5 papers
Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning
Lukas T. Rotkopf, Marco Schlimbach, Julius C. Holzschuh +3
Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when data are accelerated or noisy…
Generative Modeling of Complex-Valued Brain MRI Data
Marco Schlimbach, Moritz Rempe, Jessica Mnischek +4
Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence that it encodes tissue propert…
Efficient Complex-Valued Vision Transformers for MRI Classification Directly from k-Space
Moritz Rempe, Lukas T. Rotkopf, Marco Schlimbach +6
Deep learning applications in Magnetic Resonance Imaging (MRI) predominantly operate on reconstructed magnitude images, a process that discards phase information and requires compu…
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
M. Rempe, F. Hörst, C. Seibold +7
We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Moritz Rempe, Fabian Hörst, Helmut Becker +4
Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligen…