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Related Concept Videos

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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Related Experiment Video

Updated: Jun 29, 2025

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

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

Published on: April 13, 2013

How auto-differentiation can improve CT workflows: classical algorithms in a modern framework.

Richard Schoonhoven, Alexander Skorikov, Willem Jan Palenstijn

|April 4, 2024

Summary

This summary is machine-generated.

This study applies deep learning principles to classical computed tomography (CT) algorithms. Auto-differentiation enhances CT workflows by integrating classical and machine learning methods for improved performance.

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Last Updated: Jun 29, 2025

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Area of Science:

Medical Imaging
Computer Science
Algorithm Design

Background:

Deep learning successes rely on flexible computational blocks and automatic differentiation.
Classical algorithms often operate independently, limiting workflow optimization.

Purpose of the Study:

To adapt deep learning's composable framework and auto-differentiation to classical algorithms.
To enhance computed tomography (CT) workflows using these principles.

Main Methods:

Applied four design principles: end-to-end optimization, explicit quality criteria, declarative algorithm construction, and using classical algorithms as blocks.
Focused on computed tomography (CT) as the application field.
Utilized auto-differentiation to integrate classical and machine learning algorithms.

Main Results:

Demonstrated the effectiveness of auto-differentiation beyond neural network training.
Showcased successful application in four diverse CT workflow case studies.
Validated the integration of classical and machine learning algorithms within CT.

Conclusions:

Auto-differentiation is a powerful tool for optimizing complex workflows, including those in medical imaging.
The composable block philosophy can significantly enhance classical algorithms, extending their capabilities.
This approach offers a unified framework for combining diverse computational methods in CT.