Simultaneous Motion Estimation And Image Reconstruction Research Articles

Reducing 4DCBCT imaging dose and time: exploring the limits of adaptive acquisition and motion compensated reconstruction

This study investigates the dose and time limits of adaptive 4DCBCT acquisitions (adaptive-acquisition) compared with current conventional 4DCBCT acquisition (conventional-acquisition). We investigate adaptive-acquisitions as low as 60 projections (∼25 s scan, 6 projections per respiratory phase) in conjunction with emerging image reconstruction methods. 4DCBCT images from 20 patients recruited into the adaptive CT acquisition for personalized thoracic imaging clinical study (NCT04070586) were resampled to simulate faster and lower imaging dose acquisitions. All acquisitions were reconstructed using Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), motion compensated FDK (MCFDK), motion compensated MKB (MCMKB) and simultaneous motion estimation and image reconstruction (SMEIR) algorithms. All reconstructions were compared against conventional-acquisition 4DFDK-reconstruction using Structural SIMilarity Index (SSIM), signal-to-noise ratio (SNR), contrast-to-noise-ratio (CNR), tissue interface sharpness diaphragm (TIS-D), tissue interface sharpness tumor (TIS-T) and center of mass trajectory (COMT) for difference in diaphragm and tumor motion. All reconstruction methods using 110-projection adaptive-acquisition (11 projections per respiratory phase) had a SSIM of greater than 0.92 relative to conventional-acquisition 4DFDK-reconstruction. Relative to conventional-acquisition 4DFDK-reconstruction, 110-projection adaptive-acquisition MCFDK-reconstructions images had 60% higher SNR, 10% higher CNR, 30% higher TIS-T and 45% higher TIS-D on average. The 110-projection adaptive-acquisition SMEIR-reconstruction images had 123% higher SNR, 90% higher CNR, 96% higher TIS-T and 60% higher TIS-D on average. The difference in diaphragm and tumor motion compared to conventional-acquisition 4DFDK-reconstruction was within submillimeter accuracy for all acquisition reconstruction methods. Adaptive-acquisitions resulted in faster scans with lower imaging dose and equivalent or improved image quality compared to conventional-acquisition. Adaptive-acquisition with motion compensated-reconstruction enabled scans with as low as 110 projections to deliver acceptable image quality. This translates into 92% lower imaging dose and 80% less scan time than conventional-acquisition.

U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction.

For four-dimensional cone-beam computed tomography (4D-CBCT), its image quality is usually degraded by insufficient projections at each respiratory phase after phase-sorting. Recently, we developed a simultaneous motion estimation and image reconstruction (SMEIR) technique, which can improve lung 4D-CBCT reconstruction quality by incorporating an interphase motion model generated as deformation vector fields (DVFs). Simultaneous motion estimation and image reconstruction uses an intensity-driven two-dimensional (2D)-three-dimensional (3D) deformation technique to estimate these DVFs by intensity-matching 2D projections. However, 2D-3D deformation may fail to generate accurate intra-lung DVFs, since the motion of intricate, small lung structures only leads to subtle intensity variations on 2D projections that are insufficient to drive accurate DVF optimization. This study is to develop convolutional neural network (CNN)-based methods to fine-tune the 2D-3D deformation DVFs to improve the efficiency and accuracy of 4D-CBCT reconstruction. We built two U-net-based architectures for this study. The first architecture (U-net-3C) uses 2D-3D deformation-estimated DVFs (in three cardinal directions) as the input with three channels (3C), and outputs are fine-tuned DVFs. For the second architecture (U-net-4C), the reference phase CBCT image reconstructed by SMEIR was added as an additional input channel (4C) to represent patient-specific heterogeneous properties of the lung. The output fine-tuned high-quality DVFs of both models were input again into the SMEIR workflow, as an optimized motion model, to generate the final 4D-CBCT. Both methods were evaluated on 11 lung patient cases, using fivefold cross-validation. We also reconstructed 4D-CBCTs by the original SMEIR and the SMEIR-Bio (SMEIR with biomechanical modeling) algorithms for comparison. The 4D-CBCT accuracy was quantitatively assessed through metrics including root-mean-square-error (RMSE), universal quality index (UQI), and normalized cross-correlation (NCC). The DVF accuracy was evaluated by manually tracked lung landmarks. We also evaluated our proposed methods on the SPARE challenge dataset based on reconstructed 4D-CBCT quality using the above metrics. The average (±standard deviation) residual DVF errors of SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 3.88±3.12mm, 3.71±2.90mm, 3.75±3.40mm, and 5.73±4.61mm, respectively. The SMEIR-U-net-3C and SMEIR-U-net-4C generated images of generally improved RMSE, UQI, and NCC as compared to the other methods. Compared with SMERI-U-net-3C, SMEIR-U-net-4C has slightly higher 4D-CBCT reconstruction and DVF estimation accuracy. For the SPARE dataset, the UQI for SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 0.96, 0.97, 0.96, and 0.94. The CNN-based models can achieve fast (~10s) and accurate DVF fine-tuning to improve the efficiency and accuracy of 4D-CBCT reconstruction.

