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Abstract #2181

The sensitivity of classical and deep image similarity metrics to MR acquisition parameters

Veronica Ravano1,2,3, Gian Franco Piredda1,2,3, Tom Hilbert1,2,3, Bénédicte Maréchal1,2,3, Reto Meuli2, Jean-Philippe Thiran2,3, Tobias Kober1,2,3, and Jonas Richiardi2
1Advanced Clinical Imaging Technology, Siemens Healthineers, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Post-acquisition data harmonization promises to unlock multi-site data for deep learning applications. In turn, this rests on measuring image similarity. Here, we investigate the sensitivity of several similarity measures to fourteen acquisition protocols of a 3D T1-weighted (MPRAGE) contrast. Standard similarity metrics, a deep perceptual loss and a segmentation loss are extracted between image pairs and compared. The perceptual loss is highly correlated with L1 distance and outperforms other metrics in detecting acquisition parameter changes. The segmentation loss, however, is poorly correlated with other metrics, suggesting that these image similarity metrics alone aren't sufficient to harmonize data for clinical applications.

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