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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 3, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Local response prediction in model-based CT material decomposition.

Wenying Wang¹, Steven Tilley¹, Matthew Tivnan¹

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA 21205.

Proceedings of Spie--The International Society for Optical Engineering

|October 29, 2020

Summary

This summary is machine-generated.

Spectral CT uses energy-dependent data for material decomposition. This study introduces a method to precisely control regularization effects between material bases, enhancing image quality and tuning parameters for specific spectral CT imaging goals.

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Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Spectral computed tomography (CT) enables material decomposition and density estimation by utilizing energy-dependent measurement information.
Model-based material decomposition (MBMD) algorithms, incorporating statistical models and advanced regularization, aim to improve density estimation accuracy and reduce radiation dose.
The interplay between regularization strategies and image properties across different material bases in spectral CT is complex and not fully understood.

Purpose of the Study:

To derive a closed-form set of local impulse responses for regularized MBMD solutions.
To quantify the spatial resolution within each material image.
To characterize the cross-influence of regularization between different material bases.

Main Methods:

Derivation of local impulse response functions for a general regularized MBMD objective.
Analysis of the derived impulse responses to predict image properties.
Development of predictors for spatial resolution and inter-basis regularization influence.

Main Results:

A closed-form solution for local impulse responses in regularized MBMD was successfully derived.
The derived predictors accurately quantify spatial resolution in individual material images.
The method quantifies the impact of regularization in one material basis on other material images, revealing complex interactions.

Conclusions:

The derived impulse response predictors offer a powerful tool for understanding and controlling regularization in spectral CT.
This framework allows for prospective tuning of regularization parameters to optimize image quality for specific spectral CT applications.
The findings facilitate improved MBMD algorithm design and spectral CT image analysis.