Pressure measurement has many implications in fluid mechanics and many advances have been made in terms of reconstructing the pressure field by directly integrating the pressure gradient fields originating from time-resolved particle image velocimetry measurements. The majority of the current direct integration algorithms initiate the integration at the domain boundaries where the pressure gradient measurement is often unreliable. In this work, we introduce the boundary-independent shortest path integration algorithm that initiates the integration at the inner measurement domain and grows outwards towards the boundaries. The algorithm calculates a new undeveloped grid point by averaging over millions of line integrations of shortest paths originating from nearby developed grid points. The process iterates until all points in a map are determined. Multiple maps calculated from distributed reference points are averaged to improve calculation accuracy. Using the JHU DNS database, we optimize and validate the proposed integration method. We demonstrate that this integration algorithm can be directly applied to pressure gradient fields containing inner boundaries of different sizes and shapes with no decrease in performance and accuracy. We further show that this method resulted in errors comparable to existing integration methods. Overall, this method represents a simple algorithm for reconstructing the pressure field independent of the boundary conditions and reduces the propagation of errors from the boundaries substantially.

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