Optimizing high-resolution knee MRI at 3 tesla: conventional acceleration versus deep learning reconstruction

Authors

Dominik Geisel (ORCID: https://orcid.org/0000-0001-7348-0971)
Andreas Pohlmann (ORCID: https://orcid.org/0000-0002-8572-2568)
Markus Herbert Lerchbaumer (ORCID: https://orcid.org/0000-0003-1151-9935)
David Kohnert
Thula Walter (ORCID: https://orcid.org/0000-0002-2003-8351)
Moritz Wagner (ORCID: https://orcid.org/0000-0002-5078-0725)
Dominik Deppe (ORCID: https://orcid.org/0000-0003-4534-0466)
Khalid Baghdadi
L Nitschke
Hassan Ali Alyousef

Institutions

Siemens (Germany) (DE)
Siemens Healthcare (United States) (US)
Charité - Universitätsmedizin Berlin (DE)

Publication Details

Journal: BMC Medical Imaging
Published: 2026-03-03
DOI: https://doi.org/10.1186/s12880-026-02251-0
Primary Topic: Knee injuries and reconstruction techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Optimizing high-resolution knee MRI at 3 tesla: conventional acceleration versus deep learning reconstruction

Dominik Geisel, Andreas Pohlmann, Markus Herbert Lerchbaumer, David Kohnert et al.

Mar 3, 2026

BMC Medical Imaging

Knee injuries and reconstruction techniques

article

Optimizing high-resolution knee MRI at 3 tesla: conventional acceleration versus deep learning reconstruction

Dominik Geisel, Andreas Pohlmann, Markus Herbert Lerchbaumer, David Kohnert, Thula Walter, Moritz Wagner, Dominik Deppe, Khalid Baghdadi, L Nitschke, Hassan Ali Alyousef

March 3, 2026article en

Abstract

No abstract available for this paper.

BMC Medical Imaging

Siemens (Germany) (DE), Siemens Healthcare (United States) (US), Charité - Universitätsmedizin Berlin (DE)

Openalex Percentile: Top 36%

Knee injuries and reconstruction techniques

DOI Open Access PDF OpenAlex

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