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Abstract #3812

Motion-Dependent L1 Minimization for Dynamic Cardiac MRI Reconstruction

Qiu Wang1, Jun Liu1, Zhili Yang1, Nicolas Chesneau1, Michael O. Zenge2, Michaela Schmidt2, Nirmal Janardhanan1, Edgar Mueller2, Mariappan S. Nadar1

1Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, NJ, United States; 2MR Application & Workflow Development, Siemens AG, Healthcare Sector, Erlangen, Bavaria, Germany

High temporal resolution is often desired in Cardiac Magnetic Resonance Imaging (CMRI). Compressed sensing has enables the reconstruction with a reduced the number of acquired frequencies, hence accelerating the acquisition. Spatial-temporal regularization has been proven effective for enforcing the smoothness. However, the fine details of the heart such as valve leaflets can be eliminated by strong regularization. In this work, a new approach was proposed for dynamic cardiac MRI reconstruction by setting the motion-dependent L1 regularization. Experiments conducted on CMRI data demonstrate the effectiveness of the proposed approach in preserving fine details of the heart.

Keywords

accelerates acquisition addition aliasing application applied approaches assessment assign assigned available becomes blur blurry boxed cardiac cardiovascular caused channels circles clinical coefficients coil commercially component computer computing construct corporate corporation correlation deal denoted denotes dependent details detect development deviation differently dimension dimensions disclaimer dynamic effective effectiveness eliminate enforce entire entries equal estimation field fine firstly formula formulation frequencies function greater healthy heart helps included incorporating indicate inputs inside introduce keeping leaflets located locations mark marked matrices matrix might minimization missing motion moving noise operator optimization original overcome paper parallel penalization pixel pixels plot plots preserving prevents problem product proposed proven reconstruction reducing redundant regular regularization relatively replace report resolution respectively secondly sector selected sense setting smoothing smoothness solved spatial specified static step strong stronger structure summary system take target technical technology temporal tensor third thirdly threshold trigger type valve vector verify versions versus vertical vision volunteer wavelet wavelets whether yield zoom