilities for accelerating complex computations.

Purpose of the Study:

To develop and evaluate an ultra-fast DTS reconstruction technique.
To implement the Feldkamp, Davis, and Kress (FDK) algorithm using general-purpose GPU (GPGPU) programming.
To assess the performance and clinical feasibility of the GPU-based implementation compared to CPU-based methods.

Main Methods:

The FDK algorithm was programmed in C for both GPU and CPU.
Performance was evaluated on 25 patient cases using cone-beam computed tomography (CBCT) data.
Reconstruction times, image quality (pixel differences, CNR), and scalability with volume size were analyzed.

Main Results:

GPU-based implementation achieved reconstruction times of 1.3-2.5 seconds for a 256-slice volume.
This translates to >13-18 projections per second, sufficient for real-time acquisition.
Speed improvements of up to 87 times compared to CPU, with visually identical images and minimal CNR differences.

Conclusions:

The developed GPU-based FDK algorithm enables ultra-fast DTS image reconstruction.
Clinical implementation is highly feasible, virtually eliminating reconstruction time delays.
This acceleration opens possibilities for advanced image processing and real-time applications in medical imaging.