Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy

Motion analysis comparing surface imaging and diaphragm tracking on kV projections for deep inspiration breath hold (DIBH)

BACKGROUND

Surface-guided imaging (SGI) is increasingly utilized to monitor patient motion during deep inspiration breath hold (DIBH) in radiotherapy. Understanding the association between surface and internal motion is crucial for effective monitoring.

PURPOSE

To investigate the relation between motion detected by SGI using surface-guided radiotherapy (SGRT) and internal motion measured through diaphragm tracking on kV projections acquired with DIBH for online CBCT.

METHODS

Both SGI and kV were simultaneously acquired for ten patients over a total of 200 breath holds (BH). Diaphragm tracking was performed using second-degree polynomial curve fitting on the derivative images for each kV projection and high-pass filtering at 1/30 Hz to remove rotational effects. The superior-inferior (SI) and anterior-posterior (AP) motions of SGI were then compared to kV tracking using various statistical measures.

RESULTS

The correlation (individuals' median: -0.07 to 0.73) was a suboptimal metric for the BH data. The median and 95th percentile absolute differences between SGI-SI and kV were 0.73 mm and 3.46 mm, respectively, during DIBH. For SGI-AP, the corresponding values were 0.55 mm and 2.80 mm. For inter-BH measurements, the contingency table based on a 3 mm threshold indicated surface/diaphragm motion agreement for SGI-SI/kV and SGI-AP/kV was 61 % and 56 %, respectively.

CONCLUSION

Both intra- and inter-BH measurements indicated a limited association between surface and diaphragm motion, with certain constraints noted due to kV tracking and DIBH data. These findings warrant further investigation into the association between surface and internal motion.

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purpose

Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.

Material and methods

Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm.

Results

Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms.

Conclusion

It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

Advances and potential of optical surface imaging in radiotherapy

This article reviews the recent advancements and future potential of optical surface imaging (OSI) in clinical applications as a four-dimensional (4D) imaging modality for surface-guided radiotherapy (SGRT), including OSI systems, clinical SGRT applications, and OSI-based clinical research. The OSI is a non-ionizing radiation imaging modality, offering real-time 3D surface imaging with a large field of view (FOV), suitable for in-room interactive patient setup, and real-time motion monitoring at any couch rotation during radiotherapy. So far, most clinical SGRT applications have focused on treating superficial breast cancer or deep-seated brain cancer in rigid anatomy, because the skin surface can serve as tumor surrogates in these two clinical scenarios, and the procedures for breast treatments in free-breathing (FB) or at deep-inspiration breath-hold (DIBH), and for cranial stereotactic radiosurgery (SRS) and radiotherapy (SRT) are well developed. When using the skin surface as a body-position surrogate, SGRT promises to replace the traditional tattoo/laser-based setup. However, this requires new SGRT procedures for all anatomical sites and new workflows from treatment simulation to delivery. SGRT studies in other anatomical sites have shown slightly higher accuracy and better performance than a tattoo/laser-based setup. In addition, radiographical image-guided radiotherapy (IGRT) is still necessary, especially for stereotactic body radiotherapy (SBRT). To go beyond the external body surface and infer an internal tumor motion, recent studies have shown the clinical potential of OSI-based spirometry to measure dynamic tidal volume as a tumor motion surrogate, and Cherenkov surface imaging to guide and assess treatment delivery. As OSI provides complete datasets of body position, deformation, and motion, it offers an opportunity to replace fiducial-based optical tracking systems. After all, SGRT has great potential for further clinical applications. In this review, OSI technology, applications, and potential are discussed since its first introduction to radiotherapy in 2005, including technical characterization, different commercial systems, and major clinical applications, including conventional SGRT on top of tattoo/laser-based alignment and new SGRT techniques attempting to replace tattoo/laser-based setup. The clinical research for OSI-based tumor tracking is reviewed, including OSI-based spirometry and OSI-guided tumor tracking models. Ongoing clinical research has created more SGRT opportunities for clinical applications beyond the current scope.