A Biomechanical Modeling Guided CBCT Estimation Technique

You, Zai-Jin; Tehrani, Joubin Nasehi; Wang, Jing

doi:10.1109/tmi.2016.2623745

Cited by 30 publications

(51 citation statements)

References 53 publications

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“…For example, there may be other methods for synthetic CT generation with or without deformable registration, which can be further explored, as discussed above. For very low contrast tumors, the MMFD algorithm can be replaced by a finite element analysis (FEA)‐based biomechanical modeling to deform the low‐contrast regions within an organ based on surface matching of the organ . Future studies are warranted to fully evaluate the effect of tumor contrast.…”

Section: Discussionmentioning

confidence: 99%

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A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

et al. 2018

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Purpose: On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on-board kV imaging system and patient prior knowledge to generate on-board four-dimensional (4D)-MRI for target localization in liver SBRT. Methods: Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRI prior ) and all other phases. MRI prior was used to generate a synthetic CT at EOE phase (sCT prior ). On-board 4D MRI at each respiratory phase was considered a deformation of MRI prior . The deformation field map (DFM) was estimated by matching DRRs of the deformed sCT prior to on-board kV projections using a motion modeling and free-form deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on-board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results: In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 AE 1.31%/ 0.95 AE 0.01/0.88 AE 0.15 mm using orthogonal-view 30°scan angles with 102 projections. The onboard 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the "reference on-board 4D MRI" for the liver patient case. Conclusions: A method was developed to generate on-board 4D MRI using prior 4D MRI and onboard limited kV projections. Preliminary results demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.

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Section: Discussionmentioning

confidence: 99%

“…For very low contrast tumors, the MMFD algorithm can be replaced by a finite element analysis (FEA)-based biomechanical modeling to deform the low-contrast regions within an organ based on surface matching of the organ. 35 Future studies are warranted to fully evaluate the effect of tumor contrast.…”

Section: Discussionmentioning

confidence: 99%

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

et al. 2018

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“…Another type of CBCT reconstruction technique tries to incorporate prior information into the reconstruction process, such as the 2D-3D deformation method [23][24][25][26][27][28]. Instead of directly reconstructing CBCT images from acquired projections, the 2D-3D deformation method views the new CBCT volume as a deformation of prior CT/CBCT images, and translates the CBCT reconstruction into a deformation-vector-field (DVF) optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Huang

Wang

2019

Vis. Comput. Ind. Biomed. Art

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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 conebeam 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.

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“…In addition to the previously mentioned techniques, a new CT reconstruction approach was recently investigated . Instead of directly reconstructing the CT volume from acquired projections, the new approach estimates it by deforming a previously acquired high‐quality CT volume using a deformation field.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the previously mentioned techniques, a new CT reconstruction approach was recently investigated. [21][22][23][24][25][26][27][28][29][30] Instead of directly reconstructing the CT volume from acquired projections, the new approach estimates it by deforming a previously acquired high-quality CT volume using a deformation field. The image reconstruction thus turns into the optimization of the deformation field to match the acquired projections with the projected ones from the deformed CT volume, 28 which is a 2D-3D deformation process.…”

Section: Introductionmentioning

confidence: 99%

A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC)

Zhang

Iyengar

et al. 2017

Med. Phys.

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Purpose Sequential same-patient CT images may involve deformation-induced and non-deformation-induced voxel intensity changes. An adaptive deformation recovery and intensity correction (ADRIC) technique was developed to improve the CT reconstruction accuracy, and to separate deformation from non-deformation-induced voxel intensity changes between sequential CT images. Materials and Methods ADRIC views the new CT volume as a deformation of a prior high-quality CT volume, but with additional non-deformation-induced voxel intensity changes. ADRIC first applies the 2D-3D deformation technique to recover the deformation field between the prior CT volume and the new, to-be-reconstructed CT volume. Using the deformation-recovered new CT volume, ADRIC further corrects the non-deformation-induced voxel intensity changes with an updated algebraic reconstruction technique (‘ART-dTV’). The resulting intensity-corrected new CT volume is subsequently fed back into the 2D-3D deformation process to further correct the residual deformation errors, which forms an iterative loop. By ADRIC, the deformation field and the non-deformation voxel intensity corrections are optimized separately and alternately to reconstruct the final CT. CT myocardial perfusion imaging scenarios were employed to evaluate the efficacy of ADRIC, using both simulated data of the extended-cardiac-torso (XCAT) digital phantom and experimentally acquired porcine data. The reconstruction accuracy of the ADRIC technique was compared to the technique using ART-dTV alone, and to the technique using 2D-3D deformation alone. The relative error metric and the universal quality index metric are calculated between the images for quantitative analysis. The relative error is defined as the square root of the sum of squared voxel intensity differences between the reconstructed volume and the ‘ground-truth’ volume, normalized by the square root of the sum of squared ‘ground-truth’ voxel intensities. In addition to the XCAT and porcine studies, a physical lung phantom measurement study was also conducted. Water-filled balloons with various shapes/volumes and concentrations of iodinated contrasts were put inside the phantom to simulate both deformations and non-deformation-induced intensity changes for ADRIC reconstruction. The ADRIC-solved deformations and intensity changes from limited-view projections were compared to those of the ‘gold-standard’ volumes reconstructed from fully-sampled projections. Results For the XCAT simulation study, the relative errors of the reconstructed CT volume by the 2D-3D deformation technique, the ART-dTV technique and the ADRIC technique were 14.64%, 19.21% and 11.90% respectively, by using 20 projections for reconstruction. Using 60 projections for reconstruction reduced the relative errors to 12.33%, 11.04% and 7.92% for the three techniques, respectively. For the porcine study, the corresponding results were 13.61%, 8.78%, 6.80% by using 20 projections; and 12.14%, 6.91% and 5.29% by using 60 projections. The ADRIC technique also dem...

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A Biomechanical Modeling Guided CBCT Estimation Technique

Cited by 30 publications

References 53 publications

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

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

A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC)

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