“4D” IMRT Delivery

Mageras, G.; Yorke, Ellen; Jiang, Steve B

doi:10.1007/3-540-30356-1_22

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…Just like bone alignment techniques, visualization of the real position of the target volume during breathing improves the overall quality of radiation therapy. Although the implementation of one or more respiratory gating techniques was initially motivated by the need to limit respiratory movements, these techniques now allow new modalities of irradiation, such as hypofractionated and intensity modulated radiotherapy [113][114][115][116]. Sophisticated and more or less dedicated apparatuses, such as Novalis TX (BrainLAB, Feldkirchen, Germany), Cyberknife (Accuray, Sunnyvale, USA), or dynamic arc therapy, combined with an appropriate respiratory gating technique are already operational.…”

Section: Discussionmentioning

confidence: 99%

Respiratory Gating for Radiotherapy: Main Technical Aspects and Clinical Benefits

Giraud

Houle

2013

ISRN Pulmonology

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Respiratory-gated radiotherapy offers a significant potential for improvement in the irradiation of tumor sites affected by respiratory motion such as lung, breast, and liver tumors. An increased conformality of irradiation fields leading to decreased complication rates of organs at risk is expected. Five main strategies are used to reduce respiratory motion effects: integration of respiratory movements into treatment planning, forced shallow breathing with abdominal compression, breath-hold techniques, respiratory gating techniques, and tracking techniques. Measurements of respiratory movements can be performed either in a representative sample of the general population, or directly on the patient before irradiation. Reduction of breathing motion can be achieved by using either abdominal compression, breath-hold techniques, or respiratory gating techniques. Abdominal compression can be used to reduce diaphragmatic excursions. Breath-hold can be achieved with active techniques, in which airflow of the patient is temporarily blocked by a valve, or passive techniques, in which the patient voluntarily breath-holds. Respiratory gating techniques use external devices to predict the phase of the breathing cycle while the patient breathes freely. Another approach is tumor-tracking technique, which consists of a real-time localization of a constantly moving tumor. This work describes these different strategies and gives an overview of the literature.

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Section: Discussionmentioning

confidence: 99%

Respiratory Gating for Radiotherapy: Main Technical Aspects and Clinical Benefits

Giraud

Houle

2013

ISRN Pulmonology

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“…Notably, the real-time position management (RPM) system (Varian Medical Systems, Inc., Palo Alto, CA, USA) has been extensively implemented and investigated clinically at a number of centers [2]. These external respiratory surrogates are particularly appealing because they 1) are noninvasive to the patient; 2) do not involve ionizing radiation.…”

Section: Introductionmentioning

confidence: 99%

A Kernel Method for Real-Time Respiratory Tumor Motion Estimation Using External Surrogates

Liu

2011

2011 10th International Conference on Machine Learning and Applications and Workshops

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Respiratory tumor motion is a major challenge in radiation therapy. Real-time tumor tracking aims to dynamically target the tumor with the radiation beam, allowing a reduction in the volume of healthy tissue exposed to a high dose. Fluoroscopic tracking with implanted fiducial markers is not widely accepted due to its invasiveness and, to a lesser extent, additional imaging dose. Using external respiratory surrogates is an attractive approach to localizing tumors affected by respiratory motion. In this paper, we present a kernel regression approach to the respiratory tumor motion problem. Due to the local nature of the kernel functions, it is inherently robust to outliers in the training data. In addition, both training and adapting to new data is highly efficient and almost free under the kernel regression model. We evaluated the method on respiratory motion data from six patients (three lung and three pancreas patients). It was found that the average 3D error is < 1 mm; the 95th percentile error is < 3 mm (1.2 mm on average). These errors are well below the peak-to-peak amplitude (10 mm on average) for all six patients and within clinically acceptable ranges. Overall, the method is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates in an accurate and efficient (~1 ms per prediction) way. These nice properties make it a suitable candidate for accurate and robust tumor tracking using external respiratory surrogates.

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“…An attractive approach to localizing tumors indirectly is through external respiratory surrogates, such as patient skin surface or tidal volume (Bert et al 2005, Jiang 2006b). For instance, the real-time position management system (Varian Medical Systems, Inc., Palo Alto, CA, USA) has been extensively implemented and widely adopted around the world (Mageras et al 2005). These external respiratory surrogates are particularly appealing because (1) they are noninvasive to the patient; (2) they do not involve ionizing radiation; (3) they are inexpensive and convenient to use.…”

Section: Introductionmentioning

confidence: 99%

Real-time tumor motion estimation using respiratory surrogate via memory-based learning

Lewis

Berbeco

et al. 2012

Phys. Med. Biol.

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Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc.) provide a noninvasive, non-ionizing, low-cost, and practical approach to obtain respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data is performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ~15 mm and average duration of ~115 seconds per trace. Given only 5-second (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ~50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few ms per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

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“4D” IMRT Delivery

Cited by 7 publications

References 69 publications

Respiratory Gating for Radiotherapy: Main Technical Aspects and Clinical Benefits

Respiratory Gating for Radiotherapy: Main Technical Aspects and Clinical Benefits

A Kernel Method for Real-Time Respiratory Tumor Motion Estimation Using External Surrogates

Real-time tumor motion estimation using respiratory surrogate via memory-based learning

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