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

Dosimetric evaluation of 4D‐CBCT reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for carbon ion therapy of lung cancer

Motion management is critical for the efficacy of carbon ion therapy for moving targets such as lung tumors. We evaluated the feasibility of using four-dimensional cone beam computed tomography (4D-CBCT) reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for dose calculation and accumulation in carbon ion treatment of lung cancer. Motion-compensated 4D-CBCT images were reconstructed with the SMEIR algorithm to capture the most updated anatomy and motion with an updated interphase motion model on the treatment day. Projections of all CBCT phases were simulated from the planning 4D-CT by the ray tracing technique. Treatment planning and dose calculation were performed with a GPU-based Monte Carlo dose calculation software for carbon ion therapy. The treatment plan was optimized on the average computed tomography (CT) to obtain optimal intensity of the carbon ions. From the optimized plan, dose distributions on individual phases of 4D-CT and 4D-CBCT were calculated by the Monte Carlo-based dose engine. Dose accumulation was performed on 4D-CBCT images using deformable vector fields (DVF) generated by SMEIR. The accumulated planning target volume (PTV) dose based on 4D-CBCT was then compared to the accumulated dose calculated on 4D-CT, where the DVFs between different phases were obtained by the demons deformable registration algorithm. Dose value histograms (DVH) as well as absolute deviations of the maximum dose ( ), mean dose ( ), and dose coverage metrics ( and ) for PTV were quantitatively evaluated for the two sets of plans. Good agreement was found between the 4D-CT and 4D-CBCT-based PTV-DVH curves. The average values of , , and calculated between the 4D-CT and SMEIR-4D-CBCT-based plans were , , 2.12%, and , respectively, for the PTVs of ten patient case studies. Based on these results, SMEIR-reconstructed 4D-CBCTs can potentially be used for motion estimation, dose evaluation, and adaptive treatment planning in lung cancer carbon ion therapy.

Quantitative 4D-PET reconstruction for small animal using SMEIR-reconstructed 4D-CBCT.

Respiratory motions in small animals PET cause image degradation during reconstruction. This work aims to develop a motion compensated 4D-PET reconstruction method using accurate motion corrections and attenuation corrections from 4D-CBCT images reconstructed using a simultaneous motion estimation and image reconstruction (SMEIR) method. Projections of 4D-CBCT were calculated using a ray-tracing method on a digital 4D rat phantom, and list-mode data of 4D-PET with matched respiratory phases were simulated using the GATE Monte Carlo package. The respiratory rate was set at 1.0 second per cycle with 10 phases of 30 projection images each. 4D-CBCT images were reconstructed using the SMEIR method and motion information and linear attenuation from 4D-CBCT were subsequently used for motion compensated 4D-PET reconstruction and attenuation corrections. We quantitatively evaluate the reconstructed 4D-PET using the errors of tumor volume and standard uptake values of tumors with different sizes. The tumor motion was successfully reconstructed and showed good agreement with the original phantom. The proposed method reduced tumor volume errors and standard uptake value errors. For tumor diameters of 3.0, 4.5, and 6.0 mm, the tumor volume errors are 32.5%, 29.2% and 19.4% respectively with motion compensation and 45.1%, 37.5% and 20.2% respectively without compensation.

A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ’s fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model’s accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.

Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR)

In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10–40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.

TH‐EF‐207A‐05: Feasibility of Applying SMEIR Method On Small Animal 4D Cone Beam CT Imaging

Purpose:Small animal cone beam CT imaging has been widely used in preclinical research. Due to the higher respiratory rate and heat beats of small animals, motion blurring is inevitable and needs to be corrected in the reconstruction. Simultaneous motion estimation and image reconstruction (SMEIR) method, which uses projection images of all phases, proved to be effective in motion model estimation and able to reconstruct motion‐compensated images. We demonstrate the application of SMEIR for small animal 4D cone beam CT imaging by computer simulations on a digital rat model.Methods:The small animal CBCT imaging system was simulated with the source‐to‐detector distance of 300 mm and the source‐to‐object distance of 200 mm. A sequence of rat phantom were generated with 0.4 mm3 voxel size. The respiratory cycle was taken as 1.0 second and the motions were simulated with a diaphragm motion of 2.4mm and an anterior‐posterior expansion of 1.6 mm. The projection images were calculated using a ray‐tracing method, and 4D‐CBCT were reconstructed using SMEIR and FDK methods. The SMEIR method iterates over two alternating steps: 1) motion‐compensated iterative image reconstruction by using projections from all respiration phases and 2) motion model estimation from projections directly through a 2D‐3D deformable registration of the image obtained in the first step to projection images of other phases.Results:The images reconstructed using SMEIR method reproduced the features in the original phantom. Projections from the same phase were also reconstructed using FDK method. Compared with the FDK results, the images from SMEIR method substantially improve the image quality with minimum artifacts.Conclusion:We demonstrate that it is viable to apply SMEIR method to reconstruct small animal 4D‐CBCT images.

