Real-time intrafraction motion monitoring in external beam radiotherapy

Intracranial stereotactic radiosurgery (SRS) requires highly accurate treatment delivery. Infrared (IR)-based monitoring is a non-invasive alternative to fixed-pin systems where a coordinate frame is mechanically fixed to the patient's skull (Lightstone et al 2005). This has led to the commercialisation of a number of 6 degree of freedom (DoF) systems using passive IR reflectors either mounted on the couch, a bite block, a thermoplastic mask, or the body of the patient (Bova et al 1997, Lightstone et al 2005, Willoughby et al 2012). Stereoscopic in-room cameras are used to monitor the IR reflector position, acting as surrogate for the tumour position (Jin et al 2008, Willoughby et al 2012). In addition, systems such as the ExacTrac 6D (Brainlab) and real-time position management (RPM) (figure 1(a)) can be used for respiratory gating of extracranial sites. These positioning systems are connected to a 6 DoF couch and are capable of beam interruption and patient repositioning during treatment with sub-millimetre accuracy (mean and SD of error) (Willoughby et al 2012).

RPM geometric accuracy was verified against fiducial marker (FM) trajectories for lung, liver and pancreas patients (Li et al 2012) and for lung patients treated in deep-inspiration breath-hold (DIBH) with visual feedback (Scherman Rydhög et al 2017). For RPM-guided left-sided breast DIBH treatments using multiple reflectors, Fassi et al (2018) reported a median residual 3D set-up error of 5.8 mm compared with kilovoltage (kV) images of implanted clips.

To reduce the internal–external correlation uncertainty, IR-based monitoring is often used in conjunction with x-ray monitoring as described in section 2.3. In addition, on True Beam linacs (Varian), the respiratory gating system can be used in tandem with the kV on-board imaging system (OBI) where kV imaging is used to verify the internal target anatomy at the beginning of the gated treatment window determined by the RPM signal. If the internal anatomy has changed, the treatment can be interrupted and the patient repositioned based on newly acquired volumetric imaging (Vinogradskiy et al 2018).

Optical surface monitoring uses one or multiple high definition (HD) cameras to map the patient's surface. AlignRT (Vision RT) (figure 1(b)) uses three such room-mounted cameras while Catalyst (C-RAD, Uppsala, Sweden) uses two room-mounted cameras. These systems project structured light patterns on the patient such that 6 DoF motion can be estimated (Willoughby et al 2012). Visible light from in-room lighting, the reflectivity and colour of patients' clothing or skin tone can potentially affect the accuracy of surface mapping (Willoughby et al 2012). During treatment, the real-time detected patient surface can be compared with a reference surface, often obtained from the simulation CT. Typically one or more subsets of the surface can be selected as a region of interest (ROI) and are used to report the translation and rotation of the patient in real-time via registration to the reference surface. These systems can also replace skin tattoos for set-up and allow the use of less invasive fixation devices for SRS (Li et al 2011a, Pan et al 2012, Hoisak and Pawlicki 2018). Some integrated systems such as Align RT are able to automatically trigger beam-hold when the current surface does not match the reference surface. Re-positioning of the patient can be done in-room with immediate feedback from the system to guide the optimal match without the need for x-ray imaging.

Extracranially, surface guidance for intrafraction monitoring was mainly used for breast DIBH treatments (Tang et al 2014, Ma et al 2018). The main advantage of DIBH is the increased distance between the target volume and the heart resulting in lower dose to the heart and therefore lower rates of early toxicity (Zagar et al 2017). Using 3D surface mapping, Betgen et al (2013) evaluated the reproducibility of voluntary DIBH and found a systematic interfractional translation up to 5 mm.

