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Title:

Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Graf, Robert; Schmitt, Joachim; Schlaeger, Sarah; Möller, Hendrik Kristian; Sideri-Lampretsa, Vasiliki; Sekuboyina, Anjany; Krieg, Sandro Manuel; Wiestler, Benedikt; Menze, Bjoern; Rueckert, Daniel; Kirschke, Jan Stefan
Abstract:
BACKGROUND: Automated segmentation of spinal magnetic resonance imaging (MRI) plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures is challenging. METHODS: This retrospective study, approved by the ethical committee, involved translating T1-weighted and T2-weighted images into computed tomography (CT) images in a total of 263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared two-dimensio...     »
Journal title abbreviation:
Eur Radiol Exp
Year:
2023
Journal volume:
7
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s41747-023-00385-2
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37957426
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Professur für AI for Image-Guided Diagnosis and Therapy (Prof. Wiestler); Professur für Neuroradiologie (Prof. Zimmer)
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