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Abstract #2766

Accelerated FLAIR imaging at 0.6T using T2W-guided multi-contrast deep learning-based reconstruction using a Zero-Shot approach

Navid Jabarimani1,2, Chinmay Rao2, Ece Ercan3, Yiming Dong1, Nicola Pezzotti4, Mariya Doneva5, Matthias J.P van Osch1, Marius Staring2, and Martijn Nagtegaal1
1C.J. Gorter MRI Center, Radiology, Leiden University Medical Center, Leiden, Netherlands, 2Division of Image Processing, Radiology, Leiden University Medical Center, Leiden, Netherlands, 3MR Clinical Science, Philips Healthcare, Best, Netherlands, 4Philips Cardiologs, Paris, France, 5Philips Innovative Technologies, Hamburg, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Brain, Low-field MRI, Image reconstruction, Machine Learning/Artificial Intelligence

Motivation: Mid-field MRI (0.6T) is an accessible alternative for clinical brain imaging; however, FLAIR images, essential for identifying lesions and edema, often exhibit reduced quality at this field strength.

Goal(s): We aimed to improve the accelerated FLAIR scans at 0.6T with deep learning while addressing the challenge of limited 0.6T datasets.

Approach: A two-contrast content/style model was trained on an unpaired 3T image dataset and was applied zero-shot to reconstruct FLAIR images with T2W guidance at 0.6T.

Results: The proposed method outperformed standard compressed sensing, demonstrating its potential for enhancing mid-field FLAIR image quality for further clinical applications.

Impact: We showed that FLAIR and T2-weighted scans can be used for contrast/style-based reconstruction methods, even when trained at 3T data directly applied to 0.6T data. Resulting improved image quality improves the usability of mid-field MRI.


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Keywords