The airflow in and out of the lungs can be monitored using a spirometer which, in turn, is used to estimate the air volume inside the lungs at a given time point. The patient breathes through a mouthpiece, less leakage-prone than a mask (Wong et al 1999) and wears a nose clip to ensure that all the breathing occurs through the mouth (Hoisak et al 2004). In addition to the monitoring, a scissor valve can be added and used to maintain the air volume at a chosen level, therefore enforcing a breath-hold. This is known as active breathing control (ABC) and was first described by Wong et al (1999). A version by Elekta, under the name active breathing coordinator (ABC) (figure 1(c)) uses a balloon valve which prevents air-flow when inflated. ABC has been used for liver (Eccles et al 2006), left breast (Remouchamps et al 2003) and lung (McNair et al 2009) cancer patients. The main limitations for the use of ABC is the need for patient compliance, coaching sessions and good communication between the radiographer and the patient.

A thermistor measuring the air temperature may also be used to determine if the patient is inhaling or exhaling (Kubo and Hill 1996).

Pressure systems detect respiratory motion via the varying pressure in a belt around the abdominal section of the patient. The Anzai belt (Anzai Medical, figure 1(d)) is part of a respiratory gating system (Siemens) where a pressure sensor (30 mm diameter, 9.5 mm thickness) is inserted in the belt and outputs a binary 5 V signal to the linac depending on the gating window parameters.

Most commonly, high contrast implanted FMs (figure 3) act as surrogate for the tumour position due to poor soft tissue contrast. FMs (figure 3(a)) are routinely implanted in the prostate for pre-treatment image guidance but may also be implanted percutaneously in the liver, pancreas and lungs or bronchoscopically in the peripheral lung (Shirato et al 2007) and in mediastinal lymph nodes (Schmidt et al 2016). Endoscopic implantation is possible into or near the digestive tract (Fukada et al 2013) while spinal and paraspinal lesion implantations are performed surgically (Shirato et al 2007). Endovascular coils have also been used as markers for lung tumours (Prévost et al 2008). Thinner markers that can take an irregular shape (figure 3(b)) may be preferred to regularly shaped markers to limit artefacts in reconstructed volumetric images or the risk of migration or implantation complication (Hanazawa et al 2017, Castellanos et al 2018). Liquid FMs such as Lipiodol (Guerbet, France) (Rose et al 2014) or BioXmark (figure 3(c)) allow for a personalized injected volume, reduced artefacts in reconstructed volume images and reduced dose perturbation for particle therapy at the cost of lower contrast in x-ray projection images.

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For any treatment guidance or adaptation based on intrafraction monitoring, markers must be segmented automatically in real-time, which is more difficult in MV images that have inherently lower contrast than kV images (Mao et al 2008, Lin et al 2013) and may have markers close to or outside the field edge (Fledelius et al 2014, Poulsen et al 2014, Hunt et al 2016). MV scatter onto the kV imager may also degrade the kV image quality (Luo et al 2008, Fledelius et al 2014) but can be efficiently reduced using triggered read-out to eliminate the accumulated MV scatter before each kV image acquisition (Poulsen et al 2015a).

Cylindrical or spherical markers can be segmented in real-time in kV or MV projections using simple parametric templates (Tang et al 2007, Mao et al 2008, Marchant et al 2012, Fledelius et al 2014). Arbitrarily shaped markers or marker groups require more complex templates that can be generated semi-automatically using breath-hold computed tomography (CT) scans (Regmi et al 2014) or fully automatically using pre-treatment cone-beam CT (CBCT) projections (Bertholet et al 2017, Campbell et al 2017b). The segmented marker position is typically selected as the one with the highest normalized cross-correlation coefficient between the 2D template and a pre-defined ROI of the projection. There will always be a maximum in the normalized cross-correlation hence causing segmentation error if the marker is outside of the ROI. A larger ROI increases the chances that the marker is inside the ROI, but the computation time increases linearly with the ROI area and the template area, and a larger ROI increases the risk of mistaking the marker for some other structure in the image (Fledelius et al 2014). Suitable ROIs result in a typical processing time below 10 ms per marker per image (Mao et al 2008, Fledelius et al 2014). Low cross-correlation coefficients also allow to detect potentially erroneous segmentation in template-based methods (Tang et al 2007, Fledelius et al 2014, Bertholet et al 2017) (table 2).

