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Abstract #2103

Physics-driven deep unsupervised correction of susceptibility artifacts in blip-reversed multi-echo EPI for rapid T2 mapping

Abdallah Zaid Alkilani1,2, Mustafa Utkur3,4, Cemre Ariyurek3,4, Sila Kurugol3,4, Tolga Çukur1,2,5, Onur Afacan3,4, and Emine Ulku Saritas1,2
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Radiology, Harvard Medical School, Boston, MA, United States, 4Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, 5Neuroscience Graduate Program, Bilkent University, Ankara, Turkey

Synopsis

Keywords: Quantitative Imaging, Artifacts, Echo planar imaging, Multi-echo, Susceptibility artifacts, Distortion correction, Brain, Deep learning, Unsupervised learning, Computational efficiency

Motivation: Susceptibility artifacts in EPI are a barrier to image quality and anatomical accuracy, particularly challenging in multi-echo data due to varying distortions across separate echo times.

Goal(s): Our goal is to devise a physics-driven, deep unsupervised model for efficient and robust artifact correction in multi-echo EPI scans.

Approach: We developed a multi-echo Forward-Distortion network (meFD-Net) with parallel decoding pathways to process multi-echo sequences, synthesizing reversed-phase-encode pairs per echo for unsupervised learning and field estimation.

Results: meFD-Net achieves competitive artifact correction with the gold-standard TOPUP method, while significantly shortening processing time and demonstrating high fidelity to anatomical structures in multi-echo EPI.

Impact: Applicability on multi-echo EPI scans, high anatomical accuracy and fast processing times enabled by meFD-Net significantly enhances feasibility in clinical and research settings. These advancements can facilitate real-time EPI applications through efficient, artifact-free imaging across diverse conditions.

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