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Abstract #2144

Optimizing data efficiency in SENCEFUL-based lung perfusion studies

Andreas Max Weng1, Tobias Wech1, Lenon Mendes Pereira1, Simon Veldhoen1, Andreas Steven Kunz1, Thorsten Alexander Bley1, and Herbert Köstler1

1Department of Diagnostic and Interventional Radiology, University Hospital of Würzburg, Würzburg, Germany

SElf-gated Non-Contrast-Enhanced FUnctional Lung imaging (SENCEFUL) allows assessment of lung ventilation and perfusion without the use of contrast agent or ionizing radiation. The original implementation, however, is rather inefficient in terms of data usage when reconstructing perfusion weighted datasets, as it analyzes data from a single breathing state only. In this study we present an approach that uses data from all breathing states, aiming at an improved quality of the resulting perfusion maps. A registration algorithm was applied for this purpose.

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