Template-free methods were also proposed using machine-learning with manually labelled data from the first treatment fraction as training dataset (Lin et al 2013) or using a dynamic programming (DP)-based method (Wan et al 2014, 2016). Due to the post-processing nature of the DP-based algorithm, it has not been used in real-time to date. However, owing to the fast processing time, a pre-treatment imaging data set could be acquired to initiate detection and intra-treatment images could be appended to the data set as they are acquired for real-time segmentation.

Table 2 summarizes the properties of selected methods. Note that accuracy results are not presented here. A fair comparison of segmentation algorithms is particularly difficult given the variety of image quality, marker type, treatment site, and ground truth data used for the evaluation.

FMs and their implantation represent an added cost and toxicity risk. Percutaneous implantation was linked to a risk similar to conventional percutaneous biopsy in lung, pancreas and liver (Kothary et al 2009) with pneumothorax as the most common complication. For trans-rectal implantation in the prostate, the main risk is urinary tract infection. However, it may be minimized by the use of thin markers requiring a small needle (Castellanos et al 2018). The use of markers also implies delays in the treatment due to the implantation itself but also often a waiting time between implantation and planning CT to let markers stabilize although a delay between implantation and planning CT was found to be unnecessary in liver patients (Worm et al 2016). Other limitations include marker migration and changes in the tumour position relative to the markers due to tissue deformations. Especially in the liver where markers are often implanted outside of the tumour to avoid tumour seeding during percutaneous implantation, an increased target-surrogate distance has been linked to a reduced targeting accuracy (Seppenwoolde et al 2011). For transbronchial implantation in the lungs, Ueki et al (2014) reported a residual intrafractional variation of the tumour position with respect to the markers of 1.5 mm in the SI direction. Shirato et al (2007) reported on the Hokkaido group experience in marker implantation in multiple sites with multiple techniques and reported successful implantation in 90 of 100 lesions without any serious complication. They observed that there is a learning curve among endoscopists regarding fixation rate for implantation in the bronchial tree and that the relationship between the markers and tumour can change significantly after two weeks. To avoid the risk, cost, and uncertainty related to the use of FMs, markerless monitoring in kV and MV images may be used for certain sites (sections 2.2 and 2.3).

Real-time x-ray imaging is limited to 2D localization information. Ideally, stereoscopic kV imaging is used to determine the target position via triangulation with high accuracy. However, this requires additional equipment.

2.2.2.1. The CyberKnife^® system

2.2.2.2. The RTRT system

2.2.2.3. The Vero system

2.2.2.4. Markerless stereoscopic monitoring

2.2.2.5. Combined kV/MV

2.2.3.1. KV monoscopic imaging

2.2.3.2. MV monoscopic imaging

As reported in the AAPM TG75 report, a substantial limitation of kV imaging-based motion monitoring is the added imaging dose to the patient, especially at the skin surface (Murphy et al 2007). A kV image from a standard linac delivers 1–3 mGy per image depending on the technique. A total added imaging dose of 2–10 mSv was estimated for KIM-guided prostate RT at 1 Hz and, for comparison, the dose typically delivered by one pelvis CBCT scan was 4.3 mSv (Ng et al 2013). On the RTRT system (Shirato et al 2004) the skin dose from one fluoroscope was estimated to 29–1182 mGy h⁻¹ and was highly dependent on kV peak and pulse duration but less so on skin-isocenter distance. Transient or main erythema can appear with an imaging dose of 2000 mGy or 6000 mGy respectively (Murphy et al 2007). Skin dose is therefore non-negligible for long IMRT treatments with the RTRT system. Depth dose at 5 cm was up to 58% of the peak dose and may also become a concern in IMRT treatments. Reduction of field size is an important but insufficient measure to reduce the dose, since the same area will receive the same skin dose every day. In a gantry mounted system, the source to detector distance is shorter than for the RTRT system which reduces exposure by a third compared to that of the RTRT system for a similar dose at the imager. The most direct way to reduce exposure remains reducing the imaging frequency as implemented in later generations of the RTRT system (Shirato et al 2004) or using hybrid monitoring.

