Refresher Course: Hybrid, Molecular and Translational Imaging

RC 206 - How AI can help in hybrid imaging

Lectures

1
Chairperson's introduction

Chairperson's introduction

04:00Clemens C. Cyran, Munich / DE

2
Data acquisition and post-processing: is high-throughput hybrid imaging soon possible?

Data acquisition and post-processing: is high-throughput hybrid imaging soon possible?

12:00Johannes Haubold, Essen / DE

3
Automatic segmentation in PET imaging: are we there yet?

Automatic segmentation in PET imaging: are we there yet?

12:00Lalith Kumar Shiyam Sundar, Vienna / AT

4
Integrative analysis of multimodality data

Integrative analysis of multimodality data

12:00Bram Van Ginneken, Nijmegen / NL

5
Let's dive in: case-based use of AI in oncologic hybrid imaging

Let's dive in: case-based use of AI in oncologic hybrid imaging

12:00Mathieu Hatt, Brest / FR

6
Panel discussion: Let's be real: what is possible with AI in hybrid imaging today?

Panel discussion: Let's be real: what is possible with AI in hybrid imaging today?

08:00Panel discussion: Let's be real: what is possible with AI in hybrid imaging today?

4 min
Chairperson's introduction
Clemens C. Cyran, Munich / Germany
12 min
Data acquisition and post-processing: is high-throughput hybrid imaging soon possible?
Johannes Haubold, Essen / Germany
  1. To learn about different post-processing mechanisms and systematic data acquisition principles.
  2. To appreciate the challenges in acquiring and processing high-quality data.
  3. To understand the opportunity and demand of high-throughput imaging.
12 min
Automatic segmentation in PET imaging: are we there yet?
Lalith Kumar Shiyam Sundar, Vienna / Austria
  1. To learn about the technical basics of image segmentation.
  2. To appreciate the specific challenges of working with hybrid imaging data.
  3. To understand automated PET imaging segmentation's current status and future prospects.
12 min
Integrative analysis of multimodality data
Bram Van Ginneken, Nijmegen / Netherlands
  1. To learn about multimodal data, sources and qualities.
  2. To appreciate the difficulty in integrating data from various sources.
  3. To understand the need for integrative diagnostics
12 min
Let's dive in: case-based use of AI in oncologic hybrid imaging
Mathieu Hatt, Brest / France
  1. To learn about currently available AI solutions for oncologic hybrid imaging.
  2. To appreciate how AI-based solutions can aid everyday clinical work with hybrid imaging.
  3. To understand the importance of critical appreciation and reflection on clinical AI applications.
8 min
Panel discussion: Let's be real: what is possible with AI in hybrid imaging today?

This session offers AI-generated subtitles.