A novel technique for markerless, self-sorted 4D-CBCT: Feasibility study

Vergalasova, Irina; Cai, Jing; Yin, Fang‐Fang

doi:10.1118/1.3685443

Cited by 46 publications

(63 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The error tolerance parameter ε we introduced in Eq. (11) can partially account for the mismatches. For a general solution, a calibration between the CT simulator and onboard imaging system can be performed.…”

Section: D Limitations Of the Current Studymentioning

confidence: 99%

A technique for estimating 4D‐CBCT using prior knowledge and limited‐angle projections

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

Section: D Limitations Of the Current Studymentioning

confidence: 99%

A technique for estimating 4D‐CBCT using prior knowledge and limited‐angle projections

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…To comprehensively evaluate the markerless technique, a simulation study was performed using the XCAT phantoms 101 . The phantoms were used to model different patient anatomies as well as variations in the respiratory motion (including changes in respiratory cycle duration, inspiration to expiration ratio, diaphragm motion amplitude, AP chest wall motion amplitude, and tumor and organ derived trajectories).…”

Section: Radiation Therapymentioning

confidence: 99%

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

Segars

Tsui

Cai

et al. 2018

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

The four dimensional (4D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT’s design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

show abstract

“…Vectors subscripted by the spatio-temporal dimensions relate to the minimization of the total variation according to definitions (6) and (8). Vectors denoted by subscript relate the minimization of the PCA regularization term, while vectors with subscript relate to the enforcement of the nonnegativity constraint.…”

Section: Appendix a Split-bregman And Conjugate Gradient Algorithmsmentioning

confidence: 99%

“…The g-FDK algorithm was implemented with a ramp (Ram-Lak) filter according to the outline in [9], [36]. The TV reconstruction was implemented as the solution to the constrained optimization problem (3) with spatial and temporal TV regularization defined according to (8). The Split-Bregman solver was applied as described in Appendix A.…”

Section: G Implementationmentioning

confidence: 99%

“…Many methods have been suggested to sort the acquired X-ray projections into respiratory phases corresponding to a given temporal resolution under an assumption of semi-periodic respiratory motion (see e.g., [5]- [8]). This preprocessing step exposes the fundamental reconstruction problem; After sorting the acquired projection data in a suitable number of respiratory phases (we use 10 temporal phases in this work), each phase lacks the sufficient number of projections to reconstruct the desired quality images with established techniques such as the Feldkamp, Davis, and Kress (FDK) approach [4]-even when gated adaptions (g-FDK) are utilized [9].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Registration-Based Reconstruction of Four-Dimensional Cone Beam Computed Tomography

Christoffersen

Hansen

Poulsen

et al. 2013

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

We present a new method for reconstruction of 4-D cone beam computed tomography from an undersampled set of X-ray projections. The novelty of the proposed method lies in utilizing optical flow based registration to facilitate that each temporal phase is reconstructed from the full set of acquired projections. The reconstruction of each phase thus exhibits limited aliasing despite significant intra-phase undersampling. The method is fully self-contained. Initially an approximate 4-D volume is reconstructed and an inter-phase registration based hereon. A subsequent reconstruction pass integrates the optical flow estimation in a cost function formulation in which the X-ray projections from all temporal phases are considered for the reconstruction of each individual phase. Quantitative and qualitative evaluations were performed through reconstruction of both a numerical phantom and a clinical dataset. The obtained reconstructions are compared to the state-of-the-art alternatives of total variation regularization and prior image constrained compressed sensing. Our studies show that the proposed method is the better overall "compromise" in the depiction of both moving and stationary anatomical structures.

show abstract

A novel technique for markerless, self-sorted 4D-CBCT: Feasibility study

Abstract: A novel technique employing the basics of Fourier transform theory was investigated and demonstrated to be feasible in achieving markerless, self-sorted 4D-CBCT reconstruction.

Cited by 46 publications

References 51 publications

A technique for estimating 4D‐CBCT using prior knowledge and limited‐angle projections

A technique for estimating 4D‐CBCT using prior knowledge and limited‐angle projections

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

Registration-Based Reconstruction of Four-Dimensional Cone Beam Computed Tomography

Contact Info

Product

Resources

About