In addition to the robotic linac and kV imaging system of the CyberKnife^® system (see section 2.2.2), Synchrony comprises a vest fitted with light emitting diodes (LED) markers and three ceiling-mounted cameras to monitor external motion at 20–40 Hz (Ozhasoglu et al 2008). Prior to treatment, at least eight x-ray pairs are acquired at different breathing phases (including end-inhale and end-exhale) and used to triangulate the fiducial maker positions (figure 4(a)). The external motion is continuously recorded, and an ECM is built that relates the internal FM motion to the external marker motion (see section 2.3.6). During treatment, the ECM is used to infer the marker positions and re-align the treatment beam. In addition, new x-ray pairs can be acquired about every 30 s to directly determine the FM positions by triangulation. The model can be updated on the fly in case of small errors or completely rebuilt using a new set of eight x-ray pairs after treatment interruption.

Hoogeman et al (2009) analysed the log files for the treatment of 44 lung cancer patients on the CyberKnife^® Synchrony system and calculated the correlation error as the difference between the estimated target positions and the actual target position in the intra-treatment images. They found a sub-millimetre population mean error (mean of the SDs) in each direction and no difference in correlation model error between centrally or peripherally located tumours.

Bibault et al (2014) reported on markerless lung tumour monitoring using the Synchrony system for 51 patients. The method is known as Xsight Lung Tracking System and allows to use the DRR method (see section 2.2.2) for lung tumours larger than 15 mm located in the apex and peripheral lung region and further than 15 mm away from major vessels and ribs. Another detectability criterion was that the projection of the tumour onto the spine must be at an angle different from 45°.

The ExacTrac system (figures 2(b) and 4(b)) combines an IR camera system with two floor-mounted kV sources and opposite ceiling-mounted detectors (Willoughby et al 2006a). Between five and seven external IR reflective markers are placed on the patient and detected by ceiling-mounted cameras (see section 2.1.1). An IR reflective star is placed on the couch and used for automatic couch adjustments. During treatment, when the external signal matches the reference gating level, an x-ray image pair is acquired and the 3D triangulated position of a FM is compared with its reference position. If there is a discrepancy larger than a set tolerance, the beam is switched off and the couch position is adjusted. Willoughby et al (2006a) and Verellen et al (2006) reported on the initial clinical experience with 11 and three lung cancer patients respectively. A 6D fusion option was later implemented to allow 6 DoF localization from the kV imaging system (Jin et al 2008).

In cranial SRS, reflective IR markers placed on a thermoplastic mask may be used for intrafraction monitoring (see section 2.1.1). However, the masks are slightly elastic and patients may still move within the mask. On the ExacTrac system, x-ray pairs can be acquired and 6 DoF position correction is obtained by 2D/3D image registration with planning DRRs. Radiograph pairs can be acquired for verification pre- and post-treatment (Gevaert et al 2012).

The Vero system described in section 2.2.2 includes an ExacTrac IR camera system. At the start of treatment, simultaneous stereoscopic kV imaging at 11 Hz and external IR monitoring at 50 Hz are performed in a 20–40 s training session to build an ECM (figure 4(c)). During treatment, the internal target position is determined from the ECM and stereoscopic images are acquired every 2 s. A ROI corresponding to a 3 mm tolerance radius around the predicted FM position is shown and the user can decide to terminate the session if the tolerance is systematically exceeded. Depuydt et al (2014) reported on the first ten liver and lung SBRT patients treated on the Vero RTTT system. The ECM building took an average of 2.7 min and was valid for an average (range) of 6.9 min (2.7–17.4 min). Significant cranial and posterior drift were observed for the IR and internal SI signal at the beginning of treatment suggesting that the drift was due to patient relaxation. Following a similar analysis for ten lung cancer patients, Akimoto et al (2013) recommended frequent model updates to avoid large baseline drift-related errors.

