Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients

Sharifi, Hoda; Zhang, Hong; Bagher‐Ebadian, Hassan; Lu, Wei; Ajlouni, Munther; Jin, Jian-Yue; Kong, Feng-Ming; Chetty, Indrin J.; Zhong, Hualiang

doi:10.1088/1361-6560/aab235

Cited by 12 publications

(14 citation statements)

References 25 publications

(27 reference statements)

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“…In recent years, a large number of non-rigid registration models for medical images has been reported, such as the free form deformation (FFD) models based on B-spline [9][10][11], finite elements model (FEM) [12][13][14][15], viscous fluid model [16,17], and Demons [4,5,[18][19][20][21][22] etc. Since FFD based on B-spline employs the cubic B-spline to model the elastic deformation and each B-spline curve is only related to four adjacent control points, it can provide a high degree of flexibility for estimating the local motions of human tissues and organs.…”

Section: Related Workmentioning

confidence: 99%

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Lei

et al. 2021

IET Image Processing

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Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nyström approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Lei

et al. 2021

IET Image Processing

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“…For this purpose, specific tools, developed in academic institutions, are imported to commercial RT TPS [12,139,141]. Therefore, DIR algorithms require further development to better meet clinical needs, such as accounting for the different imaging modalities between planning and treatment delivery [133,142], near-real-time algorithms with graphic processing unit-based frameworks [139,[143][144][145], and anatomical properties simulated by finite element models [146][147][148][149][150].…”

Section: Uncertainties and Perspectives Of Dir In Rtmentioning

confidence: 99%

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

et al. 2019

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Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformationbased planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of reirradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

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“…This may introduce large uncertainties in the computation of ventilation images. In order to reduce the effect of displacement errors in individual voxels, we used a finite element-based biomechanical method in combination with DIR to calculate the relative volumetric variation in the lung (11)(12)(13).…”

Section: Original Articlementioning

confidence: 99%

Four-dimensional computed tomography-based biomechanical measurements of pulmonary function and their correlation with clinical outcome for lung stereotactic body radiation therapy patients

Sharifi

McDonald

Lee

et al. 2019

Quant. Imaging Med. Surg.

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Background: Functional image guided radiotherapy allows for the delivery of an equivalent dose to tumor targets while sparing high ventilation lung tissues. In this study, we investigate whether radiation dose to functional lung is associated with clinical outcome for stereotactic body radiation therapy (SBRT) patients.Methods: Four-dimensional computed tomography (4DCT) images were used to assess lung function.Deformable image registration (DIR) was performed from the end-inhale phase to the end-exhale phase with resultant displacement vectors used to calculate ventilation maps. In addition to the Jacobian-based ventilation we introduce a volumetric variation method (Rv) based on a biomechanical finite element method (FEM), to assess lung ventilation. Thirty NSCLC patients, treated with SBRT, were evaluated in this study.4DCT images were used to calculate both Jacobian and Rv-based ventilation images. Areas under the receiver operating characteristic curve (AUC) were used to assess the predictive power of functional metrics.Metrics were calculated over the whole lung as well as high and low ventilated regions.Results: Ventilation in dose regions between 1 and 5 Gy had higher AUC values compared to other dose regions. Rv based ventilation imaging method also showed to be less spatially variant and less heterogeneous, and the resultant Rv metrics had higher AUC values for predicting grade 2+ dyspnea.Conclusions: Low dose delivered to high ventilation areas may also increase the risk of compromised pulmonary function. Rv based ventilation images could be useful for the prediction of clinical toxicity for lung SBRT patients.

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Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients

Cited by 12 publications

References 25 publications

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Four-dimensional computed tomography-based biomechanical measurements of pulmonary function and their correlation with clinical outcome for lung stereotactic body radiation therapy patients

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