EUROSAFE IMAGING SESSION

EU 18 - Artificial intelligence for dose optimisation

Lectures

1
EU 18_1 - Technology using AI for radiation protection

EU 18_1 - Technology using AI for radiation protection

19:33Mika Kortesniemi, Helsinki / FI

1. To understand the revised process of radiation protection and optimisation.
2. To understand the need for more comprehensive imaging quality data extending to the clinical level.
3. To learn how radiomics and AI may help in more clinically adjusted and quantitative optimisation.

2
EU 18_2 - What is the limit of dose reduction by artificial intelligence methods: 2D and 3D?

EU 18_2 - What is the limit of dose reduction by artificial intelligence methods: 2D and 3D?

22:22Christoph Hoeschen, Magdeburg / DE

1. To name a number of very drastic claims of dose reduction using AI in x-ray based imaging.
2. To show that AI methods will, for diagnostic purposes, be limited based on physical information theory aspects, and why some approaches seem to be able to go beyond due to methodological errors.
3. To learn exemplary methods on how to detect methodological errors and construct test cases for ensuring to avoid such errors in your own research.

3
EU 18_3 - Chances and limitations of AI for nuclear medical imaging

EU 18_3 - Chances and limitations of AI for nuclear medical imaging

22:30Christoph Hoeschen, Magdeburg / DE

1. To understand the nuclear imaging processes and to identify in which steps AI can be of help.
2. To focus on some selected applications, e.g. towards reduction of the administered dose or costs of scanners.
3. To analyse possible drawbacks and bottlenecks.

EU 18-1
Technology using AI for radiation protection
Learning Objectives
1. To understand the revised process of radiation protection and optimisation. 2. To understand the need for more comprehensive imaging quality data extending to the clinical level. 3. To learn how radiomics and AI may help in more clinically adjusted and quantitative optimisation.
EU 18-2
What is the limit of dose reduction by artificial intelligence methods: 2D and 3D?
Learning Objectives
1. To name a number of very drastic claims of dose reduction using AI in x-ray based imaging. 2. To show that AI methods will, for diagnostic purposes, be limited based on physical information theory aspects, and why some approaches seem to be able to go beyond due to methodological errors. 3. To learn exemplary methods on how to detect methodological errors and construct test cases for ensuring to avoid such errors in your own research.
EU 18-3
Chances and limitations of AI for nuclear medical imaging
Learning Objectives
1. To understand the nuclear imaging processes and to identify in which steps AI can be of help. 2. To focus on some selected applications, e.g. towards reduction of the administered dose or costs of scanners. 3. To analyse possible drawbacks and bottlenecks.

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