3D mouse shape reconstruction based on phase-shifting algorithm for fluorescence molecular tomography imaging system

Abstract

We introduce a fast, low cost, and robust method, based on fringe pattern and phase shifting, to obtain three-dimensional (3D) mouse surface geometry for fluorescence molecular tomography (FMT) imaging. We use two pairs of pico-projector and webcam to project and capture fringe patterns from different views. At first, we calibrate the pico-projectors and the webcams to obtain their system parameters. Each pair of pico-projector and webcam has its own coordinate system. We use a cylindrical calibration bar to calculate the transformation matrix between these two coordinate systems. After that, the pico-projectors project nine fringe patterns with a phase shifting step of 2π/9 onto the surface of a mouse shaped phantom. The deformed fringe patterns are captured by the corresponding webcam respectively, and then are used to construct two phase maps that are converted to two 3D surfaces composed of scattered points. The two 3D point clouds are further merged into one with the transformation matrix. The surface extraction process takes less than 30 seconds. Finally, we apply the Digiwarp method to warp a standard Digimouse into the measured surface. The proposed method can reconstruct the surface of a mouse size object with an accuracy of 0.5 mm, which is sufficient to obtain a finite element mesh for FMT imaging. An FMT experiment of a mouse shaped phantom with one embedded fluorescence capillary target is performed. With the warped finite element mesh, we reconstruct the target successfully, which validates our surface extraction approach.

Optimization and performance evaluation of a conical mirror based fluorescence molecular tomography imaging system

“…1. 10 Two pairs of pico-projector and webcam were used to measure the object surface from two different views. After the surface was measured, the object was moved into the conical mirror by a linear stage for FMT scan.…”

“…The details of the calibration and node-to-pixel mapping of this system can be found in Ref. 10. And the reconstruction algorithms that were applied to this study can be found in Refs.…”