Machine Learning in MRI (ML in MRI)2.5 ECTS
(englische Bezeichnung: Machine Learning in MRI)

Lehrveranstaltungen:

• • Machine Learning in MRI
    (Seminar, 2 SWS, Florian Knoll et al., Mo, 10:00 - 11:30; Vorbesprechung: 2.5.2022, 10:00 - 12:00 Uhr)

Inhalt:

We will cover recent machine learning developments in the areas of Magnetic Resonance (MR) data acquisition, image generation, image analysis and image interpretation. We will go over papers from leading international journals and conferences. Students can either suggest their own topics/papers or select from a range of papers presented by the lecturers. Each student will then study the assigned papers, discuss them with the lecturers and at the end of the semester give a presentation about the key findings.

Lernziele und Kompetenzen:

After completing this course, students will be able to:

• critically read and understand a scientific paper in the fields of medical imaging and machine learning.

• present a complex topic in their own words to their peers.
  

Literatur:

A list of papers will be established individually at the beginning of the semester.
Exemplary papers include:
• Akcakaya et al. 2017: Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging
• Vishnevskiy et al. 2020: Deep variational network for rapid 4D flow MRI reconstruction
• Yoo et al. 2021: Time-Dependent Deep Image Prior for Dynamic MRI

Organisatorisches:

If you want to participate in the seminar, please write an email to Prof. Knoll between 13/04/2022 and 09/05/2022 to register.

Studien-/Prüfungsleistungen:

Machine learning in MRI (Prüfungsnummer: 660650)

(englischer Titel: Machine learning in MRI)

Untertitel: ML in MRI

(englischer Untertitel: ML in MRI)

Seminarleistung, benotet, 2.5 ECTS

weitere Erläuterungen:
50% Presentation to the group during the semester (20min presentation, 10min discussion).
50% written report 3-5 pages at the end of the semester.

Prüfungssprache: Deutsch und Englisch

Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023

1. Prüfer:

Florian Knoll