interpolation w/ cubic convolution kernel: boundary treatment?

Hi,

I am using the symmetric cubic convolution kernel ("Catmull-Rom splines") to interpolate data over a limited range in a variable x. For the interpolation I am using typically 10 nodes which are equidistant in x.
Example: The interpolated function between nodes 4 and 5 is computed based on the data points at nodes 3,4,5,6
A nice property of the Catmull-Rom splines is that I get a continuous 1st derivative everywhere.

My question is now: how should I treat the ranges near the boundary?
With 10 nodes, how should I interpolate the data between node 9 and 10? So far I am using linear interpolation here - but this is conceptually ugly (and it's not precise, although the latter is not my biggest problem since I want a _nice_ solution).
I am especially worried that in my current approach the interpolated function has no continuous 1st derivative at node 9.

Is there a solution, in which I could use e.g. a non-symmetric convolution kernel to interpolate between nodes 9 and 10, based on the nodes 8,9,10 or maybe 7,8,9,10 - in a way that I get a continuous 1st derivative everywhere?
I have not found any discussion about the boundary-treatment in the literature (and neither on the Google-wide web). In image processing sometimes people mirror the image at the boundaries (i.e. they would introduce a hypothetical 11th node for which the data value is set equal to the data at the 9th node and then interpolate using the data points at nodes 8,9,10,11) - but this would not work in my case.
.

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