Motion-adaptive radiotherapy - what to do when the tumor won’t sit still Martin J Murphy PhD Department of Radiation Oncology Virginia Commonwealth University

External-beam radiotherapy

The nagging problem of external-beam alignment Except in frame-based radiosurgery, there is no rigid connection between the treatment site and the radiation delivery mechanism Typically, the treatment beams are aligned to setup landmarks once, at the beginning of irradiation, but The patient is not a rigid body The patient is alive and breathing (one would hope) This leads to Shape changes in the anatomy and movement of the target during irradiation Resulting in a time-varying mismatch between the planned dose distribution and the actual anatomy

Solutions to the alignment problem

The dark ages: irradiate everything

The middle ages: Nail the patient to the treatment table, close your eyes, and treat

The modern age: Image-guided radiotherapy Use image-based patient alignment at the beginning of each fraction Locate the treatment site in the images Align the beam directly to the treatment site But, there can still be movement after alignment

The paradigm for movement is respiration

Respiratory motion Lung tumors can move 3 - 30 mm during respiration The motion can be spatially and temporally complex and irregular Breath-holding can be difficult for some patients

The most widespread motion adaption method is beam gating A basic method - turn the beam on and off as the target moves past

The post-modern age: Real-time motion-adaptive radiotherapy - follow the moving tumor with the beam One can move a collimating aperture synchronously with the moving tumor

Or, one can move the entire linear accelerator synchronously with the tumor

Components of a real-time tumor tracking system A target localization system to continuously measure the tumor position A dynamic re-alignment system to shift the beam with respect to the patient, or vice-versa A real-time control loop to translate tumor position into beam/couch coordinates and synchronously reposition the beam/patient while correcting for delays in the detection/realignment process

An example of a real-time tracking system

Target localization

Direct tumor tracking Sometimes you get lucky and can clearly see the tumor outline in a 2D radiograph

Internal fiducial tracking Implant radio-opaque markers in or near the tumor before CT Track the fiducials fluoroscopically

The Calypso Electromagnetic localization system

Calypso system for electromagnetic tracking Small electromagnetic coils (transponders) are implanted at the treatment site

Calypso electromagnetic tracking An antenna system with transmitters and receivers is positioned over the patient The antenna system alternately excites and detects resonant EM radiation from each transponder

Surrogate tracking Make a fluoroscopic video or 4D CT of the patient Correlate the tumor motion with the external marker motion During treatment, use the external marker motion to predict the tumor position

Tumor-chest correlation

Tumor-chest correlation coefficient Good Bad

Dynamic realignment systems

Dynamic beam shift - CyberKnife Synchrony X-ray imaging samples the tumor position surface markers correlate with the tumor position Optical tracking of the surface provides continuous tumor position estimates Real-time control loop synchronizes beam pointing with inferred breathing motion of the tumor

Dynamic beam shift - multileaf collimator (video courtesy of Paul Keall, Stanford University)

Control loop for synchronization

Control loop for synchronization

Predictive control loop for system latency There will be a delay between the time that the tumor position is measured and the time that the beam re-alignment is completed The delay (latency) can be 50 - 500 ms Therefore the control loop must “lead” the target in order to keep the beam aligned This requires a real-time predictive algorithm

Requirements of a temporal prediction control loop Accurately predict breathing amplitude up to 500 ms in advance Accommodate a wide variety of breathing patterns, some of which might be highly irregular Require little or no customization to individual patients Continually adjust to breathing that changes with time Avoid losing track of the breathing pattern during irregular transients Recover optimal accuracy rapidly following disruptions

Methods of prediction Make a biomechanical model of the breathing process Make a mathematical model of the breathing cycle using periodic functions Make a heuristic model of the breathing cycle that mimics each patient’s observed breathing pattern

Adaptive filters Adaptive filters are heuristic algorithms that mimic the incoming signal by taking sequential samples of the signal amplitude and combining them in a weighted sum to estimate the present or future amplitude The filters make no assumptions about the functional form of the signal or the physical mechanism producing it The filters can continually adjust their weighting parameters to adapt to changes in the signal shape

A basic linear adaptive filter

From linear filter to neural network In a linear filter the output signal estimate is just the weighted sum of the input samples In a nonlinear filter two or more weighted sums of the input samples are combined in a non-linear function to produce the output estimate A nonlinear filter can recognize much more complex patterns in the incoming signal than a linear filter

A nonlinear neural network

Often, breathing is regular and predictable

But sometimes it looks like this

Summary Real-time adaptation to respiratory motion is in practice at some clinics Electromagnetic transponders have been clinically demonstrated to provide accurate real-time position data without x-rays EM transponders have not yet been combined with real-time beam synchronization - this is a work in progress Without EM transponders one must either use fluoroscopy to observe the tumor or predict the tumor position from external surrogates All real-time systems have latencies that must be accommodated by a predictive control loop Breathing prediction is actually rather difficult and is a very active area of research for real-time respiratory tracking for radiation therapy