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Abstract #0377

Tumor likelihood estimation on MRI prostate data by utilizing k-Space information

Moritz Rempe1, Fabian Hörst1, Constantin Seibold1, Boris Hadaschik2, Marco Schlimbach3, Jan Egger1, Kevin Kröninger3, Felix Breuer4, Martin Blaimer4, and Jens Kleesiek1
1Institute for AI in medicine, Essen, Germany, 2Department of Urology, University Hospital Essen, Essen, Germany, 3Technical University Dortmund, Dortmund, Germany, 4Fraunhofer Institute for Integrated Circuits IIS, Würzburg, Germany

Synopsis

Keywords: Diagnosis/Prediction, Diffusion Acquisition, k-Space

Motivation: In MRI, k-Space data is currently not directly used in the classification process, even though it may hold valuable additional information.

Goal(s): This study aims to assess whether MRI k-Space data can directly - without image reconstruction - and accurately predict prostate cancer likelihood, reducing the need for image-domain reconstructions and enabling higher undersampling rates.

Approach: A principal component analysis based coil compression pipeline was developed to process MRI k-Space data, tested with varying undersampling factors to simulate accelerated scans.

Results: The model achieved an AUROC of 86.1% with fully sampled data and 78.0% at 16x undersampling, demonstrating robust predictions without canonical reconstruction methods.

Impact: This study enables faster, reliable MRI-based prostate cancer predictions by utilizing k-Space raw data. It opens new possibilities for real-time diagnostics and broader applications of raw MRI data across clinical imaging.

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Keywords