A finite state model for respiratory motion analysis in image guided radiation therapy

Wu, Huanmei; Sharp, G; Salzberg, Betty; Kaeli, David; Shirato, Hiroki; Jiang, Steve B

doi:10.1088/0031-9155/49/23/012

Cited by 72 publications

(66 citation statements)

References 14 publications

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“…Notably, the first few cycles of the VBF waveform that are used for the calibration had large breathing amplitudes that were not consistent with many of the other breathing cycles. In addition, both waveforms contain noise that is consistent with cardiac motion, (23) which may have adversely impacted the performance of prospective triggering. Figure 2 highlights a case study where improvements in image quality were observed when VBF was used.…”

Section: Resultsmentioning

confidence: 99%

Impact of incorporating visual biofeedback in 4D MRI

Kim

Price

et al. 2016

J Applied Clin Med Phys

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Precise radiation therapy (RT) for abdominal lesions is complicated by respiratory motion and suboptimal soft tissue contrast in 4D CT. 4D MRI offers improved contrast although long scan times and irregular breathing patterns can be limiting. To address this, visual biofeedback (VBF) was introduced into 4D MRI. Ten volunteers were consented to an IRB‐approved protocol. Prospective respiratory‐triggered, T2‐weighted, coronal 4D MRIs were acquired on an open 1.0T MR‐SIM. VBF was integrated using an MR‐compatible interactive breath‐hold control system. Subjects visually monitored their breathing patterns to stay within predetermined tolerances. 4D MRIs were acquired with and without VBF for 2‐ and 8‐phase acquisitions. Normalized respiratory waveforms were evaluated for scan time, duty cycle (programmed/acquisition time), breathing period, and breathing regularity (end‐inhale coefficient of variation, EI‐COV). Three reviewers performed image quality assessment to compare artifacts with and without VBF. Respiration‐induced liver motion was calculated via centroid difference analysis of end‐exhale (EE) and EI liver contours. Incorporating VBF reduced 2‐phase acquisition time (4.7±1.0 and 5.4±1.5trueprefixmin with and without VBF, respectively) while reducing EI‐COV by 43.8%±16.6%. For 8‐phase acquisitions, VBF reduced acquisition time by 1.9±1.6trueprefixmin and EI‐COVs by 38.8%±25.7% despite breathing rate remaining similar (11.1±3.8 breaths/min with vs. 10.5±2.9 without). Using VBF yielded higher duty cycles than unguided free breathing (34.4%±5.8% vs. 28.1%±6.6%, respectively). Image grading showed that out of 40 paired evaluations, 20 cases had equivalent and 17 had improved image quality scores with VBF, particularly for mid‐exhale and EI. Increased liver excursion was observed with VBF, where superior–inferior, anterior–posterior, and left–right EE‐EI displacements were 14.1±5.8, 4.9±2.1, and 1.5±1.0 mm, respectively, with VBF compared to 11.9±4.5, 3.7±2.1, and 1.2±1.4 mm without. Incorporating VBF into 4D MRI substantially reduced acquisition time, breathing irregularity, and image artifacts. However, differences in excursion were observed, thus implementation will be required throughout the RT workflow.PACS number(s): 87.55.‐x, 87.61.‐c, 87.19.xj

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Section: Resultsmentioning

confidence: 99%

Impact of incorporating visual biofeedback in 4D MRI

Kim

Price

et al. 2016

J Applied Clin Med Phys

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“…Several models have been proposed to mimic tumour motions, see [6,9] and references therein. This paper proposes two stochastic linear models and a hybrid combination of them, which are suitable to use in both the Kalman filter and the IMM filter.…”

Section: Models and Filter Algortihmsmentioning

confidence: 99%

“…Since respiratory tumour trajectories may exhibit irregular motions as shown in [9], a single CV model or a single CA model may not be able to capture the dynamics in the complete trajectory. For this reason, a hybrid combination of the two models is proposed.…”

Section: Models and Filter Algortihmsmentioning

confidence: 99%

Prediction of tumour motion using interacting multiple model filter

Putra

Haas

Mills³

et al. 2006

IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing

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Accurate prediction of tumour motion -over a prescribed time -is essential for enabling adaptive radiotherapy. The prediction time horizon is determined by measurement processing time, predictor algorithm processing time and the time-to-adapt radiation delivery. A trade off between the predictor algorithm complexity and the required prediction time horizon, therefore, has to be made. This paper proposes an interacting multiple model (IMM) filter and two Kalman filters to predict 0.2 s ahead respiratory tumour motions. The performance of the filters is evaluated using 333 traces of 4 minutes respiratory motions for 24 adult patients. The average RMSE of the IMM filter and the best Kalman filter with 5Hz measurements rate are 0.98 mm and 1.1 mm, which are improvements of 38% and 30% compared to use of measurements only.

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“…Our previous investigation developed a method of dynamic respiratory gating to compensate EOE baseline-shift 38 based on the finite state model (FSM) by Wu 39 which can identify the EOE phase of the tracked motion in real-time during treatment delivery. In this technical note, we will simulate an application of that method to evaluate the feasibility of using a dMLC to make the adjustments necessary for dynamic respiratory gating in a real-time tracking scenario with free breathing.…”

Section: Introductionmentioning

confidence: 99%

Use of dMLC for implementation of dynamic respiratory‐gated radiation therapy

Pepin

Shirato

2013

Medical Physics

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Purpose:To simulate and evaluate the use of dynamic multileaf collimators (dMLC) in respiratory gating to compensate for baseline drift. Methods: Tumor motion tracking data from 30 lung tumors over 322 treatment fractions was analyzed with the finite state model. A dynamic respiratory gating window was established in real-time by determining the average positions during the previous two end-of-expiration breathing phases and centering the dMLC aperture on a weighted average of these positions. A simulated dMLC with physical motion constraints was used in dynamic gating treatment simulations. Fluence maps were created to provide a statistical description of radiation delivery for each fraction. Duty cycle was also calculated for each fraction. Results: The average duty cycle was 2.3% greater under dynamic gating conditions. Dynamic gating also showed higher fluences and less tumor obstruction. Additionally, dynamic gating required fewer beam toggles and each delivery period was longer on average than with static gating. Conclusions: The use of dynamic gating showed better performance than static gating and the physical constraints of a dMLC were shown to not be an impediment to dynamic gating.

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A finite state model for respiratory motion analysis in image guided radiation therapy

Cited by 72 publications

References 14 publications

Impact of incorporating visual biofeedback in 4D MRI

Impact of incorporating visual biofeedback in 4D MRI

Prediction of tumour motion using interacting multiple model filter

Use of dMLC for implementation of dynamic respiratory‐gated radiation therapy

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