Multimodal image fusion in ultrasound-based neuronavigation: improving overview and interpretation by integrating preoperative MRI with intraoperative 3D ultrasound.

OBJECTIVE: We have investigated alternative ways to integrate intraoperative 3D ultrasound images and preoperative MR images in the same 3D scene for visualizing brain shift and improving overview and interpretation in ultrasound-based neuronavigation. MATERIALS AND METHODS: A Multi-Modal Volume Visualizer (MMVV) was developed that can read data exported from the SonoWand neuronavigation system and reconstruct the spatial relationship between the volumes available at any given time during an operation, thus enabling the exploration of new ways to fuse pre- and intraoperative data for planning, guidance and therapy control. In addition, the mismatch between MRI volumes registered to the patient and intraoperative ultrasound acquired from the dura was qualified. RESULTS: The results show that image fusion of intraoperative ultrasound images in combination with preoperative MRI will make perception of available information easier by providing updated (real-time) image information and an extended overview of the operating field during surgery. This approach will assess the degree of anatomical changes during surgery and give the surgeon an understanding of how identical structures are imaged using the different imaging modalities. The present study showed that in 50% of the cases there were indications of brain shift even before the surgical procedure had started. CONCLUSIONS: We believe that image fusion between intraoperative 3D ultrasound and preoperative MRI might improve the quality of the surgical procedure and hence also improve the patient outcome.

Can robots without Hebbian plasticity make good models of adaptive behaviour?

No. Animals' primary problem is the shaping of movements, guided by and adapting to sensory signals. This requires a narrower class of biorobotic models than that spanned by Webb's dimensions and examples. We claim that all model variables and mechanisms must have real counterparts, input vectors must model known sensor fields, internal state vectors and transformations must model neurophysiological processes, and output vectors must model coordinated muscle signals.