- abstract = {<b>Background:</b> Cardiac Cine Magnetic Resonance Imaging (MRI) provides dynamic visualization of the heart s structure and function but is hindered by slow acquisition, requiring repeated breath-holds that challenge sick patients. Accelerated imaging can mitigate these issues but potentially reduce spatial and temporal resolution. Therefore, innovative approaches are essential to ensure effective performance under high acceleration conditions. Deep learning-based reconstruction methods show promise in enhancing image quality from highly undersampled data, accelerating scans while maintaining diagnostic accuracy. However, they often fail to effectively exploit the spatio-temporal features inherent to cine MRI, which are essential for accurate reconstruction, th},
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