Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐<scp>CBCT</scp>) using groupwise deformable registration

Riblett, Matthew J.; Christensen, Gary E.; Weiss, Elisabeth; Hugo, Geoffrey D.

doi:10.1002/mp.13133

Cited by 28 publications

(39 citation statements)

References 32 publications

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“…Using this DVF, a FDK reconstruction is performed but with the backprojected traces deformed to correct for respiratory motion. MA‐ROOSTER: the motion‐aware spatial and temporal regularization reconstruction implemented by Dr Cyril Mory from the CREATIS laboratory. The reconstruction is solved iteratively by enforcing spatial smoothness as well as temporal smoothness along a warped trajectory according to the prior DVF built from the pretreatment 4D‐CT. MoCo: the data‐driven motion‐compensated method implemented by Dr Matthew Riblett from the Virginia Commonwealth University and Prof Geoffrey Hugo from the Washington University. The motion‐compensation DVF is built using groupwise deformable image registration of a preliminary 4D‐CBCT reconstruction computed by the PICCS method. MC‐PICCS: the motion‐compensated (MC) prior image constrained compressed sensing (PICCS) reconstruction implemented by Dr Chun‐Chien Shieh from the University of Sydney.…”

Section: Resultsmentioning

confidence: 99%

“…The reconstruction is solved iteratively by enforcing spatial smoothness as well as temporal smoothness along a warped trajectory according to the prior DVF built from the pretreatment 4D-CT. 3. MoCo: the data-driven motion-compensated method 33 implemented by Dr Matthew Riblett from the Virginia Commonwealth University and Prof Geoffrey Hugo from the Washington University. The motion-compensation DVF is built using groupwise deformable image registration of a preliminary 4D-CBCT reconstruction computed by the PICCS method.…”

Section: A Participantsmentioning

confidence: 99%

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SPARE: Sparse‐view reconstruction challenge for 4D cone‐beam CT from a 1‐min scan

Shieh

González

et al. 2019

Medical Physics

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Purpose Currently, four‐dimensional (4D) cone‐beam computed tomography (CBCT) requires a 3–4 min full‐fan scan to ensure usable image quality. Recent advancements in sparse‐view 4D‐CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Using this framework, the AAPM‐sponsored SPARE Challenge was conducted in 2018 to identify and compare state‐of‐the‐art algorithms. Methods A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D‐Lung database. The selected patients had multiple 4D‐CT sessions, where the first 4D‐CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU‐based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root‐mean‐squared‐error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region‐of‐interests (ROI) — patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV. Results Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion‐compensation, and deformation of the prior 4D‐CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp–Davis–Kress (FDK) algorithm. The RMS of the three‐dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA‐ROOSTER method, which utilizes a 4D‐CT motion model for temporal regularization, had the best and most consistent image quality and accuracy. Conclusion The SPARE Challenge represents the first framework for systematically evaluating state‐of‐the‐art algorithms for 4D‐CBCT reconstruction from a 1‐min scan. Results suggest the potential for reducing scan time and dose for 4D‐CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: A Participantsmentioning

confidence: 99%

SPARE: Sparse‐view reconstruction challenge for 4D cone‐beam CT from a 1‐min scan

Shieh

González

et al. 2019

Medical Physics

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“…It is interesting to note that we used pairwise registration methods in our DIR, which need of the registration of an image to a reference image among the same set. Groupwise registration methods, on the contrary, simultaneously register all images of a 4D set to a common reference frame, thus minimizing the influence of artifacts on any particular image representing the patient at any specific time on the final outcome . Registration is thus expected to be more robust and accurate.…”

Section: Discussionmentioning

confidence: 99%

“…Registration is thus expected to be more robust and accurate. In the context of medical physics in radiation oncology, groupwise registration has been used to improve quality in 4D CT and 4D CBCT reconstruction . This new method for DIR in the context may potentially benefit the accuracy of 4D dose accumulation.…”

