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Abstract #2310

Low-rank and sparse matrix decomposition for accelerated non-contrast-enhanced functional lung MRI

Efe Ilicak1, Jascha Zapp1, Lothar R. Schad1, and Frank Zoellner1
1Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany

Lung functions have significant clinical value for diagnosis of pulmonary diseases. Fourier Decomposition is a non-contrast-enhanced method for assessing pulmonary functions from time-resolved images. However, its performance depends on temporal resolution. Here we propose two compressed sensing reconstruction strategies based on low-rank and sparse matrix decomposition. Retrospective demonstrations on in vivo acquisitions demonstrate the performance of these techniques, enabling improved scan efficiency without degrading image quality.

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