An RTRT system was installed at the Nippon Telegraph and Telephone corporation Hospital in Sapporo, Japan. However, this system only had two kV imagers which may have an obstructed view at certain gantry angles (Berbeco et al 2005b). The system was therefore supplemented by an external optical system (Anzai Medical) using a laser source and detector on an extendable arm placed on the treatment couch. Berbeco et al (2005b) investigated the residual motion for eight lung cancer patients treated with respiratory gating. Amplitude-based gating had slightly lower residual motion than phase-based gating for irregular breathing. Beam-to-beam and day-to-day variations were observed that warrant an adjustment of the gating window during the course of treatment, preferably based on online internal imaging.

Bertholet et al (2018) implemented hybrid monitoring on a standard linac using Combined Optical and Sparse Monoscopic Imaging with Kilovoltage x-rays (COSMIK, figure 4(d)). The method was developed as a hybrid alternative to KIM and therefore uses a similar monoscopic imaging technique and the RPM (Varian) as external monitoring device. COSMIK uses a pre-treatment CBCT both for patient set-up and as a training data set for ECM building. The FMs are automatically segmented in the CBCT projections (Bertholet et al 2017) and their 3D trajectories are estimated using the Gaussian PDF method (Poulsen et al 2008a). The 3D FM trajectories are used for automatic patient set-up (Worm et al 2012) and to fit an augmented linear ECM (Ruan et al 2008). During treatment, the internal FM positions are estimated from the continuous external signal using the ECM. kV images are acquired every 3 s, the FMs are segmented and their 3D positions are estimated. The ECM is updated based on the last three images for baseline drift between the internal and external signal. COSMIK can be used for non-coplanar fields without imaging, using the latest updated ECM. COSMIK was validated in phantom experiments and simulations and used on 14 liver SBRT patients treated with implanted FMs without motion mitigation. COSMIK was more recently combined with real-time 4D dose reconstruction (Ravkilde et al 2018, Skouboe et al 2019).

Hybrid methods with ECM updates are more accurate than monitoring based on respiratory signals alone (Malinowski et al 2013, Poels et al 2014, Bertholet et al 2018) but less accurate than continuous kV imaging and they cannot be used to monitor non-correlated internal motion such as seen in the prostate. Similar accuracy is achievable on specialized equipment and standard linacs because the accuracy is limited by the use of an ECM rather than by the way (stereoscopic or monoscopic kV imaging) that the ECM is being established (Cho et al 2010, Bertholet et al 2018). Despite the lower accuracy related to the use of an ECM, hybrid monitoring presents certain advantages over continuous kV imaging such as reduced imaging dose, shorter latency, continuous monitoring even during beam-off time, robustness to missing or erroneous marker segmentation and compatibility with non-coplanar treatment fields.

External/internal correlation and ECMs are therefore central to the use of hybrid methods. Several studies have investigated the correlation between breathing and target motion, the stability of that correlation, ECMs of different forms, and update strategies (McClelland et al 2013). The external motion is often ambiguously related to the internal motion due to hysteresis where the same external position results in different internal positions during inhale and exhale. Linear or quadratic models cannot model hysteresis but may be combined with state augmentation using a time delayed sample (Ruan et al 2008) or the first temporal derivative of the external position (Depuydt et al 2013).

On the CyberKnife^® system, the hysteresis is addressed by using two quadratic functions without state augmentation: one for the inhale phase and one for the exhale phase (Seppenwoolde et al 2007). However, if the external motion exceeds the value observed during model building, a linear function is used to avoid large errors due to quadratic extrapolation. During the training phase, linear as well as dual quadratic models are fitted in each direction of motion. The model with the smallest DoF-adjusted error is selected. As a result, the selected model may be linear in some directions of motion and quadratic in others. Because several external signals are used, the information from the different external markers can also be weighted using the Partial Least Square (PLS) method, thus eliminating latent variables that do not contribute to the accuracy of the model (Malinowski et al 2012). Malinowski et al (2013) also investigated the effect of model updates on targeting accuracy using two statistical metrics based on the external signal alone which resulted in a similar accuracy as updates based on estimation errors but required fewer updates.

