• Jump to content
  • Jump to navigation
  • Jump to bottom of page
Simulate organization breadcrumb open Simulate organization breadcrumb close
Pattern Recognition Lab
  • FAUTo the central FAU website
  • Campo
  • UnivIS
  • Jobs
  • Map
  • Help

Pattern Recognition Lab

Navigation Navigation close
  • Overview
    • Contact
    • Directions
    Portal Overview
  • Team
    • Former PRL members
    Portal Team
  • Research
    • Research Groups
    • Research Projects
    • Pattern Recognition Blog
    • Beyond the Patterns
    • Publications
    • Research Demo Videos
    • Datasets
    • Competitions
    Portal Research
  • Teaching
    • Curriculum / Courses
    • Lecture Notes
    • Lecture Videos
    • Thesis / Projects
    • Free Machine and Deep Learning Resources
    • Free Medical Engineering Resources
    • LME Videos
    Portal Teaching
  • Lab
    • News
    • Ph.D. Gallery
    • Cooperations
    • Join the Pattern Recognition Lab
    Portal Lab
  1. Home
  2. Research
  3. Research Groups
  4. Other Projects
  5. Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation

Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation

In page navigation: Research
  • Beyond the Patterns
  • Competitions
  • Publications
  • Datasets

Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation

Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation

(Non-FAU Project)

Overall project:
Project leader: Jonas Denck
Project members:
Start date: September 1, 2017
End date:
Acronym:
Funding source:
URL:

Abstract

Research project in cooperation with Siemens Healthineers, Erlangen

Magnetic Resonance Imaging (MRI) is an important but complex imaging modality in current radiology. Artificial intelligence (AI) can play an important role for acclerating MR sequence acquisition as well as supporting image interpretation and diagnosis. However, there are also opportunities besides image acquisition and interpretation for which AI can play a vital role to optimze the clinical workflow and decrease costs. 

Automated Protocoling

One critical workflow step for an MRI exam is protocoling, i.e., selecting an adequate imaging protocol under consideration of the ordered procedure, clinical indication, and medical history. Due to the complexity of MRI exams and the heterogeneity of MR protocols, this is a nontrivial task. The aim of this project is to analyze and quantify challenges complicating a robust approach for automated protocoling, and propose solutions to these challenges.

Automated Billing

Moreover, reporting and documentation is a crucial step in the radiology workflow. We have therefore automated the selection of billing codes from modality log data for an MRI exam. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding administrative task, enhance the MRI workflow, and prevent coding errors.

Publications

Friedrich-Alexander-Universität
Erlangen-Nürnberg

Schlossplatz 4
91054 Erlangen
  • Login
  • Intranet
  • Imprint
  • Privacy
  • Accessibility
Up