# An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation

## TL;DR

This paper introduces an optical flow-based interpolation method for 3D biomedical images that reduces anisotropy and minimizes divergence in velocity data, improving the quality of flow velocity measurements.

## Contribution

It presents a novel optical flow framework specifically designed for velocity data interpolation that also enforces divergence minimization, enhancing flow field accuracy.

## Key findings

- Effective reduction of anisotropy in 3D velocity datasets
- Improved accuracy in flow velocity interpolation
- Minimized divergence in interpolated flow fields

## Abstract

Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.