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Abstract #4358

Ultra-thin slice Time-of-Flight MR angiography for brain with a deep learning constrained Compressed SENSE reconstruction

Jihun Kwon1, Takashige Yoshida2, Masami Yoneyama1, Johannes M Peeters3, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Tokyo Metropolitan Police Hospital, Tokyo, Japan, 3Philips Healthcare, Best, Netherlands


Time-of-flight MR angiography (TOF-MRA) is a non-contrast-enhanced imaging technique widely used to visualize intracranial vasculature. In this study, we investigated the use of ultra-thin slice (up to 0.4 mm) to improve the delineation of the cerebral arteries in TOF-MRA. To reduce the noise while preserving the image quality, Compressed SENSE AI (CS-AI) reconstruction was used. Our results showed that the improved noise reduction by CS-AI enabled better visualization of vessels, especially on the thinner slices compared to conventional Compressed-SENSE. The usefulness of CS-AI was also demonstrated in clinical cases with moyamoya disease and suspected aneurysm patients.

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