• Matlab
  • Simulink
  • NS3
  • OMNET++
  • NS2

Registration for Operative Nuclear Imaging in Surgery

3D functional nuclear imaging modalities like SPECT or PET provide valuable information, as small structures can be marked with radioactive tracers to be localized before surgery. This positional information is valuable during surgery as well, for example when locating potentially cancerous lymph nodes in the case of breast cancer. However, the volumetric information provided by pre-operative SPECT scans loses validity quickly due to posture changes and manipulation of the soft tissue during surgery. During the intervention, the surgeon has to rely on the acoustic feedback provided by handheld gamma-detectors in order to localize the marked structures. In this paper, we present a method that allows updating the pre-operative image with a very limited number of tracked readings.

A previously acquired 3D functional volume serves as prior knowledge and a limited number of new 1D detector readings is used in order to update the prior knowledge. This update is performed by a 1D-3D registration algorithm that registers the volume to the detector readings. This enables the rapid update of the visual guidance provided to the surgeon during a radio-guided surgery without slowing down the surgical workflow. We evaluate the performance of this approach using Monte-Carlo simulations, phantom experiments and patient data, resulting in a positional error of less than 8 mm which is acceptable for surgery. The 1D-3D registration is also compared to a volumetric reconstruction using the tracked detector measurements without taking prior information into account, and achieves a comparable accuracy with significantly less measurements.

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