Projection-based dynamic tomography

Jailin, Clément; Roux, Stéphane; Sarrut, David; Rit, Simon

doi:10.1088/1361-6560/ac309e

Cited by 13 publications

(15 citation statements)

References 75 publications

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“…The nominal cycle resolved by 4D-CBCT fails to capture the irregularity and non-periodicity, which may provide crucial information on motion statistics and trends to guide patient immobilization, set-up, and treatment monitoring (Poulsen et al 2014 , Li et al 2018 ). The ultimate solution to such a challenge is time-resolved CBCT imaging, or dynamic CBCT (Li et al 2010 , Cai et al 2014 , Gao et al 2018 , Jailin et al 2021 ). Dynamic CBCT, in contrast to the phase-resolved 4D-CBCT, reconstructs a continuous time series of volumetric images reflecting the spatial and temporal kinematics of patient anatomy without the phase-binning process.…”

Section: Introductionmentioning

confidence: 99%

“…The solved CBCT images are not fully time-resolved but are limited to 50 phases as well. Another study tried to solve dynamic CBCTs by combining projection-based motion estimation and motion-compensated reconstruction (Jailin et al 2021 ). The method models the time kinematics via a series of time functions including surrogate motion signals.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

et al. 2023

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Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, the dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach: We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results: STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting based neural representation method. STINR tracks the lung target to an averaged center-of-mass error of 1-2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance: STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

et al. 2023

View full text Add to dashboard Cite

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“…There have been other studies that attempt to produce similar results as we have in this paper, i.e. DVFs for every projection, that can include breath-to-breath variability, and a motion-free reconstruction, (Liu et al 2015, Jailin et al 2021. However, Liu et al (2015) only applied their method to simulated data from a simplified 2D simulation (i.e.…”

Section: Discussionmentioning

confidence: 94%

“…As far as we are aware this is the first time such a method has been applied to multiple real CBCT datasets. We believe our method is less complicated than those presented in (Liu et al 2015, Jailin et al 2021. The runtime for our method ranged from 30 to 120 min for the real CBCT scans on an Intel Core i7-10700K CPU.…”

Section: Discussionmentioning

confidence: 99%

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Huang,

Thielemans,

Price

et al. 2024

Phys. Med. Biol.

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Objective: As the most common solution to motion artefact for Cone-Beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan. Approach: Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.Results: For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans. Significance: This study demonstrated the feasibility of our proposed framework for simultaneous motion model fitting and motion compensated reconstruction using unsorted 3D CBCT projection data.

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“…Several studies were subsequently published using biomechanical modeling or deep learning (Zhang et al 2019) to improve the registration accuracy of SMEIR-based approaches. Additionally, many other groups developed similar approaches and alternatives to SMEIR with various implementations of simultaneous modeling and reconstruction (Liu et al 2015, Riblett et al 2018, Sauppe et al 2018, Jailin et al 2021, Mo et al 2021. However, these approaches had the same limitations.…”

Section: Introductionmentioning

confidence: 99%

Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

Lauria,

Miller,

Singhrao

et al. 2024

Phys. Med. Biol.

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Objective: To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the MC-SART was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT).Approach: For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT’s robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT.Main Results: We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality. Significance: This work is an important extension of the MC-SART technique. We demonstrated the ability of a priori 5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

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Projection-based dynamic tomography

Cited by 13 publications

References 75 publications

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study

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