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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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Updated: Dec 19, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

Justin Solomon¹, Peijei Lyu², Daniele Marin²

¹Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.

Medical Physics

|June 8, 2020

Summary

This study evaluated a deep learning-based CT reconstruction algorithm, finding it significantly reduces noise magnitude and maintains high-contrast resolution. However, it exhibits non-stationary noise and reduced low-contrast resolution, similar to iterative methods.

Keywords:

computed tomography deep learning image quality image reconstruction noise power spectrum

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Area of Science:

Medical Imaging
Radiology
Image Reconstruction

Background:

Computed tomography (CT) image quality is influenced by noise and spatial resolution.
Deep learning (DL) based reconstruction algorithms offer potential improvements over traditional methods like filtered back-projection (FBP) and iterative reconstruction (IR).

Purpose of the Study:

To characterize the noise properties and spatial resolution of a commercial deep learning-based CT reconstruction algorithm.
To compare its performance against conventional (FBP) and iterative (ASiR-V) algorithms.

Main Methods:

Two phantom experiments were conducted using multisized and custom phantoms at various radiation dose levels.
Noise power spectrum (NPS), task transfer functions (TTF), and noise inhomogeneity were measured.
Images were reconstructed using FBP, GE ASiR-V, and GE True Fidelity (DL-based) algorithms.

Main Results:

DL reconstruction reduced noise magnitude by 68-74% compared to FBP.
Noise texture was marginally different for DL (9% lower NPS f_av) versus substantially lower for ASiR-V (55% lower).
Both DL and ASiR-V showed non-stationary noise and degraded low-contrast resolution (up to 36-42% reduction in TTF f_50%), while high-contrast resolution remained similar across all algorithms.

Conclusions:

The DL algorithm effectively reduces noise magnitude and preserves high-contrast spatial resolution.
It exhibits locally non-stationary noise and reduced low-contrast spatial resolution, characteristic of current IR techniques.