Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Uesaka, Mitsuru; Nakagawa, Keiichi; Haga, Akihiro

doi:10.1093/jrr/rry085

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…However, 4D‐CBCT faced obstacles immediately from its onset, such as poor image quality and low contrast due to undersampling streaking artifacts, remediated with long acquisition times and high radiation doses to fulfill the required projection sampling. In order to address these challenges, investigators proposed different solutions such as using prior information, compressed sensing, motion modeling, deformable registration, and a large variety of other software reconstruction techniques to alleviate the lack of sufficient projection data within each phase bin 235–285 ; optimizing the gantry acquisition protocol specific to the patient respiration and/or fiducial markers to adequately sample the patient respiration while minimizing both scan time and dose 286–300 ; and even developing 4D digital tomosynthesis to get time‐resolved motion data in the most efficient manner, albeit at the expense of full volumetric information 301–305 …”

Section: Daily Image Guidance Of Lung Treatmentsmentioning

confidence: 99%

A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

Vergalasova

Cai

2020

Medical Physics

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Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

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Section: Daily Image Guidance Of Lung Treatmentsmentioning

confidence: 99%

A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

Vergalasova

Cai

2020

Medical Physics

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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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“…However, the invasive implantation remains a daunting barrier for its wide acceptance in the clinic. A markerless approach is highly desirable and has been investigated intensively . With highly customized image pre‐processing and enhancement maneuvers, localization through registration based on cross‐correlation is successful when image contrast between tumor and its background is reasonable, but often fails at angles where tumor is obscured by mediastinum or ribs .…”

Section: Introductionmentioning

confidence: 99%

“…A markerless approach is highly desirable and has been investigated intensively. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] With highly customized image pre-processing and enhancement maneuvers, localization through registration based on cross-correlation is successful when image contrast between tumor and its background is reasonable, but often fails at angles where tumor is obscured by mediastinum or ribs. 18 A potential solution is a markerless tracking scenario, which explores the large body of data collected in the marker-based setting, builds a framework to extract underlining non-marker features inside the tumor among a population of lung patients, and uses this information as a prior to help the detection and tracking of tumor in subsequent patients.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

et al. 2020

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Purpose To design a convolutional recurrent neural network (CRNN) that calculates three‐dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D‐CBCT images. Method Under an IRB‐approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free‐breathing. Concurrently, an electromagnetic signal‐guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross‐correlation‐based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave‐one‐out strategy using data from 11 lung patients, including 5500 kV images. The root‐mean‐square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. Result Three‐dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high‐performance computer cluster. Conclusions CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross‐correlation‐based registration approach, and has the potential to remove reliance on the implanted fiducials.

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Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography

Cited by 5 publications

References 15 publications

A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

A modern review of the uncertainties in volumetric imaging of respiratory‐induced target motion in lung radiotherapy

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

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

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