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Title: MO-AB-BRA-09: Development and Evaluation of a Biomechanical Modeling-Assisted CBCT Reconstruction Technique (Bio-Recon)

Abstract

Purpose: To develop a Bio-recon technique by incorporating the biomechanical properties of anatomical structures into the deformation-based CBCT reconstruction process. Methods: Bio-recon reconstructs the CBCT by deforming a prior high-quality CT/CBCT using a deformation-vector-field (DVF). The DVF is solved through two alternating steps: 2D–3D deformation and finite-element-analysis based biomechanical modeling. 2D–3D deformation optimizes the DVF through an ‘intensity-driven’ approach, which updates the DVF to minimize intensity mismatches between the acquired projections and the simulated projections from the deformed CBCT. In contrast, biomechanical modeling optimizes the DVF through a ‘biomechanical-feature-driven’ approach, which updates the DVF based on the biophysical properties of anatomical structures. In general, Biorecon extracts the 2D–3D deformation-optimized DVF at high-contrast structure boundaries, and uses it as the boundary condition to drive biomechanical modeling to optimize the overall DVF, especially at low-contrast regions. The optimized DVF is fed back into the 2D–3D deformation for further optimization, which forms an iterative loop. The efficacy of Bio-recon was evaluated on 11 lung patient cases, each with a prior CT and a new CT. Cone-beam projections were generated from the new CTs to reconstruct CBCTs, which were compared with the original new CTs for evaluation. 872 anatomical landmarks were also manually identifiedmore » by a clinician on both the prior and new CTs to track the lung motion, which was used to evaluate the DVF accuracy. Results: Using 10 projections for reconstruction, the average (± s.d.) relative errors of reconstructed CBCTs by the clinical FDK technique, the 2D–3D deformation-only technique and Bio-recon were 46.5±5.9%, 12.0±2.3% and 10.4±1.3%, respectively. The average residual errors of DVF-tracked landmark motion by the 2D–3D deformation-only technique and Bio-recon were 5.6±4.3mm and 3.1±2.4mm, respectively. Conclusion: Bio-recon improved accuracy for both the reconstructed CBCT and the DVF. The accurate DVF can benefit multiple clinical practices, such as image-guided adaptive radiotherapy. We acknowledge funding support from the American Cancer Society (RSG-13-326-01-CCE), from the US National Institutes of Health (R01 EB020366), and from the Cancer Prevention and Research Institute of Texas (RP130109).« less

Authors:
; ;  [1]
  1. UT Southwestern Medical Center, Dallas, TX (United States)
Publication Date:
OSTI Identifier:
22649498
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; COMPUTERIZED TOMOGRAPHY; DEFORMATION; ITERATIVE METHODS; OPTIMIZATION; PARTICLE TRACKS; SIMULATION

