3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Li, Ruijiang; Lewis, John H.; Jia, Xun; Gu, Xuejun; Folkerts, Michael; Men, Chunhua; Song, William Y.; Jiang, Steve B

doi:10.1118/1.3582693

Cited by 65 publications

(132 citation statements)

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“…In (S1), to realize the c operator, linear intensity equalization is implemented as in Ref. 45. To address the residual intensity inconsistency between CT and CBCT images, which is indeed an open problem existing in all 2D-3D matching-based methods, 27 Demons with simultaneous intensity correction (DISC) (Ref.…”

Section: A3 Implementationmentioning

confidence: 99%

“…In light of the availability of this prior information, 2D-3D matching-based reconstruction models have been proposed in a wide spectrum of imaging tasks, such as tomosynthesis, real-time volumetric imaging, and 3D/4D CBCT. 27,36,37,39,[43][44][45] These methods optimize a motion vector field (MVF) to deform the p-CT, such that its forward projections match the measurements. Brock et al proposed to match the forward projections by adjusting the MVF control points, so that the forward projections have minimal mismatch with the measurements.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

et al. 2014

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Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

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Section: A3 Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

et al. 2014

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“…As the organ displacement around the diaphragm could be 2-3 cm or larger in the superior-inferior (SI) direction, respiratory motion should be fully considered for radiation treatments involving thoracic and upper abdominal cancers that involve lung, stomach, pancreas, and liver. 1 The discrepancy could be especially severe in stereotactic body radiation therapy (SBRT) for lung cancer treatment because the consequence is magnified by its low fraction number (3-5 fractions) and high fractional dose (12)(13)(14)(15)(16)(17)(18) Gy per fraction). However, as in vivo dosimetry is currently not available clinically, dose-response studies are generally based on prescribed or planned dose instead of delivered dose.…”

Section: Introductionmentioning

confidence: 99%

“…This is the first proof-of-concept study of delivered dose assessment for SBRT lung cancer treatment that combines principal component analysis (PCA) approach [11][12][13][14][15][16] for 3D reconstructions at sampled timepoints and the Monte Carlobased Dose Planning Method (DPM) 17 for dose calculation. Although the PCA approach has been validated for tumor localization with the presence of significant respiratory motion, further investigation was needed to investigate the feasibility, accuracy, and potential usefulness of using these 3D images for dose calculation, particularly for patients who breathe differently than they did during acquisition of the 4DCT used for motion modeling.…”

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3D delivered dose assessment using a 4DCT-based motion model

et al. 2015

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Purpose: The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course of stereotactic body radiation therapy (SBRT). Methods: A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient-specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basis DVFs. (2) During the treatment procedure, 2D time-varying projection images (either kV or MV projections) are acquired, from which time-varying "fluoroscopic" 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time-varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio-torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT-based planning and to the actual delivered dose that was calculated using "ground truth" XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario. Results: For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction-based dose estimates. Conclusions: With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern.

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“…Recent advances of the IGRT registration methods emphasize real-time computation and low-dose image acquisition. Russakoff et al [1,2], Khamene et al [3], Munbodh et al [4], Li et al [5,6] rejected the time-consuming 3D/3D registration and performed 2D/3D registration by optimizing similarity functions defined in the projection domain. Other than the optimization-based methods, Chou et al [7,8] recently introduced a faster and low-dose 2D/3D image registration by using a linear operator that approximates the deformation parameters.…”

Section: Introductionmentioning

confidence: 99%

Real-Time 2D/3D Deformable Registration Using Metric Learning

Chou

Pizer

2013

Lecture Notes in Computer Science

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Abstract. We present a novel 2D/3D deformable registration method, called Registration Efficiency and Accuracy through Learning Metric on Shape (REALMS ), that can support real-time Image-Guided Radiation Therapy (IGRT ). The method consists of two stages: planning-time learning and registration. In the planning-time learning, it firstly models the patient's 3D deformation space from the patient's time-varying 3D planning images using a low-dimensional parametrization. Secondly, it samples deformation parameters within the deformation space and generates corresponding simulated projection images from the deformed 3D image. Finally, it learns a Riemannian metric in the projection space for each deformation parameter. The learned distance metric forms a Gaussian kernel of a kernel regression that minimizes the leave-one-out regression residual of the corresponding deformation parameter. In the registration, REALMS interpolates the patient's 3D deformation parameters using the kernel regression with the learned distance metrics. Our test results showed that REALMS can localize the tumor in 10.89 ms (91.82 fps) with 2.56 ± 1.11 mm errors using a single projection image. These promising results show REALMS's high potential to support realtime, accurate, and low-dose IGRT.

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3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Abstract: 9 10Purpose: To evaluate an algorithm for real-time 3D tumor localization from a single x-11 ray projection image for lung cancer radiotherapy. 13Methods: Recently we have developed an algorithm for reconstructing volumetric

Cited by 65 publications

References 24 publications

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

3D delivered dose assessment using a 4DCT-based motion model

Real-Time 2D/3D Deformable Registration Using Metric Learning

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