This study introduces an iterative kernel low-rank algorithm to recover images in a free breathing and ungated cardiac MRI dataset. The approach relies on the manifold structure of dynamic data to recover it from highly undersampled measurements. The data is acquired using variable density spiral acquisition. An iterative kernel low-rank algorithm is introduced to estimate the manifold structure of the images, or equivalently the manifold Laplacian matrix, from central k-space regions. Unlike previous manifold regularization implementations, the iterative algorithm, coupled with the non-Cartesian acquisitions, eliminates the need for dedicated navigators to estimate the manifold Laplacian, thus improving sampling efficiency.The iterative kernel low-rank algorithm facilitates the extension of manifold regularization to navigatorless spiral acquisitions, thus improving sampling efficiency. This algorithm provides improved reconstruction compared to the state of the art methods.