Citation Formats

Zhang, Y, Nasehi Tehrani, J, and Wang, J. MO-AB-BRA-09: Development and Evaluation of a Biomechanical Modeling-Assisted CBCT Reconstruction Technique (Bio-Recon). United States: N. p., 2016. Web. doi:10.1118/1.4957161.
Zhang, Y, Nasehi Tehrani, J, & Wang, J. MO-AB-BRA-09: Development and Evaluation of a Biomechanical Modeling-Assisted CBCT Reconstruction Technique (Bio-Recon). United States. doi:10.1118/1.4957161.
Zhang, Y, Nasehi Tehrani, J, and Wang, J. 2016. "MO-AB-BRA-09: Development and Evaluation of a Biomechanical Modeling-Assisted CBCT Reconstruction Technique (Bio-Recon)". United States. doi:10.1118/1.4957161.
@article{osti_22649498,
title = {MO-AB-BRA-09: Development and Evaluation of a Biomechanical Modeling-Assisted CBCT Reconstruction Technique (Bio-Recon)},
author = {Zhang, Y and Nasehi Tehrani, J and Wang, J},
abstractNote = {Purpose: To develop a Bio-recon technique by incorporating the biomechanical properties of anatomical structures into the deformation-based CBCT reconstruction process. Methods: Bio-recon reconstructs the CBCT by deforming a prior high-quality CT/CBCT using a deformation-vector-field (DVF). The DVF is solved through two alternating steps: 2D–3D deformation and finite-element-analysis based biomechanical modeling. 2D–3D deformation optimizes the DVF through an ‘intensity-driven’ approach, which updates the DVF to minimize intensity mismatches between the acquired projections and the simulated projections from the deformed CBCT. In contrast, biomechanical modeling optimizes the DVF through a ‘biomechanical-feature-driven’ approach, which updates the DVF based on the biophysical properties of anatomical structures. In general, Biorecon extracts the 2D–3D deformation-optimized DVF at high-contrast structure boundaries, and uses it as the boundary condition to drive biomechanical modeling to optimize the overall DVF, especially at low-contrast regions. The optimized DVF is fed back into the 2D–3D deformation for further optimization, which forms an iterative loop. The efficacy of Bio-recon was evaluated on 11 lung patient cases, each with a prior CT and a new CT. Cone-beam projections were generated from the new CTs to reconstruct CBCTs, which were compared with the original new CTs for evaluation. 872 anatomical landmarks were also manually identified by a clinician on both the prior and new CTs to track the lung motion, which was used to evaluate the DVF accuracy. Results: Using 10 projections for reconstruction, the average (± s.d.) relative errors of reconstructed CBCTs by the clinical FDK technique, the 2D–3D deformation-only technique and Bio-recon were 46.5±5.9%, 12.0±2.3% and 10.4±1.3%, respectively. The average residual errors of DVF-tracked landmark motion by the 2D–3D deformation-only technique and Bio-recon were 5.6±4.3mm and 3.1±2.4mm, respectively. Conclusion: Bio-recon improved accuracy for both the reconstructed CBCT and the DVF. The accurate DVF can benefit multiple clinical practices, such as image-guided adaptive radiotherapy. We acknowledge funding support from the American Cancer Society (RSG-13-326-01-CCE), from the US National Institutes of Health (R01 EB020366), and from the Cancer Prevention and Research Institute of Texas (RP130109).},
doi = {10.1118/1.4957161},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To evaluate a 4D-CBCT reconstruction technique both geometrically and dosimetrically Methods: A prior-knowledge guided 4DC-BCT reconstruction method named the motion-modeling and free-form deformation (MM-FD) has been developed. MM-FD views each phase of the 4D-CBCT as a deformation of a prior CT volume. The deformation field is first solved by principal component analysis based motion modeling, followed by constrained free-form deformation.The 4D digital extended-cardiac- torso (XCAT) phantom was used for comprehensive evaluation. Based on a simulated 4D planning CT of a lung patient, 8 different scenarios were simulated to cover the typical on-board anatomical and respiratory variations: (1) synchronized andmore » (2) unsynchronized motion amplitude change for body and tumor; tumor (3) shrinkage and (4) expansion; tumor average position shift in (5) superior-inferior (SI) direction, (6) anterior-posterior (AP) direction and (7) SI, AP and lateral directions altogether; and (8) tumor phase shift relative to the respiratory cycle of the body. Orthogonal-view 30° projections were simulated based on the eight patient scenarios to reconstruct on-board 4D-CBCTs. For geometric evaluation, the volume-percentage-difference (VPD) was calculated to assess the volumetric differences between the reconstructed and the ground-truth tumor.For dosimetric evaluation, a gated treatment plan was designed for the prior 4D-CT. The dose distributions were calculated on the reconstructed 4D-CBCTs and the ground-truth images for comparison. The MM-FD technique was compared with MM-only and FD-only techniques. Results: The average (±s.d.) VPD values of reconstructed tumors for MM-only, FDonly and MM-FD methods were 59.16%(± 26.66%), 75.98%(± 27.21%) and 5.22%(± 2.12%), respectively. The average min/max/mean dose (normalized to prescription) of the reconstructed tumors by MM-only, FD-only, MM-FD methods and ground-truth tumors were 78.0%/122.2%/108.2%, 13%/117.7%/86%, 58.1%/120.8%/103.6% and 57.6%/118.6%/101.8%,respectively. Conclusion: The MM-FD method provides superior reconstruction accuracy both geometrically and dosimetrically, which can potentially be used for 4D target localization, dose tracking and adaptive radiation therapy. This research is supported by grant from Varian Medical Systems.« less
