A Real-Time Gamma Probe Tracking System for Cancer Tissue Identification

Tracking and visualization of the sensing area for a tethered laparoscopic gamma probe

Our Hamlyn researchers proposed a real-time gamma probe tracking system integrated with augmented reality, aiming to aid surgery for prostate cancer.

One of the main treatment options for prostate cancer, the most common cancer in UK males, is carrying out surgery.

Minimally invasive surgery (MIS) including robot-assisted procedures are increasingly used owing to its significant advantages, such as reducing the risk of infection and trauma to the patient’s tissues.

However, due to the lack of reliable intra-operative visualisation, complete cancer resection and lymph node identification remain as challenges. In other words, making a clear distinction between cancerous and non-cancerous tissue is an arduous task for surgeons. Until today, many surgeons still rely on their naked eye and sense of touch to detect where the cancer is located in the tissue.

Recently, endoscopic radio-guided cancer resection has been introduced where a novel tethered laparoscopic gamma detector can be used to determine the location of tracer activity, which can complement preoperative nuclear imaging data and endoscopic imaging.

Nevertheless, these probes do not clearly indicate where on the tissue surface the activity originates, making the localising detection of the sensing area difficult, as well as increasing the mental workload of the surgeons.

A robust real-time gamma probe tracking system integrated with augmented reality

In light of this, our researchers at the Hamlyn Centre collaborated with Lightpoint Medical Ltd to propose a robust real-time gamma probe tracking system that integrated with augmented reality. The proposed system features a new hybrid marker which incorporated both circular dots and chessboard vertices to increase the detection rate.

a An example of a tethered probe being used in MIS; b the gamma probe marker; c example detected circular dots and chessboard vertices; d the local coordinates defined on the probe — (a) An example of a tethered probe being used in MIS; (b) the gamma probe marker; (c) example detected circular dots and chessboard vertices; (d) the local coordinates defined on the probe

A dual-pattern marker has been attached to the gamma probe, which combines chessboard vertices and circular dots for higher detection accuracy. The additional green stripe was included to introduce asymmetry and resolve direction ambiguity.

Both patterns are detected simultaneously based on blob detection and the pixel intensity based vertices detector and used to estimate the pose of the probe. In addition, temporal information is incorporated into the framework to reduce tracking failure.

Moreover, our researchers utilised the 3D point cloud generated from structure from motion to find the intersection between the probe axis and the tissue surface.

When presented as an augmented image, this can provide visual feedback to the surgeons. The proposed method has been validated with ground truth probe pose data generated using the OptiTrack system.

a Hardware setup for experiments; b the transformation matrixes between laparoscope, OptiTrack system, optical sensors and designed marker — (a) Hardware setup for experiments; (b) the transformation matrixes between laparoscope, OptiTrack system, optical sensors and designed marker

In conclusion, the experimental results showed that this dual-pattern marker could provide high detection rates, as well as more accurate pose estimation and a larger workspace than the previously proposed hybrid markers.

Furthermore, our researcher demonstrated the feasibility and the potentiality of using the proposed framework to track the ‘SENSEI®’ probe (a miniaturised cancer detection probe for MIS, developed by Lightpoint Medical Ltd).

In addition to the design of the new marker, our research team also proposed a solution to provide clear visual feedback for indicating the tracer location of the affected lymph nodes or tumour on the tissue surface by utilising augmented reality techniques.