4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling

A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp–Davis–Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

WE-AB-204-09: Respiratory Motion Correction in 4D-PET by Simultaneous Motion Estimation and Image Reconstruction (SMEIR)

Purpose: In conventional 4D-PET, images from different frames are reconstructed individually and aligned by registration methods. Two issues with these approaches are: 1) Reconstruction algorithms do not make full use of all projections statistics; and 2) Image registration between noisy images can Result in poor alignment. In this study we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) method for cone beam CT for motion estimation/correction in 4D-PET. Methods: Modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM- TV) is used to obtain a primary motion-compensated PET (pmc-PET) from all projection data using Demons derived deformation vector fields (DVFs) as initial. Motion model update is done to obtain an optimal set of DVFs between the pmc-PET and other phases by matching the forward projection of the deformed pmc-PET and measured projections of other phases. Using updated DVFs, OSEM- TV image reconstruction is repeated and new DVFs are estimated based on updated images. 4D XCAT phantom with typical FDG biodistribution and a 10mm diameter tumor was used to evaluate the performance of the SMEIR algorithm. Results: Image quality of 4D-PET is greatly improved by the SMEIR algorithm. When all projections are used to reconstruct a 3D-PET, motion blurring artifacts are present, leading to a more than 5 times overestimation of the tumor size and 54% tumor to lung contrast ratio underestimation. This error reduced to 37% and 20% for post reconstruction registration methods and SMEIR respectively. Conclusion: SMEIR method can be used for motion estimation/correction in 4D-PET. The statistics is greatly improved since all projection data are combined together to update the image. The performance of the SMEIR algorithm for 4D-PET is sensitive to smoothness control parameters in the DVF estimation step.

TH-CD-303-10: 4D Cone-Beam CT Reconstruction Using Multi-Organ Meshes for Sliding Motion Modeling

Purpose: In the simultaneous motion estimation and image reconstruction (SMEIR) algorithm for 4D cone-beam CT (4D-CBCT), the motion model is obtained by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to enhance the performance of the SMEIR for 4D-CBCT by using a multi-organ meshing model to explicitly consider the discontinuity in the motion fields between different organs during the respiration. Methods: In the SMEIR algorithm, the deformable motion between a reference phase 4D-CBCT and other phases is obtained by matching the 4D-CBCT projections at each phase with the corresponding forward projections of the deformed reference phase. In this work, the sliding motion between lung and thoracic cage is considered by a multi-organ meshing model. Specifically, lung and thoracic cage are first segmented from a reference phase 4D-CBCT. Multi-organ tetrahedral meshes are then created from the segmented images to control different motions for different organs during the respiration. In the multi-organ meshes, continuous motion is enforced within each organ and along the normal direction of the organ interface while discontinuous motion is allowed along the tangential direction of the organ interface. The updated motion model is then used in the motion-compensated image reconstruction step in the SMEIR algorithm. Results: In 4D NCAT digital phantom, normalized cross correlations between reconstructed image and the ground truth image for homogeneous and multi-organ mesh-based methods are 0.9893 and 0.9920, respectively; the maximum motion errors along the interface between the lung and thoracic cage for homogeneous and multi-organ mesh-based methods are 6.12 mm and 0.95 mm, respectively. Conclusion: The multi-organ mesh-based 4D-CBCT reconstruction method can estimate the motion fields accurately by considering the sliding motion between different organs. Both image quality and motion estimating accuracy are improved by the proposed method as compared to traditional homogeneous mesh-based approach.

SU‐D‐207‐04: GPU‐Based 4D Cone‐Beam CT Reconstruction Using Adaptive Meshing Method

