Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters

Tehrani, Joubin Nasehi; Yang, Yin; Werner, René; Lu, Wei; Low, Daniel A.; Guo, Xiaohu; Wang, Jing

doi:10.1088/0031-9155/60/22/8833

Cited by 22 publications

(29 citation statements)

References 42 publications

(61 reference statements)

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“…28 The optimized incompressibility factors of the lung material during biomechanical simulation are shown in the last column. The Euclidean landmark motion errors, derived from the biomechanical modeling using the real SDVFs (by deformable registration of 4D-CT) and the predicted SDVFs, are shown in the third and fourth columns, respectively (Table V).…”

Section: Resultsmentioning

confidence: 99%

“…28 In this approach, the uncoupled Mooney-Rivlin material model was used, and the whole lung was simulated as homogenous material. The incompressibility of the whole lung (k-factor in last column of Table V) was optimized iteratively for each patient to minimize the overall landmark errors.…”

Section: F Evaluating the Predicted Sdvfsmentioning

confidence: 99%

“…In previous studies, we evaluated the sensitivity of tumor motion simulation accuracy to lung biomechanical properties and developed an optimization-based method to estimate patient-specific optimal biomechanical parameters. 28,29 However, for our biomechanical modeling we assumed having direct access to lung surface deformation obtained by deformable image registration of the planning 4D-CT.…”

Section: Introductionmentioning

confidence: 99%

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Lung surface deformation prediction from spirometry measurement and chest wall surface motion

2016

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Purpose: The authors have developed and evaluated a method to predict lung surface motion based on spirometry measurements, and chest and abdomen motion at selected locations. Methods: A patient-specific 3D triangular surface mesh of the lung region was obtained at the end expiratory phase by the threshold-based segmentation method. Lung flow volume changes were recorded with a spirometer for each patient. A total of 192 selected points at a regular spacing of 2 × 2 cm matrix points were used to detect chest wall motion over a total area of 32 × 24 cm covering the chest and abdomen surfaces. QR factorization with column pivoting was employed to remove redundant observations of the chest and abdominal areas. To create a statistical model between the lung surface and the corresponding surrogate signals, the authors developed a predictive model based on canonical ridge regression. Two unique weighting vectors were selected for each vertex on the lung surface; they were optimized during the training process using all other 4D-CT phases except for the test inspiration phase. These parameters were employed to predict the vertex locations of a testing data set. Results: The position of each lung surface mesh vertex was estimated from the motion at selected positions within the chest wall surface and from spirometry measurements in ten lung cancer patients. The average estimation of the 98th error percentile for the end inspiration phase was less than 1 mm (AP = 0.9 mm, RL = 0.6 mm, and SI = 0.8 mm). The vertices located at the lower region of the lung had a larger estimation error as compared with those within the upper region of the lung. The average landmark motion errors, derived from the biomechanical modeling using real surface deformation vector fields (SDVFs), and the predicted SDVFs were 3.0 and 3.1 mm, respectively. Conclusions: Our newly developed predictive model provides a noninvasive approach to derive lung boundary conditions. The proposed system can be used with personalized biomechanical respiration modeling to derive lung tumor motion during radiation therapy from noninvasive measurements. C 2016 American Association of Physicists in Medicine. [http://dx

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Section: Resultsmentioning

confidence: 99%

Section: F Evaluating the Predicted Sdvfsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lung surface deformation prediction from spirometry measurement and chest wall surface motion

2016

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“…This approach usually works well for high-contrast regions [4], [6], [7] but its accuracy is often limited in low-contrast regions with subtle intensity differences [5]. In addition, the solved deformation fields may not be biomechanically realistic because the deformation fails to consider the elastic properties of anatomical structures [17]. …”

Section: Introductionmentioning

confidence: 99%

A Biomechanical Modeling Guided CBCT Estimation Technique

You

Tehrani

Wang

2017

IEEE Trans. Med. Imaging

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Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.

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“…Lung motion prediction, including tumors and OARs, has been studied using more advanced physical approaches, including biomechanical modeling with the finite element method (25, 26), motion vector modeling with deformable image registration (DIR) (27, 28), hybrid modeling with biomechanics and DIR (29, 30), and statistical modeling with respiratory parameters (31-33). Although the prediction accuracy might be clinically acceptable, the major limitations included a lack of real-time performance owing to the complex, iterative computation and lack of adaptations to changes of breathing behaviors or irregularities.…”

Section: Introductionmentioning

confidence: 99%

A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities

Yuan

Wei

Gaebler

et al. 2016

International Journal of Radiation Oncology*Biology*Physics

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Purpose To develop a physical, adaptive motion perturbation model to predict tumor motion using feedback from dynamic measurement of breathing conditions to compensate for breathing irregularities. Methods and Materials A novel respiratory motion perturbation (RMP) model was developed to predict tumor motion variations caused by breathing irregularities. This model contained 2 terms: the initial tumor motion trajectory, measured from 4-dimensional computed tomography (4DCT) images, and motion perturbation, calculated from breathing variations in tidal volume (TV) and breathing pattern (BP). The motion perturbation was derived from the patient-specific anatomy, tumor-specific location, and time-dependent breathing variations. Ten patients were studied, and 2 amplitude-binned 4DCT images for each patient were acquired within 2 weeks. The motion trajectories of 40 corresponding bifurcation points in both 4DCT images of each patient were obtained using deformable image registration. An in-house 4D data processing toolbox was developed to calculate the TV and BP as functions of the breathing phase. The motion was predicted from the simulation 4DCT scan to the treatment 4DCT scan, and vice versa, resulting in 800 predictions. For comparison, noncorrected motion differences and the predictions from a published 5-dimensional model were used. Results The average motion range in the superoinferior direction was 9.4 ± 4.4 mm, the average ΔTV ranged from 10 to 248 mm3 (−26% to 61%), and the ΔBP ranged from 0 to 0.2 (−71% to 333%) between the 2 4DCT scans. The mean noncorrected motion difference was 2.0 ± 2.8 mm between 2 4DCT motion trajectories. After applying the RMP model, the mean motion difference was reduced significantly to 1.2 ± 1.8 mm (P = .0018), a 40% improvement, similar to the 1.2 ± 1.8 mm (P = .72) predicted with the 5-dimensional model. Conclusions A novel physical RMP model was developed with an average accuracy of 1.2 ± 1.8 mm for interfraction motion prediction, similar to that of a published lung motion model. This physical RMP was analytically derived and is able to adapt to breathing irregularities. Further improvement of this RMP model is under investigation.

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Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters

Cited by 22 publications

References 42 publications

Lung surface deformation prediction from spirometry measurement and chest wall surface motion

Lung surface deformation prediction from spirometry measurement and chest wall surface motion

A Biomechanical Modeling Guided CBCT Estimation Technique

A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities

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