Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input

Jiang, Keyong; Fujii, Fumitake; Shiinoki, Takehiro

doi:10.1088/1361-6560/ab49ea

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…Module#2 solves the regression problem of estimating the 3D movement amount from the 2D MRI pixel values and, at the same time, constructs a network by LSTM to eliminate the deviation of the irradiation position due to the imaging time of cine-MRI and the device delay [ 11 , 12 ]. This made it possible to predict the respiratory cycle from the movement before and after the frame of interest.…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning

Tamura¹,

Demachi²,

Ishiguro³

et al. 2022

Cureus

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The purpose of this study is to propose algorithms and methods for achieving high accuracy in tracking and interception irradiation technology for tumors that move by respiration using MR-linac (MRIdian®, ViewRay Inc.) and to use deep learning to predict the movement of moving tumors in real time during radiation therapy and reconstruct cine magnetic resonance imaging (cine-MRI) into four-dimensional (4D) movies. MethodsIn this study, we propose a reconstruction algorithm using 4DCT for treatment planning taken before irradiation as training data in consideration of the actual treatment flow. In the algorithm, two neural networks made before treatment are used to reconstruct 4D movies that predict tumor movement in real time during treatment. Cycle GAN (generative adversarial network) was used to convert MR images to CT images, and long short-term memory was used to convert cine-MRI to 4D movies and predict tumor movement. ResultsWe succeeded in predicting the time including the imaging time of the MR images, the lag until irradiation, and the calculation time in the algorithm. In addition, the conversion and prediction results at each phase of reconstruction were generally good so that they could be clinically applied. ConclusionsThe reconstruction algorithm proposed in this study enables high-precision radiotherapy while predicting the volume information of the tumor and the actual tumor position, which could not be obtained during radiotherapy.

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Section: Methodsmentioning

confidence: 99%

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning

Tamura¹,

Demachi²,

Ishiguro³

et al. 2022

Cureus

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“…Several studies have used ML algorithms for predicting tumour motion based on past motion, 72–89 including in MRI‐guided radiotherapy 90–92 and ultrasound‐guided radiotherapy 93 . A comparison study of ML algorithms was made by Sharp et al.…”

Section: Prediction Of Tumour Motionmentioning

confidence: 99%

“…Several studies have used ML algorithms for predicting tumour motion based on past motion, [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89] including in MRI-guided radiotherapy [90][91][92] and ultrasound-guided radiotherapy. 93 A comparison study of ML algorithms was made by Sharp et al and showed that most ML algorithms have a lower localisation error compared to no prediction.…”

Section: Prediction Of Tumour Motionmentioning

confidence: 99%

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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“…Motion prediction frameworks have been implemented to address these combined latency issues when monitoring breathing motion by predicting near-future motion to guide gating or machine adjustments (Shirato et al 2000, McCall andJeraj 2007). Breathing motion prediction methods have been extensively studied, and several methods have been explored to address this problem, including linearized regression model, nonlinear estimation model, Kalman filtering and artificial neural networks (Isaksson et al 2005, Ruan and Keall 2010, Sun et al 2017, Jiang et al 2019, Li et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of stomach motions based upon gastric contraction and breathing models

Zhang¹,

Cao²,

Kashani³

et al. 2022

Phys. Med. Biol.

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Objective: Precision radiation therapy requires managing motions of organs at risk that occur during treatment. While methods have been developed for real-time respiratory motion tracking, non-breathing intra-fractional variations (including gastric contractile motion) have seen little attention to date. The purpose of this study is to develop a cyclic gastric contractile motion prediction model to support real-time management during radiotherapy. Approach: The observed short-term reproducibility of gastric contractile motion permitted development of a prediction model that 1) extracts gastric contraction motion phases from few minutes of golden angle stack of stars scanning (at patient positioning), 2) estimate gastric phase of real-time sampled data acquired during treatment delivery to these reconstructed phases and 3) predicting future gastric phase by linear extrapolation using estimation results from step 2 to account for processing and system latency times. Model was evaluated on three parameters including training time window for step 1, number of spokes for real-time sampling data in step 2 and future prediction time. Main Results: The model was tested on a population of 20-minute data samples from 25 scans from 15 patients. The mean prediction error with 10 spokes and 2 minutes training was 0.3±0.1 mm (0.1 – 0.7mm) with 5.1 s future time, slowly rising to 0.6±0.2 mm (0.2 – 1.1mm) for 6.8 s future time and then increasing rapidly for longer forward predictions, for an average 3.6±0.5 mm (2.8 – 4.7 mm) HD95 of gastric motion. Results showed that reducing of train time window (5 to 2min) doesn't influence the prediction performance, while using 5 spokes increased prediction errors. Significance: The proposed gastric motion prediction model has sufficiently accurate prediction performance to allow for sub-millimeter accuracy while allowing sufficient time for data processing and machine interaction and shows the potential for clinical implementation to support stomach motion tracking during radiotherapy.

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Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input

Cited by 8 publications

References 23 publications

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning

A review of artificial intelligence applications for motion tracking in radiotherapy

Real-time prediction of stomach motions based upon gastric contraction and breathing models

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