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Abstract #3534

Compressive Sensing Reconstruction for Multi-Contrast Data with Unequal Acceleration Rates

Emre Kopanoglu1,2, Alper Güngör1, Toygan Kilic3,4, Emine Ulku Saritas3,4,5, Tolga Çukur3, and H. Emre Guven1

1Department of Advanced Sensing Research Programs, ASELSAN Research Center, Ankara, Turkey, 2School of Psychology / CUBRIC, Cardiff University, Cardiff, United Kingdom, 3Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 4National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 5Neuroscience Program, Bilkent University, Ankara, Turkey

In multi-contrast acquisitions, a critical concern is whether to distribute undersampling uniformly or unequally across contrasts, as scan times and SNR typically vary among sequences. This study investigates a compressive sensing framework in jointly reconstructing multi-contrast data with unequal acceleration rates. Using in-vivo and numerical datasets, the total scan time was fixed and acceleration factors were varied between protocols. The results suggest using lower acceleration rates for protocols with higher-SNR and shorter duration, and higher rates for protocols with lower-SNR and longer duration improves image quality, even in the highly accelerated contrast. The method was also compared to seven state-of-the-art methods from the literature.

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