Improving Performance and Precision in Robotic Surgery

The image of a robot performing surgery on a living patient seems like something out of a futuristic sci-fi movie, but the origins of robot-assisted surgery date back to the mid-1980s. While modern day advancements in electronics and robotic technology haven’t brought forth total autonomous robotic surgery, robot-assisted surgery (where a human surgeon controls and supervises) is one of the fastest growing disciplines within the clinical surgery and healthcare industry – with many subspecialties.

Benefits of Robot-Assisted Surgery

Using surgical robots improves patient outcomes by minimizing incision size and the invasiveness of procedures. As a result, patients experience wide array of benefits, including:

• Reduced complications
• Faster recovery periods
• Shorter hospital stays
• Reduced postoperative pain
• Lower risk of infections
• Lessen appearance of scarring

Performance Challenges

In robotic surgery, the robot and its actuators are operated under the control and supervision of a surgeon, but it is the robot that is interacting directly with the patient, thus placing considerable demands on the capabilities and certification of the robotic system to perform as with the same accuracy as a human surgeon.

The image processing system is critical to seeing the surgical area clearly and provides the surgeon and robot with situational awareness to manipulate the actuators and end effectors. The vision system therefore needs to be able to provide a high-resolution, low-latency, deterministic image processing path. The vision system must also address low light conditions, enhancing and correcting images as needed.

Dexterity and precision are also important. Using images provided by the vision system, the robot (under the control of the surgeon) will move its actuators and end effectors to perform the operations required. Such intricate procedures require precise and accurate motor control. This means the motor drive system must be smooth and have no jitters, but also exact positioning.

Regulatory Requirements

Along with performance and capability demands, there are significant certification requirements from regulatory authorities. Before the developed robot can be deployed and surgeons trained, the robot must be certified for use by a regulatory body such as the FDA. This requires the developers to demonstrate the safety and efficiency of the overall system, including demonstrating that any failure modes of the system cannot endanger the patient.

Additional Considerations

The list of challenges facing surgery robotic systems does not just stop with performance and regulatory requirements. Surgeon training time must be considered, as well as scalability of the system to perform a wider range of procedures overtime. And finally, size, weight and power reduction should be a constant goal.

Enter Programmable SoCs

The computational performance required to implement parallel operations across the image processing pipeline and precise motor control in a real-time system is challenging and a significant driver in the cost of the system. Often a multiple chip solution is required to achieve the performance targets, which further complicates the system and makes certification harder.

Many of the challenges can be addressed using heterogeneous SoCs (system-on-chips), such as the Xilinx Zynq-7000 SoC or Zynq UltraScale+ MPSoC (multi-processor system-on-chip) products.

These heterogeneous SoCs and MPSoCs combine high performance processors with programmable logic. This enables high level sequential algorithms to be implemented using the processors while the parallel nature of programmable logic can be used to implement the image processing pipeline and motor control.

Leveraging the parallel nature of programmable logic enables the implementation of a true image processing pipeline. As the algorithm is implemented in programmable logic there is no need to move image data on or off chip to DDR memory in between processing stages. This enables a more deterministic and lower latency image processing solution than a traditional CPU/ GPU solution.

As the image processing pipelines are implemented in programmable logic, it is possible to implement multiple pipelines in parallel to support multiple cameras if desired, further enhancing situational awareness for the robot and surgeon.

The programmable logic also enables multiple motor positioning and drive algorithms. Again, as these are implemented in programmable logic, multiple elements can be implemented in parallel, further increasing the responsivity and determinism of the system.

To aid certification, both Xilinx devices and tool chains have been designed with certification in mind for several end applications.

Use of a Xilinx heterogeneous SoC also enables for a more tightly integrated solution as the need for a multi-chip approach is reduced. This provides for a reduced power solution, as the number of devices and off chip transactions to and from DDR are reduced.

Summary

Robotic surgery has brought significantly improved outcomes for patients though less-invasive operations. Creating advanced surgical robots requires the ability to perform high-performance imaging and accurate motor control and positioning. Both demand high computational performance with a hard-real-time deadline. Using Xilinx heterogeneous SoCs and MPSoCs, such as the Zynq-7000 SoC and Zynq UltraScale+ MPSoC families, developers can overcome these challenges while creating a more integrated and power-efficient solution.