A constrained linear regression optimization algorithm for diaphragm motion tracking with cone beam CT projections

Wei, Jie; Chao, M.

doi:10.1016/j.ejmp.2018.01.005

Cited by 4 publications

(5 citation statements)

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“…Similarly, tumor localization errors ranging over 0.6-4.3 mm were observed in Ref. 20 for fiducial markers. However, 3D motion models were built only to demonstrate the feasibility of real-time tracking for targets influenced by respiratory motion and their precision should be read within this context.…”

Section: Discussionsupporting

confidence: 58%

“…Previously reported tracking algorithms typically rely on a thresholding technique or impose various optimization constraints during imaging. For instance, in a recent paper, diaphragm identification was considered as a constrained linear regression optimization problem . Under this approach, thresholding was achieved using Otsu's method, the geometry was approximated by parabolas and the temporal redundancy of diaphragmatic motion was modelled by applying both band and algebraic constraints.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in a recent paper, diaphragm identification was considered as a constrained linear regression optimization problem. 20 Under this approach, thresholding was achieved using Otsu's method, the geometry was approximated by parabolas and the temporal redundancy of diaphragmatic motion was modelled by applying both band and algebraic constraints. Critically, since these computations were carried out for each image individually, execution of the algorithm was not achievable in real-time.…”

Section: Introductionmentioning

confidence: 99%

“…Evaluating the performance of diaphragm tracking algorithms has typically involved manual delineation of the diaphragm by a clinician. [20][21][22][23] This has two important limitations. First, such judgments must be made subjectively.…”

Section: Introductionmentioning

confidence: 99%

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Real‐time direct diaphragm tracking using kV imaging on a standard linear accelerator

et al. 2019

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Purpose As the predominant driver of respiratory motion, the diaphragm represents a key surrogate for motion management during the irradiation of thoracic cancers. Existing approaches to diaphragm tracking often produce phase‐based estimates, suffer from lateral side failures or are not executable in real‐time. In this paper, we present an algorithm that continuously produces real‐time estimates of three‐dimensional (3D) diaphragm position using kV images acquired on a standard linear accelerator. Methods Patient‐specific 3D diaphragm models were generated via automatic segmentation on end‐exhale four‐dimensional‐computed tomography (4D‐CT) images. The estimated trajectory of diaphragmatic motion, referred to as the principal motion vector, was obtained by registering end‐exhale to end‐inhale 4D‐CT images. Two‐dimensional (2D) diaphragm masks were generated by forward‐projecting 3D models over the complement of angles spanned during kV image acquisition. For each kV image, diaphragm position was determined by shifting angle‐matched 2D masks along the principal motion vector and selecting the position of highest contrast on a vertical difference image. Retrospective analysis was performed using 22 cone beam CT (CBCT) image sequences for six lung cancer patients across two datasets. Given the current lack of objective ground truth for diaphragm position, our algorithm was evaluated by examining its ability to track implanted markers. Simple linear regression was used to construct 3D marker motion models and estimation errors were computed as the difference between estimated and ground truth marker positions. Additionally, Pearson correlation coefficients were used to characterize diaphragm‐marker correlation. Results The mean ± standard deviation of the estimation errors across all image sequences was −0.1 ± 0.7 mm, −0.1 ± 1.8 mm and 0.2 ± 1.4 mm in the LR, SI, and AP directions respectively. The 95th percentile of the absolute errors ranged over 0.5–3.1 mm, 1.6–6.7 mm, and 1.2–4.0 mm in the LR, SI, and AP directions, respectively. The mean ± standard deviation of diaphragm‐marker correlations over all image sequences was −0.07 ± 0.57, 0.67 ± 0.49, and 0.29 ± 0.52 in the LR, SI, and AP directions, respectively. Diaphragm‐marker correlation was observed to be highly dependent on marker position. Mean correlation along the SI axis ranged over 0.91–0.93 for markers situated in the lower lobes of the lung, while correlations ranging over −0.51–0.79 were observed for markers situated in the upper and middle lobes. Conclusion This work advances a new approach to real‐time direct diaphragm tracking in realistic treatment scenarios. By achieving continuous estimates of diaphragmatic motion, the proposed method has applications for both markerless tumor tracking and respiratory binning in 4D‐CBCT.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real‐time direct diaphragm tracking using kV imaging on a standard linear accelerator