Section: Discussionmentioning

confidence: 99%

A novel concept to include uncertainties in the evaluation of stereotactic body radiation therapy after 4D dose accumulation using deformable image registration

et al. 2019

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Purpose To use four‐dimensional (4D) dose accumulation based on deformable image registration (DIR) to assess dosimetric uncertainty in lung stereotactic body radiation therapy (SBRT) treatment planning. A novel concept, the Evaluation Target Volume (ETV), was introduced to achieve this goal. Methods The internal target volume (ITV) approach was used for treatment planning for 11 patients receiving lung SBRT. Retrospectively, 4D dose calculation was done in Pinnacle v9.10. Total dose was accumulated in the reference phase using DIR with MIM. DIR was validated using landmarks introduced by an expert radiation oncologist. The 4D and three‐dimensional (3D) dose distributions were compared within the gross tumor volume (GTV) and the planning target volume (PTV) using the D95 and Dmin (calculated as Dmin,0.035cc) metrics. For lung involvement, the mean dose and V20, V10, and V5 were used in the 3D to 4D dose comparison, and Dmax (D0.1cc) was used for all other organs at risk (OAR). The new evaluation target volume (ETV) was calculated by expanding the GTV in the reference phase in order to include geometrical uncertainties of the DIR, interobserver variability in the definition of the tumor, and uncertainties of imaging and delivery systems. D95 and Dmin,0.035cc metrics were then calculated on the basis of the ETV for 4D accumulated dose distributions, and these metrics were compared with those calculated from the PTV for 3D planned dose distributions. Results The target registration error (TRE) per spatial component was below 0.5 ± 2.1mm for all our patients. For five patients, dose degradation above 2% (>4% in 2 patients) was found in the PTV after 4D accumulation and attributed to anatomical variations due to breathing. Comparison of D95 and Dmin,0.035cc metrics showed that the ETV (4D accumulated dose) estimated substantially lower coverage than the PTV (3D planning dose): in six out of the 11 cases, and for at least for one of the two metrics, coverage estimated by ETV was at least 5% lower than that estimated by PTV. Furthermore, the ETV approach revealed hot and cold spots within its boundaries. Conclusions A workflow for 4D dose accumulation based on DIR has been devised. Dose degradation was attributed to respiratory motion. To overcome limitations in the PTV for the purposes of evaluating DIR‐based 4D accumulated dose distributions, a new concept, the ETV, was proposed. This concept appears to facilitate more reliable dose evaluation and a better understanding of dosimetric uncertainties due to motion and deformation.

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“…Motion during treatment remains a key challenge for online ART, both in achieving highquality in-room imaging and for ensuring adapted plans are relevant to the anatomy during delivery. Four-dimensional imaging solutions for in-room cone beam CT [62][63][64][65] and MRI 66 continue to evolve to improve image quality in motion-influenced sites, which is critical to move ART from clearly-defined tumors to more challenging stages of disease and sites where it may be critically needed, such as locally-advanced lung cancer. 2,67 Merging advances in realtime ART, such as motion tracking, with efficient pre-treatment online ART tools may help take advantage of the merits of both approaches for managing anatomical changes on multiple timescales.…”

Section: Open Questions and Future Directionsmentioning

confidence: 99%

Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

Green

Henke

Hugo

2019

Seminars in Radiation Oncology

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118

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Adaptive radiotherapy emerged over 20 years ago and is now an established clinical practice in a number of organ sites. No one solution for adaptive therapy exists. Rather, adaptive radiotherapy is a process which combines multiple tools for imaging, assessment of need for adaptation, treatment planning, and quality assurance of this process. Workflow is therefore a critical aspect to ensure safe, effective, and efficient implementation of adaptive radiotherapy. In this work, we discuss the tools for online and offline adaptive radiotherapy and introduce workflow concepts for these types of adaptive radiotherapy. Common themes and differences between the workflows are introduced and controversies and areas of active research are discussed.

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Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐CBCT) using groupwise deformable registration

Cited by 28 publications

References 32 publications

SPARE: Sparse‐view reconstruction challenge for 4D cone‐beam CT from a 1‐min scan

SPARE: Sparse‐view reconstruction challenge for 4D cone‐beam CT from a 1‐min scan

A novel concept to include uncertainties in the evaluation of stereotactic body radiation therapy after 4D dose accumulation using deformable image registration

Practical Clinical Workflows for Online and Offline Adaptive Radiation Therapy

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