Quantitative evaluation of convolution-based methods for medical image interpolation

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A chronology of interpolation: from ancient astronomy to modern signal and image processing (2002, background): “An even more elaborate study was presented by the present author [358], who carried out a cost-performance analysis of a total of 126 kernels with spatial support ranging from two to ten grid intervals.”
Elastic registration of biological images using vector-spline regularization (2005, background): “Cubic B-spline interpolation offers a good tradeoff between accuracy and speed [18], [19].”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, background): “interpolation algorithm [55]–[57] before further processing.”
Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images (2004, methodology): “To obtain accurate reference tracings, required for the quantitative analyses described hereafter, high-resolution versions of the original images were generated by scaling up with a factor 10 using a high-quality interpolation method: cubic spline interpolation (24,25).”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, unknown): “[171], Lehman et al.”