Mapping surgical fields by moving a laser-scanning multimodal scope attached to a robot arm

Abstract

Endoscopic visualization in brain tumor removal is challenging because tumor tissue is often visually indistinguishable from healthy tissue. Fluorescence imaging can improve tumor delineation, though this impairs reflectance-based visualization of gross anatomical features. To accurately navigate and resect tumors, we created an ultrathin/flexible, scanning fiber endoscope (SFE) that acquires reflectance and fluorescence wide-field images at high-resolution. Furthermore, our miniature imaging system is affixed to a robotic arm providing programmable motion of SFE, from which we generate multimodal surface maps of the surgical field. To test this system, synthetic phantoms of debulked tumor from brain are fabricated having spots of fluorescence representing residual tumor. Three-dimension (3D) surface maps of this surgical field are produced by moving the SFE over the phantom during concurrent reflectance and fluorescence imaging (30Hz video). SIFT-based feature matching between reflectance images is implemented to select a subset of key frames, which are reconstructed in 3D by bundle adjustment. The resultant reconstruction yields a multimodal 3D map of the tumor region that can improve visualization and robotic path planning. Efficiency of creating these 3D maps is important as they are generated multiple times during tumor margin clean-up. By using pre-programmed motions of the robot arm holding the SFE, the computer vision algorithms are optimized for efficiency by reducing search times. Preliminary results indicate that the time for creating these multimodal maps of the surgical field can be reduced to one third by using known trajectories of the surgical robot moving the image-guided tool.

Keywords: 3D surface mosaic; fluorescence-guided surgery; image-guided therapy; machine vision; medical robotics.

Figure 1.

The Scanning Fiber Endoscope (SFE) system. (a) The schematic diagram of SFE. A white optical beam consisting of RGB laser light is projected onto an object and collected by a ring of optical fibers that wrap around. Color video stream is generated with high resolution at 30 Hz frame rate. (b) The physical appearance of SFE. It is only 1.2 mm in diameter with only a 9mm rigid tip length.

Figure 2.

The experiment on micro-positioning stage. (a) A cup-shaped latex phantom to represent brain tissue after tumor debulking. Multiple fluorescence spots are randomly scattered on the surface to represent residual tumor. (b) The experiment setup on optics table. The micro-positioning stage would hold SFE to scan the tumor phantom with known camera position and orientation.

Figure. 3

Reflectance and multimodal (reflectance + fluorescence) imaging diagrams for the SFE using a green fluorescence biomarker. (a) Standard SFE RGB imaging. Each RGB laser source is filtered and amplified using a color-specific PMT channel; (b) SFE fluorescence imaging, with green and red laser sources inactive. Reflectance images are formed using blue laser illumination only, while fluorescence is detected within the green color channel.

Figure 4.

The reflectance and fluorescence SFE image of tumor phantom. (a) The standard reflectance image of SFE; (b) the fluorescence imaging by deactivating red and green laser source. The phantom segments with fluorol dye, which represent tumor residual, emit fluorescence signals (green) under blue laser activation; (c) Multimodal 3D image that is generated by merging color reflectance + fluorescence.

Figure 5.

Matching features in a pair of frames I and II are detected with/without feature position prediction and lined up with colorful lines. (a) Less matching pairs are found by the previous algorithm. (b) More matches are detected with the prediction of feature points.

Figure 6.

The 3D reconstruction of debulked tumor phantom with fluorescence spots. (a) Image sampling grid 5x6 of SFE above phantom with blue points as the features and small triangles as camera locations. (b) Reconstructed a map from the SFE reflectance images and the corresponding depth map with color scale. (c) Multimodal 3D reconstruction from the 30 SFE locations above the phantom showing residual tumor.

Figure 7.

The experiment with RAVEN system. (a) A cup-shaped latex phantom to represent brain tissue after tumor debulking. (b) The experiment setup on optics table. The RAVEN arm would hold SFE to scan the tumor phantom with known programmable motion.

Figure 8.

The 3D reconstruction of debulked tumor cavity phantom with dense vessel features and no residual fluorescence tumor. (a) Reconstructed 3D surface and depth map based on known camera positions and orientations; (b) Reconstructed 3D surface and depth map solely based on features.