Purpose:Due to the limited number of projections at each phase, the image quality of a four‐dimensional cone‐beam CT (4D‐CBCT) is often degraded, which decreases the accuracy of subsequent motion modeling. One of the promising methods is the simultaneous motion estimation and image reconstruction (SMEIR) approach. The objective of this work is to enhance the computational speed of the SMEIR algorithm using adaptive feature‐based tetrahedral meshing and GPU‐based parallelization.Methods:The first step is to generate the tetrahedral mesh based on the features of a reference phase 4D‐CBCT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. After the mesh generation, the updated motion model and other phases of 4D‐CBCT can be obtained by matching the 4D‐CBCT projection images at each phase with the corresponding forward projections of the deformed reference phase of 4D‐CBCT. The entire process of this 4D‐CBCT reconstruction method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its tremendous parallel computing ability.Results:A 4D XCAT digital phantom was used to test the proposed mesh‐based image reconstruction algorithm. The image Result shows both bone structures and inside of the lung are well‐preserved and the tumor position can be well captured. Compared to the previous voxel‐based CPU implementation of SMEIR, the proposed method is about 157 times faster for reconstructing a 10 ‐phase 4D‐CBCT with dimension 256×256×150.Conclusion:The GPU‐based parallel 4D CBCT reconstruction method uses the feature‐based mesh for estimating motion model and demonstrates equivalent image Result with previous voxel‐based SMEIR approach, with significantly improved computational speed.

A Pilot Evaluation of a 4-Dimensional Cone-Beam Computed Tomographic Scheme Based on Simultaneous Motion Estimation and Image Reconstruction

To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies. The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm. The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients. The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.

TH‐E‐17A‐03: Development and Evaluation of a 4D‐CBCT Scheme Based On Simultaneous Motion Estimation and Image Reconstruction

Purpose:To develop and evaluate the performance of a novel 4D‐CBCT reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies.Methods:The SMEIR algorithm consists of two alternating steps: 1) motion‐compensated CBCT reconstruction; and 2) motion model estimation from the projections directly. In step 1, projections from all phases are used to reconstruct a reference phase 4D‐CBCT by explicitly considering the motion models between different phases. In step 2, updated inverse consistent motion models are obtained directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D‐CBCT. Motion model estimation was implemented on GPU to increase the computation efficiency. Two lung cancer patients were scanned for 4.5 and 5.7 minutes. Totally 1679 and 1982 projections were acquired and grouped into 10 phases. To evaluate SMEIR algorithm performance on conventional 1‐minute CBCT scan, projections at each phase were down‐sampled by factors ranging from 2 to 10. 4D‐CBCT were reconstructed from down‐sampled projections using FDK, total variation minimization (TV) and SMEIR. Using 4D‐CBCT reconstructed from fully sampled projections as a reference, relative errors of image and tumor motion trajectory were analyzed to quantify performances of different reconstruction algorithms.Results:SMEIR algorithm outperforms FDK and TV in both reconstruction accuracy and tumor tracking accuracy. When averaged projection number per phase decreases to 18 in two patients, relative reconstruction errors for FDK, TV and SMEIR are 38.00%, 11.72% and 9.59%, respectively. The maximum tumor tracking errors are 2.53 mm, 2.05 mm, and 0.68 mm for FDK, TV and SMEIR, respectively.Conclusion:Patient studies show that the SMEIR algorithm achieves 1‐mm tumor tracking accuracy when the average projection number per phase reduces to 18. SMEIR algorithm enables the use of conventional 1‐minute CBCT for accurate motion modeling and 4D‐CBCT reconstruction.

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone‐beam CT

Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

TH‐C‐103‐02: Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for 4D Cone‐Beam CT

Purpose: Image reconstruction and motion model estimation in four dimensional cone‐beam CT (4D‐CBCT) are handled as two sequential steps conventionally. Due to limited number of projections at each phase, the image quality of 4D‐CBCT is degraded by the view‐aliasing artifacts and the accuracy of subsequent motion modeling is compromised by inferior 4D‐CBCT. The objective of this work is to enhance the image quality of 4D‐CBCT and accuracy of motion model estimation through developing a novel strategy that is able to perform Simultaneous Motion Estimation and Image Reconstruction (SMEIR). Methods: The proposed SMEIR algorithm consists of two alternating steps: 1) model‐based iterative image reconstruction to obtain motion‐compensated CBCT (mCBCT) and 2) motion model estimation through the deformable registration of mCBCT and projections of individual phase of 4D‐CBCT. The iterative reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation regularization. During the forward‐and back‐projection of SART, all projection data are used for the reconstruction of mCBCT by utilizing the estimated deformable vector fields (DVF). The DVF is estimated by matching the forward projection of the deformed mCBCT and projections of other phases of 4D‐CBCT. A 4D NCAT phantom is used to quantitatively evaluate the performance of SMEIR algorithm with comparison to FDK and total variation (TV) minimization algorithm. Results: When all projections are used for image reconstruction by FDK, motion blurring artifacts are presented in 3D‐CBCT, leading to 33.1% relative reconstruction error. View aliasing artifacts are presented in 4D‐CBCT reconstructed by FDK, where the relative error is 46.2%. When TV minimization is used, the relative error is 19.8%. Image quality of 4D‐CBCT is substantially improved by using the proposed SMEIR algorithm and relative error is reduced to 7.6%. Conclusion: The proposed SMEIR algorithm is able to estimate motion model and reconstruct motion‐compensated 4D‐CBCT with high accuracy.