Detection of Intrafractional Motion


Conformal radiation of moving tumors is a challenging task in radiotherapy. To enable compensation of intrafractional motion one needs to determine the target trajectory throughout the treatment. Currently most approaches try to estimate the position by recording external signals like changes of the circumference of the belly. Since there is no clear relationship between those external signals and the position of the tumor, we explore image guided approaches where the moving tissue is observed by fluoroscopic imaging during treatment. The tumor motion can be obtained in these planar fluoroscopic images during patient’s breathing. Since implanting fiducial markers may not be widely accepted, we prefer marker-less methods making use of registration techniques to estimate the tumor motion.

Gating and Tracking are strategies to compensate for detected intrafractional motion. With Gating the treatment beam is only switched on, when the tumor is inside a predefined region called "gating window". With Tracking the beam follows the movement of the tumor.


We have developed a Real-time Respiratory Gating System to detect variations of the  patient’s anatomy and feedback corrections to the treatment unit [1, 2]. Since continuous acquisition of fluoroscopic images during treatment will increase dose in healthy tissue a strategy to minimize this side effect is desirable. We developed a hybrid technique which combines the image-based method with an external signal.

The image-based approach of motion detection is using the variation in similarity between images recorded during respiration. Images in the same breathing phases have a higher similarity to each other than images in different breathing phases. The normalized Mutual Information (MI) value is calculated between a reference image and the other fluoroscopic images to quantify the occurred motion. In a feasibility study it was shown that the calculation of this image processing method can be performed fast enough and that its results are reproducible.



Ongoing research focuses on the development and evaluation of different tracking methods to estimate the current position of the tumor [3,4]. We prefer a marker-less method making use of registration techniques to estimate the tumor motion.

However, registration methods might fail when the tumor is not entirely visible throughout a breathing phase. The tumor might be occluded whilst moving, when it e.g. moves behind a rib and is therefore occluded in the fluoroscopic images.

We developed an enhanced registration technique to cope with the varying appearance of the tumor during breathing. The implemented approach uses several reference images to represent the tumor in several breathing phases. When calculating the tumor motion only that reference image which has the highest similarity value with the current image is selected in a pre-matching step. Then block matching is used to calculate the motion vector relative to the selected reference image. The algorithm is sufficiently fast and accurate to enable the implementation of adaptive strategies to compensate for respiratory motion of lung tumors.


Breathing Coaching

Breathing is a semi-periodic process and therefore tumor motions exhibit variations in baseline, frequency and fundamental pattern. With breathing coaching the respiratory reproducibility can be improved. The patient is encouraged to breath according to an individualized instruction curve. The instructions are provided by audio prompts or visual feedback via video goggles. An external breathing signal was used to monitor breathing motion. In a study the improvement in reproducibility and stability of the breathing course was evaluated when breathing coaching is provided [5]. The reproducibility and stability in terms of frequency of the breathing improve significantly from the coaching provided to the volunteers.



[1] Schwarz M, Nill S, Bendl R (2009) Combination of external/image-based detection of respiratory induced motion and adaption with a gating mechanism, in: W. S. O. Dössel (Ed.), IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Vol. 25/I, Springer Heidelberg, Munich, 616–619. 

[2] Moser T, Biederer J, Nill S, Remmert G, Bendl R (2008) Detection of respiratory motion in fluoroscopic images for adaptive radiotherapy, Phys Med Biol 53: 3129-3145

[3] Schwarz M, Stoiber EM, Bendl R (2011) Comparison of two Methods for Fluoroscopic Tracking of Tumor Mass. Radiotherapy and Oncology 99 (Suppl 1):451

[4] Schwarz M, Teske H, Streibl C, Bendl R (2011) Multiple template approach for fluoroscopic tracking of lung tumors, IGRT Salzburg 2011 (Workshop Session 2)

[5] Schwanke J; 2010 Design und Implementierung eines Atem-Coaching-Systems für die adaptive, atemgetriggerte Strahlentherapie. Diploma thesis, Heilbronn University / University of Heidelberg

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