Reliability of Whole-Liver Liver-Fat-Quantification Between Deep Learning–Accelerated and Standard Volumetric Interpolated Breath-hold Examination Dixon Sequences in a Prospective Oncology Cohort
Liver fat quantification using highly accelerated, deep learning-enhanced MRI sequences enables reliable assessment of liver fat content with a significant reduction in scan time in low fat-fraction ranges.
Authors
- David Klemm
- Lea Michel (ORCID: https://orcid.org/0000-0001-6104-4646)
- Vlad Sacalean
- Fabian Bamberg (ORCID: https://orcid.org/0000-0002-7460-3942)
- Ralph Strecker (ORCID: https://orcid.org/0000-0002-8232-8952)
- Maximilian Frederik Russe (ORCID: https://orcid.org/0000-0003-3187-2429)
- Anna Maria Fink (ORCID: https://orcid.org/0009-0002-9903-283X)
- Stephan Rau (ORCID: https://orcid.org/0000-0002-8992-2401)
- Alexander Rau (ORCID: https://orcid.org/0000-0001-5881-6043)
- M. Nickel (ORCID: https://orcid.org/0000-0001-6141-5857)
- Kai Falko Kästingschäfer
Institutions
- Siemens (Germany) (DE)
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
- Siemens Healthcare (United States) (US)
- Siemens Healthineers (Germany) (DE)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1097/rli.0000000000001242
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00