  • Purpose: To develop a quasi-cine CBCT reconstruction technique that uses extremely-small angle (∼3°) projections to generate real-time high-quality lung CBCT images. Method: 4D-CBCT is obtained at the beginning and used as prior images. This study uses extremely-small angle (∼3°) on-board projections acquired at a single respiratory phase to reconstruct the CBCT image at this phase. An adaptive constrained free-form deformation (ACFD) method is developed to deform the prior 4D-CBCT volume at the same phase to reconstruct the new CBCT. Quasi-cine CBCT images are obtained by continuously reconstructing CBCT images at subsequent phases every 3° angle (∼0.5s). Note that the priormore » 4D-CBCT images are dynamically updated using the latest CBCT images. The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of ACFD. A lung patient was simulated with a tumor baseline shift of 2mm along superior-inferior (SI) direction after every respiratory cycle for 5 cycles. Limited-angle projections were simulated for each cycle. The 4D-CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate their geometric differences.The ACFD was also compared to a principal-component-analysis based motion-modeling (MM) method. Results: Using orthogonal-view 3° projections, the VPD/COMS values for tumor baseline shifts of 2mm, 4mm, 6mm, 8mm, 10mm were 11.0%/0.3mm, 25.3%/2.7mm, 22.4%/2.9mm, 49.5%/5.4mm, 77.2%/8.1mm for the MM method, and 2.9%/0.7mm, 3.9%/0.8mm, 6.2%/1mm, 7.9%/1.2mm, 10.1%/1.1mm for the ACFD method. Using orthogonal-view 0° projections (1 projection only), the ACFD method yielded VPD/COMS results of 5.0%/0.9mm, 10.5%/1.2mm, 15.1%/1.4mm, 20.9%/1.6mm and 24.8%/1.6mm. Using single-view instead of orthogonal-view projections yielded less accurate results for ACFD. Conclusion: The ACFD method accurately reconstructs snapshot CBCT images using orthogonal-view 3° projections. It has a great potential to provide real-time quasi-cine CBCT images for verification in lung radiation therapy. The research is supported by grant from Varian Medical Systems.« less
  • Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods:more » In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. Results: In numerical simulations, the 240{sup ∘} short scan angular span was divided into four consecutive 60{sup ∘} angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200{sup ∘}, three 66{sup ∘} angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60{sup ∘} angular subsectors.« less
  • Purpose: To develop an automatic markerless 4D-CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D-CT images. Methods: Each phase of onboard 4D-CBCT is considered as a deformation of one phase of the prior planning 4D-CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D-CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for eachmore » single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on-board images. Projections were simulated over 180 degree scan-angle for the 4D-XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy. Results: With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%. Conclusion: The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D-CBCT phase sorting. National Institutes of Health Grant No. R01-CA184173 Varian Medical System.« less
  • Purpose: To investigate the dosimetric accuracy of CBCTs estimated by a motion modeling and free-form deformation(MM-FD) technique for radiotherapy of lung cancer. Methods: Various inter-fractional variations featuring patient motion pattern change, tumor size change and tumor average position change were simulated from planning-CT to on-board images using both digital and physical motion phantoms. The doses calculated on the planning-CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses) and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress(FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the ‘gold-standard’ on-board images (gold-standard doses). The absolute deviations of minimummore » dose (dDmin), maximum dose (dDmax), mean dose (dDmean) and dose coverage (dV100%) of PTV were evaluated. In addition, 4D on-board dose accumulations were performed using the 4D-CBCT images estimated by MM-FD. The accumulated doses were compared to measurements using OSL detectors and radiochromic films. Results: Of all the 50 scenarios simulated, the average(± standard deviation) dDmin, dDmax, dDmean and dV100% (values normalized by the prescription dose or the PTV volume) between the planned and the gold-standard PTV doses were 34.8% (± 29.2%), 3.2% (± 3.8%), 3.5% (± 3.5%) and 13.0% (± 11.4%), respectively. The corresponding values of FDK PTV doses were 3.1% (± 3.7%), 1.4% (± 1.1%), 2.1% (± 0.8%) and 14.5% (± 14.2%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (± 0.5%), 0.9% (± 0.7%), 0.6% (± 0.4%) and 0.9% (± 0.8%), respectively.For the 4D dose accumulation study, the average(± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.0% (± 2.4%). The average gamma pass-rate(3%/3mm) between the accumulated doses and the radiochromic film measured doses was 96.1%. Conclusion: MM-FD estimated CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. The research was funded by the National Institutes of Health Grant No. R01-CA184173 and a grant from Varian Medical Systems.« less