Poels et al (2014) proposed a method for online model update on the Vero system where newly acquired data points are used to replace old training data points at the same breathing phase (determined by linear interpolation between exhale peaks). The accuracy improvement was significant albeit very small between the clinical and online update strategies, however, the treatment time can be reduced by about 5 min on average with the online update strategy compared to the clinical update which requires treatment interruption to rebuild the model.

Poels et al (2015) found similar performances for the CyberKnife^® dual quadratic (CKDQ), CyberKnife^® linear and the Vero ECM on a same dataset from 15 liver and lung patients but due to the complexity of the model, the latency of internal tumour motion estimation was 15 ms for the CKDQ compared to 2 ms for the Vero model.

Schnarr et al (2018) proposed to add a gantry-mounted kV imaging system perpendicular to the treatment beam on the tomotherapy system (Accuray Inc.) to allow hybrid motion monitoring using external optical monitoring combined with sequential monoscopic imaging.

Future software developments in hybrid motion monitoring include a 6D internal–external correlation (6D-IEC) framework using monoscopic kV-imaging in a similar workflow as COSMIK for 6 DoF hybrid monitoring (Nguyen et al 2018).

Going one step further, one may want to monitor the motion of the entire anatomical region including nearby OAR which may move differently from the target. Deformable motion models allow to estimate the respiratory motion of the local 3D anatomy from limited surrogate data that can be acquired during treatment (McClelland et al 2013). The surrogate data is often one or more external breathing signals (see section 2.1) and the model is similar to an ECM, but can estimate the full deformable motion of the local 3D anatomy. Methods have also been proposed that indirectly model the relationship between the internal motion and the surrogate data, enabling the use of real-time 2D imaging as surrogate data, such as kV-MV projection images (Vandemeulebroucke et al 2009) or 2D cine MR images (Stemkens et al 2016) (see section 2.5). Such models have been very popular in the research literature over the last 10–15 years (McClelland et al 2013, Thomas et al 2014, Stemkens et al 2016, Meschini et al 2017, Wolfelschneider et al 2017), but to date have seen very limited clinical use for two main reasons. Firstly, most methods require good quality 3D images which accurately depict the respiratory motion in order to build the motion models. The majority of methods proposed in the literature use 4DCT images for this purpose, however, 4DCT images only represent a single breath-cycle and so cannot be used to accurately model variability in the breathing motion. Furthermore, 4DCT images often contain sorting artefacts due to variable motion during acquisition which cause inaccuracies and uncertainties in the motion models. Recently, methods have been proposed that build the models from 4DMR datasets representing the 3D motion over several breath-cycles and including breath-to-breath variability (Stemkens et al 2016). One drawback is that such datasets can take a long time to acquire and process (Von Siebenthal et al 2007). Alternatively, methods have been proposed that fit the motion models directly to unsorted partial or raw imaging data, e.g. cine CT volumes, CT/MR slices (McClelland et al 2017), or CBCT projections (Martin et al 2014). Although promising, these methods still require further development and validation before they are suitable for clinical use. The second issue that has so far prevented the clinical adoption of deformable motion models is the lack of methods to verify and update the motion models during treatment. One of the key features of the hybrid methods is the ability to intermittently verify and update ECMs against new imaging data during treatment (section 2.3.6). However, this is more challenging for deformable motion models, since it is not possible to obtain intermittent measurements of the full 3D motion during treatment. Future research will need to focus on developing methods that use intrafractional imaging data (e.g. 2D MR) to verify and update the models and to be sufficiently confident in the accuracy of the motion estimates.

