Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis

Hirai, Ryusuke; Sakata, Yoshihito; Tanizawa, Akiyuki; Mori, Shinichiro

doi:10.1016/j.ejmp.2019.02.006

Cited by 33 publications

(51 citation statements)

References 19 publications

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“…To date, several studies have demonstrated the potential to eliminate the use of markers altogether through markerless tracking 35–37,50–55,57,59–64 . The implementation of markerless tracking is considered the ideal technique for the patient and the healthcare system due to its non‐invasiveness.…”

Section: Discussionmentioning

confidence: 99%

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A review of artificial intelligence applications for motion tracking in radiotherapy

2021

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During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real‐time image‐guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)‐based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker‐based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker‐based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

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Section: Discussionmentioning

confidence: 99%

“…A DL method developed by Hirai et al 53 . successfully estimated lung and liver tumour positions for markerless tracking.…”

Section: Markerless‐based Approachesmentioning

confidence: 99%

A review of artificial intelligence applications for motion tracking in radiotherapy

2021

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“…In recent years, image processing techniques to detect tumor itself in kilovoltage [ 20 , 21 ] or megavoltage [ 22 , 23 ] images without the fiducial markers have been reported. It has also been reported that an anatomical feature such as the diaphragm could be used instead of the metal markers [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

Ukon

Arai

Sugiyama

et al. 2021

Journal of Radiation Research

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The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

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“…The primary goal is to improve motion tracking performance by developing estimation methods. To this end, Taken's theorem (Ortmaier et al, 2005), artificial neural network (Cheng and Tavakoli, 2019b;Hirai et al, 2019), extended Kalman filter (EKF) (Liang et al, 2014), receding horizon model predictive controller (Bebek and Cavusoglu, 2007), and recursive least squares-based adaptive filter (Tuna et al, 2014) have been investigated in the developments of prediction-based controllers.…”

Section: Position Controlmentioning

confidence: 99%

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

Cheng

Tavakoli

2020

Front. Robot. AI

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The coronavirus disease 2019 (COVID-19) pandemic has resulted in public health interventions such as physical distancing restrictions to limit the spread and transmission of the novel coronavirus, causing significant effects on the delivery of physical healthcare procedures worldwide. The unprecedented pandemic spurs strong demand for intelligent robotic systems in healthcare. In particular, medical telerobotic systems can play a positive role in the provision of telemedicine to both COVID-19 and non-COVID-19 patients. Different from typical studies on medical teleoperation that consider problems such as time delay and information loss in long-distance communication, this survey addresses the consequences of physiological organ motion when using teleoperation systems to create physical distancing between clinicians and patients in the COVID-19 era. We focus on the control-theoretic approaches that have been developed to address inherent robot control issues associated with organ motion. The state-of-the-art telerobotic systems and their applications in COVID-19 healthcare delivery are reviewed, and possible future directions are outlined.

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Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis

Cited by 33 publications

References 19 publications

A review of artificial intelligence applications for motion tracking in radiotherapy

A review of artificial intelligence applications for motion tracking in radiotherapy

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

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