Feasibility of real‐time lung tumor motion monitoring using intrafractional ultrasound and <scp>kV</scp> cone beam projection images

Mostafaei, Farshad; Tai, A.; Gore, Elizabeth; Johnstone, Candice; Haase, William R.; Ehlers, Christopher; Cooper, David T.; Lachaı̂ne, M.; Li, X. Allen

doi:10.1002/mp.13104

Cited by 10 publications

(11 citation statements)

References 21 publications

(39 reference statements)

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“…Moreover, the external surrogate signal can be any distinct oscillation signal related to the patient respiratory motion, such as external surface signals 36 or internal ultrasound tracking signals. 37 These can all be used to assist tumor phase recognition by applying the proposed algorithm TABLE II. The variance distribution (%) of the principal component (PC) for each patient.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, an OTS reflective marker was used as a respiratory oscillation indicator for continuous monitoring during the treatment. Moreover, the external surrogate signal can be any distinct oscillation signal related to the patient respiratory motion, such as external surface signals 36 or internal ultrasound tracking signals 37 . These can all be used to assist tumor phase recognition by applying the proposed algorithm to future clinical implementation.…”

Section: Discussionmentioning

confidence: 99%

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Tumor phase recognition using cone‐beam computed tomography projections and external surrogate information

et al. 2020

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Purpose: Directly extracting the respiratory phase pattern of the tumor using cone-beam computed tomography (CBCT) projections is challenging due to the poor tumor visibility caused by the obstruction of multiple anatomic structures on the beam's eye view. Predicting tumor phase information using external surrogate also has intrinsic difficulties as the phase patterns between surrogates and tumors are not necessary to be congruent. In this work, we developed an algorithm to accurately recover the primary oscillation components of tumor motion using the combined information from both CBCT projections and external surrogates. Methods: The algorithm involved two steps. First, a preliminary tumor phase pattern was acquired by applying local principal component analysis (LPCA) on the cropped Amsterdam Shroud (AS) images. In this step, only the cropped image of the tumor region was used to extract the tumor phase pattern in order to minimize the impact of pattern recognition from other anatomic structures. Second, by performing multivariate singular spectrum analysis (MSSA) on the combined information containing both external surrogate signal and the original waveform acquired in the first step, the primary component of the tumor phase oscillation was recovered. For the phantom study, a QUASAR respiratory motion phantom with a removable tumor-simulator insert was employed to acquire CBCT projection images. A comparison between LPCA only and our method was assessed by power spectrum analysis. Also, the motion pattern was simulated under the phase shift or various amplitude conditions to examine the robustness of our method. Finally, anatomic obstruction scenarios were simulated by attaching a heart model, PVC tubes, and RANDOâ phantom slabs to the phantom, respectively. Each scenario was tested with five real-patient breathing patterns to mimic real clinical situations. For the patient study, eight patients with various tumor locations were selected. The performance of our method was then evaluated by comparing the reference waveform with the extracted signal for overall phase discrepancy, expiration phase discrepancy, peak, and valley accuracy. Results: In tests of phase shifts and amplitude variations, the overall peak and valley accuracy was À0.009 AE 0.18 sec, and no time delay was found compared to the reference. In anatomical obstruction tests, the extracted signal had 1.6 AE 1.2 % expiration phase discrepancy, À0.12 AE 0.28 sec peak accuracy, and 0.01 AE 0.15 sec valley accuracy. For patient studies, the extracted signal using our method had À1.05 AE 3.0 % overall phase discrepancy, À1.55 AE 1.45% expiration phase discrepancy, 0.04 AE 0.13 sec peak accuracy, and À0.01 AE 0.15 sec valley accuracy, compared to the reference waveforms. Conclusions: An innovative method capable of accurately recognizing tumor phase information was developed. With the aid of extra information from the external surrogate, an improvement in prediction accuracy, as compared with traditional statistical methods, was obtained. It enables u...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tumor phase recognition using cone‐beam computed tomography projections and external surrogate information

et al. 2020

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“…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 ).…”

Section: Real-time Intrafraction Motion Monitoring Methodsmentioning

confidence: 99%

Real-time intrafraction motion monitoring in external beam radiotherapy

et al. 2019

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Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to ‘see what we treat, as we treat’ and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

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“…It requires precise co-registration of US and MR images in order to establish correspondence between tracked liver points. Indirect lung tumour tracking strategies based on 2D abdominal US have only been proposed recently [2,7]. Mostafaei et al [7] combine US imaging and cone-beam CT (CBCT) in order to reduce the CBCT imaging frequency and therefore the imaging dose to the patient.…”

Section: Introductionmentioning

confidence: 99%

“…Indirect lung tumour tracking strategies based on 2D abdominal US have only been proposed recently [2,7]. Mostafaei et al [7] combine US imaging and cone-beam CT (CBCT) in order to reduce the CBCT imaging frequency and therefore the imaging dose to the patient. However, the tumour motion is estimated in superior-inferior (SI) direction only.…”

Section: Introductionmentioning

confidence: 99%

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Giger

Jud

Nguyen

et al. 2019

Predictive Intelligence in Medicine

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Motion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for interfractional motion management in radiotherapy.

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Feasibility of real‐time lung tumor motion monitoring using intrafractional ultrasound and kV cone beam projection images

Abstract: The feasibility of real-time lung tumor motion tracking in SI direction with continuous ultrasound and periodic CBPI was demonstrated. The real-time estimation of the target position from the two streams for lung cancer patients would enable respiration gating or tracking during SBRT.

Cited by 10 publications

References 21 publications

Tumor phase recognition using cone‐beam computed tomography projections and external surrogate information

Tumor phase recognition using cone‐beam computed tomography projections and external surrogate information

Real-time intrafraction motion monitoring in external beam radiotherapy

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

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