Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI

2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time-consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal parametrization of complex-valued images, jointly modeling magnitude and phase across multiple echoes to enable velocity estimation, and leveraging their inductive bias for the reconstruction of the velocity data. Additionally, to compensate for the oversmoothing tendency observed in neural-field reconstructions under severe undersampling, a simple voxel-based postprocessing step is introduced. The method is validated numerically in Cartesian and radial k-space with both high and low temporal resolution data. This approach achieves accurate reconstructions at high acceleration factors, with low errors even at 32 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mo>×</mml:mo> <mml:annotation>$\times$</mml:annotation></mml:semantics> </mml:math> and 64 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mo>×</mml:mo> <mml:annotation>$\times$</mml:annotation></mml:semantics> </mml:math> undersampling for the high temporal resolution data, and 16 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mo>×</mml:mo> <mml:annotation>$\times$</mml:annotation></mml:semantics> </mml:math> for the low temporal resolution data, and consistently outperforms classical locally low-rank regularized voxel-based methods in both flow estimates and anatomical depiction.