Electromagnetic systems provide continuous real-time 3D localization of implanted transponders or transmitters without the use of ionizing radiation. The most commonly used system is Calypso (Varian Medical Systems), where the transponders are electromagnetic resonance circuits in sealed glass capsules (Balter et al 2005). Typically, three transponders with different resonance frequencies (300–500 kHz) are implanted in or near the treatment target. An array of excitation coils in a panel above the patient excites one transponder at a time while a second array of receiver coils localizes the resonating transponder by triangulation. It gives the 3D position of the transponder centroid relative to the panel with a frequency of 10–25 Hz. The position relative to the accelerator isocenter is determined by three room-mounted cameras that track IR markers on the panel. Although the antenna panel causes changes in beam depth dose curves and beam attenuation, its dosimetric impact on clinical treatment plans was reported to be insignificant (Zou et al 2013).

Calypso was first used clinically in the prostate (Willoughby et al 2006b), where the ability of continuous monitoring without ionizing radiation has allowed systematic investigation of motion patterns (Kupelian et al 2007). Studies have revealed trends like strong cranial and anterior prostate motion correlation, increased likelihood of small to medium (>3–5 mm) prostate displacements with time (but not of large displacements (>7–10 mm))(Langen et al 2008, Su et al 2011), as well as larger respiration induced prostate motion in prone position compared to supine position (Shah et al 2011, Butler et al 2013). Other clinical sites include the prostate bed following prostatectomy (Zhu et al 2013), pancreas (Shinohara et al 2012), and liver (Poulsen et al 2015b, James et al 2016, Worm et al 2018). In lung tissue, the stability of the smooth transponder is a challenge (Shah et al 2013) and an anchored version of the transponder with better attachment in the bronchia by five nitinol legs has been developed (Booth et al 2016, Schmitt et al 2017).

Drawbacks of the Calypso system include the requirement of a dedicated non-conducting couch top, lack of flexibility to move the installation between treatment rooms, a limited transponder detection volume extending maximum 21 cm below the antenna panel, and MR artifacts caused by the transponders (Zhu et al 2009). With a diameter of 1.85 mm (14 gauge implantation needle) the first generation of Calypso transponders were considerably larger than typical FMs, but a thinner transponder for a 17 gauge needle is now available.

A similar system is RayPilot, which consists of an implantable wired radiofrequency transmitter that receives power through a wire from a couch top plate (Kindblom et al 2009, Vanhanen and Kapanen 2016). The couch top plate houses receiving antennas that detect the transmitter position and orientation at 30 Hz. The transmitter is implanted transperineally in the prostate with the wire passing through the perineum of the patient, and it is removed after treatment completion. Recent clinical studies found that the implantation and explantation procedures were feasible and safe, but the studies also reported interfractional transmitter position instabilities and recommended to combine real-time prostate motion monitoring by RayPilot with an independent IGRT system for daily prostate localization (Braide et al 2018, Vanhanen et al 2018). A newer version of the RayPilot, HypoCath, is catheter-based to remove the need for surgical intervention and allows to localize the urethra as well as the prostate.

Ultrasound (US) systems are capable of continuous image acquisition in real-time with good soft tissue contrast, while not exposing the patient to additional ionising radiation. This enables direct monitoring of internal tissue motion and deformation at high spatial and temporal resolutions. Clarity Autoscan^™ (Elekta) (figure 5(c)) is currently the only commercial US system designed for intrafraction motion monitoring. Approved specifically for prostate and prostate bed radiotherapy, the system incorporates a 3D transperineal US (TPUS) probe and is compatible with standard C-arm linacs. As such, Autoscan provides a flexible, cost-effective monitoring system that is unaffected by metal hip prostheses and does not require implanted FM. Integration with Elekta linacs enables motion mitigation via automated couch correction or gating, typically at an action threshold of 3 mm for 5 s, which can be varied if desired.

The US probe comprises a mechanically swept curvilinear transducer array with a 5 MHz centre frequency, which is secured to a baseplate to hold it in place during treatment. Sweeping the transducer array produces a continuously scanned 3D field of view. During monitoring, template matching based upon normalized cross correlation is used to automatically estimate the motion of a target reference volume within the imaging field of view (Lachaine and Falco 2013). The reference volume position is encoded in room coordinates by optically monitoring IR markers on the Autoscan probe using a room mounted stereoscopic camera (Polaris Spectra, NDI, Canada). Monitoring rates of ~0.5 Hz are employed for prostate motion monitoring.