et al. 2019

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“…However, the problem with tracking tumor motion in fluoroscopic images is that it is difficult to identify tumor targets because of the poor image contrast. The upper edge of the diaphragm is sharp in contrast to the neighboring tissue and, therefore, can be more reliably identified than the tumor in the abdominal region (11). Studies have reported that tumor motion near the diaphragm has a high correlation with diaphragm motion, suggesting that the diaphragm could be used as an internal surrogate for predicting tumor motion without the need for fiducial implants (12,13).…”

Section: Introductionmentioning

confidence: 99%

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning

Dai¹,

He²,

Zhu³

et al. 2023

Quant Imaging Med Surg

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Background: The aim of this study was to establish a correlation model between external surface motion and internal diaphragm apex movement using machine learning and to realize online automatic prediction of the diaphragm motion trajectory based on optical surface monitoring. Methods:The optical body surface parameters and kilovoltage (kV) X-ray fluoroscopic images of 7 liver tumor patients were captured synchronously for 50 seconds. The location of the diaphragm apex was manually delineated by a radiation oncologist and automatically detected with a convolutional network model in fluoroscopic images. The correlation model between the body surface parameters and the diaphragm apex of each patient was developed through linear regression (LR) based on synchronous datasets before radiotherapy.Model 1 (M1) was trained with data from the first 30 seconds of the datasets and tested with data from the following 20 seconds of the datasets in the first fraction to evaluate the intra-fractional prediction accuracy.Model 2 (M2) was trained with data from the first 30 seconds of the datasets in the next fraction. The motion trajectory of the diaphragm apex during the following 20 seconds in the next fraction was predicted with M1 and M2, respectively, to evaluate the inter-fractional prediction accuracy. The prediction errors of the 2 models were compared to analyze whether the correlation model needed to be re-established. Results:The average mean absolute error (MAE) and root mean square error (RMSE) using M1 trained with automatic detection location for the first fraction were 3.12±0.80 and 3.82±0.98 mm in the superiorinferior (SI) direction and 1.38±0.24 and 1.74±0.32 mm in the anterior-posterior (AP) direction, respectively.The average MAE and RMSE of M1 versus M2 in the AP direction were 2.63±0.71 versus 1.28±0.48 mm and 3.26±0.90 versus 1.61±0.60 mm, respectively. The average MAE and RMSE of M1 versus M2 in the SI direction were 5.84±1.22 versus 3.37±0.43 mm and 7.22±1.45 versus 4.07±0.54 mm, respectively. The prediction accuracy of M2 was significantly higher than that of M1. Conclusions:This study shows that it is feasible to use optical body surface information to automatically predict the diaphragm motion trajectory. At the same time, it is necessary to establish a new correlation model for the current fraction before each treatment.

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Motion analysis comparing surface imaging and diaphragm tracking on kV projections for deep inspiration breath hold (DIBH)

Chen,

Chiu,

Folkert

et al. 2024

Physica Medica

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A constrained linear regression optimization algorithm for diaphragm motion tracking with cone beam CT projections

Abstract: The new algorithm will provide a potential solution to rendering diaphragm motion and possibly aiding the tumor target tracking in radiation therapy of thoracic/abdominal cancer patients.

Cited by 4 publications

References 41 publications

Real‐time direct diaphragm tracking using kV imaging on a standard linear accelerator

Real‐time direct diaphragm tracking using kV imaging on a standard linear accelerator

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning

Motion analysis comparing surface imaging and diaphragm tracking on kV projections for deep inspiration breath hold (DIBH)

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