Autoscan's accuracy was validated in vivo against manual localization of intraprostatic markers in EPID images (Grimwood et al 2018, Han et al 2018) and against RayPilot monitoring (Delcoudert et al 2017). Characterisations of prostate motion during treatment describe a gradual drift from the isocentre with substantial inter-patients variations showing maximum recorded shifts  >10 mm and a mean SI drift of 0.075 mm min⁻¹ (Ballhausen et al 2015, Li et al 2017).

As a soft tissue imaging modality, US is able to monitor a range of anatomical surrogates where the lesion cannot be discerned. This has motivated the use of experimental ultrasound systems to study a range of treatment sites beyond the prostate. The upper abdomen is of particular interest, because it is susceptible to respiratory motion and is largely accessible to US without obstruction from bony anatomy.

Liver motion monitoring using an adapted Vivid 7 Dimension probe (GE Healthcare, USA) was evaluated against Calypso in a free-breathing patient immediately after liver SBRT (Ipsen et al 2017). Another group has pioneered the use of an experimental version of Clarity to monitor the 3D position of the liver in 13 patients during RT delivered in breath hold (Boda-Heggemann et al 2016, Sihono et al 2017, Vogel et al 2018). A 3D US probe was held using a mechanical arm against the rib-cage throughout planning CT, CBCT and RT delivery, without interfering with treatment delivery (Boda-Heggemann et al 2016). The residual intra-breath-hold motion (e.g. drift) measured using US during CBCT acquisition was found to correlate well with residual motion measured from CBCT projection images (Vogel et al 2018).

US was also used for motion monitoring of the pancreatic head and surrogate structures, including the superior mesenteric artery and portal vein (Omari et al 2016) as well as for diaphragm position monitoring as a surrogate for lung tumour position (Mostafaei et al 2018).

US has been combined with MLC tracking in vitro (Fast et al 2016, Ipsen et al 2016) with a total system latency of ~1 s, therefore demonstrating adequate compensation for the slow motion typically observed in prostate cancer (Fast et al 2016, Colvill et al 2014). A predictive compensation method was demonstrated on sinusoidal target movements, reducing system latencies to 172 ms (Ipsen et al 2016). This technique illustrates a potential approach to compensate for monitoring latency of breathing motion in lung radiotherapy patients, but requires further in vivo evaluation.

Despite the scarcity of clinical free breathing patient studies, promising findings have also arisen from the MICCAI Challenge on Ultrasound Liver Tracking (CLUST), which comprises an open dataset of labelled anatomical features in 64 2D and 22 4D in vivo image sequences (De Luca et al 2018). Using results from CLUST, an estimation of the impact from monitoring on treatment margins was made, indicating a possible 75% reduction.

Optimal imaging requires careful probe placement to maximise patient-probe contact and to ensure adequate anatomical coverage in the field of view. Fargier-Voiron et al (2016) and Li et al (2017) have identified a need to control for anatomical deformation and changes to image quality associated with variations in probe pressure. Furthermore, at patient set up, the probe must be manually adjusted to ensure both reproducible positioning and adequate target volume coverage. Approaches to assist with probe-positioning are being investigated (Camps et al 2017, 2018). Remote probe support and robotic systems are also being developed to optimise probe placement during both patient set up and treatment delivery (Schlosser et al 2012, Sen et al 2017, Su et al 2017a). The implications of placing an ultrasound probe within the gantry arc require further consideration of the resulting beam attenuation. Monte Carlo probe models have been developed for incorporation with planning software (Bazalova-Carter et al 2015) and the integration of robotic ultrasound with the CyberKnife^® system has also been examined (Gerlach et al 2017). Another mitigation strategy has been pursued whereby a probe was manufactured using radiolucent materials to reduce interference with the treatment beam (Schlosser and Hristov 2016). Finally, an autonomous system for avoiding the treatment beam altogether has also been demonstrated (Schlosser et al 2016).