Image reconstruction from sparse data in synchrotron-radiation-based microtomography

Xia, Dan; Xiao, Xianghui; Bian, Junguo; Han, Xiao; Sidky, Emil Y.; Carlo, Francesco De; Pan, Xiaochuan

doi:10.1063/1.3572263

Cited by 10 publications

(13 citation statements)

References 29 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[35] to reconstruct images by solving numerically the constrained-TV-minimization program, because they appear to perform robustly well in past and current studies [36, 37, 38, 39, 40, 41]. Different appropriately selected algorithms yield only different paths converging to the designed solutions.…”

Section: Determination Of Reconstruction Parametersmentioning

confidence: 99%

“…Among the numerous optimization-based reconstructions proposed for CBCT, the constrained-total-variation (TV)-minimization-based reconstruction [20, 35] has been shown of some potential for imaging conditions of practical interest [36, 37, 38]. In general, the complete specification of an optimization-based reconstruction involves, in addition to its mathematical formulation, a number of parameters [35, 39], which can significantly impact reconstruction properties (or utility).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

et al. 2015

View full text Add to dashboard Cite

Kilo-voltage (KV) cone-beam computed tomography (CBCT) unit mounted onto a linear accelerator treatment system, often referred to as on-board imager (OBI), plays an increasingly important role in image-guide radiation therapy. While the FDK algorithm is used currently for reconstructing images from clinical OBI data, optimization-based reconstruction has also been investigated for OBI CBCT. An optimization-based reconstruction involves numerous parameters, which can significantly impact reconstruction properties (or utility). The success of an optimization-based reconstruction for a particular class of practical applications thus relies strongly on appropriate selection of parameter values. In the work, we focus on tailoring the constrained-TV-minimization-based reconstruction, an optimization-based reconstruction previously shown of some potential for CBCT imaging conditions of practical interest, to OBI imaging through appropriate selection of parameter values. In particular, for given real data of phantoms and patient collected with OBI CBCT, we first devise utility metrics specific to OBI-quality-assurance tasks and then apply them to guiding the selection of parameter values in constrained-TV-minimization-based reconstruction. The study results show that the reconstructions are with improvement, relative to clinical FDK reconstruction, in both visualization and quantitative assessments in terms of the devised utility metrics.

show abstract

Section: Determination Of Reconstruction Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

et al. 2015

View full text Add to dashboard Cite

show abstract

“…(Xia et al , 2011; Han et al , 2011; Nett et al , 2010; Sidky et al , 2011; Ouyang et al , 2011; Choi et al , 2010; Ye et al , 2011) As compared to the conventional filtered-backprojection (FBP) algorithms, iterative reconstruction shows advantages on image artifact reduction when projection data are heavily undersampled or highly noisy (Han et al , 2012; Bian et al , 2010). In this paper, we propose an improved implementation of an iterative CT reconstruction algorithm that was recently developed in our group (Niu and Zhu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence

Niu¹,

Ye²,

Fruhauf³

et al. 2014

Phys. Med. Biol.

View full text Add to dashboard Cite

Recently, we proposed a new algorithm of accelerated barrier optimization compressed sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS uses gradient projection (GP) with a Barzilai-Borwein (BB) step-size selection scheme (GP-BB) to search for the optimal solution. The algorithm does not converge stably due to its non-monotonic behavior. In this paper, we further improve the convergence of ABOCS using the unknown-parameter Nesterov (UPN) method and investigate the ABOCS reconstruction performance on clinical patient data. Comparison studies are carried out on reconstructions of computer simulation, a physical phantom and a head-and-neck patient. In all of these studies, the ABOCS results using UPN show more stable and faster convergence than those of the GPBB method and a state-of-the-art Bregman-type method. As shown in the simulation study of the Shepp-Logan phantom, UPN achieves the same image quality as those of GPBB and the Bregman-type method, but reduces the iteration numbers by up to 50% and 90%, respectively. In the Catphan©600 phantom study, a high-quality image with relative reconstruction error (RRE) less than 3% compared to the full-view result is obtained using UPN with 17% projections (60 views). In the conventional filtered-backprojection (FBP) reconstruction, the corresponding RRE is more than 15% on the same projection data. The superior performance of ABOCS with the UPN implementation is further demonstrated on the head-and-neck patient. Using 25% projections (91 views), the proposed method reduces the RRE from 21% as in the FBP results to 7.3%. In conclusion, we propose UPN for ABOCS implementation. As compared to GPBB and the Bregman-type methods, the new method significantly improves the convergence with higher stability and less iterations.

show abstract

“…(Xia et al, 2011;Han et al, 2011;Nett et al, 2010;Sidky et al, 2011;Ouyang et al, 2011;Choi et al, 2010;Ye et al, 2011) As compared to the conventional filteredbackprojection (FBP) algorithms, iterative reconstruction shows advantages on image artifact reduction when projection data are heavily undersampled or highly noisy (Han et al, 2012;Bian et al, 2010). In this paper, we propose an improved implementation of an iterative CT reconstruction algorithm that was recently developed in our group .…”

Section: Introductionmentioning

confidence: 99%

Accelerated Barrier Optimization Compressed Sensing (ABOCS) for CT Reconstruction With Improved Convergence

Niu

Zhu

2013

International Journal of Radiation Oncology*Biology*Physics

View full text Add to dashboard Cite

Recently, we proposed a new algorithm of accelerated barrier optimization compressed sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS uses gradient projection (GP) with a Barzilai-Borwein (BB) step-size selection scheme (GP-BB) to search for the optimal solution. The algorithm does not converge stably due to its non-monotonic behavior. In this paper, we further improve the convergence of ABOCS using the unknown-parameter Nesterov (UPN) method and investigate the ABOCS reconstruction performance on clinical patient data. Comparison studies are carried out on reconstructions of computer simulation, a physical phantom and a head-and-neck patient. In all of these studies, the ABOCS results using UPN show more stable and faster convergence than those of the GPBB method and a state-of-theart Bregman-type method. As shown in the simulation study of the Shepp-Logan phantom, UPN achieves the same image quality as those of GPBB and the Bregman-type method, but reduces the iteration numbers by up to 50% and 90%, respectively. In the Catphan©600 phantom study, a high-quality image with relative reconstruction error (RRE) less than 3% compared to the fullview result is obtained using UPN with 17% projections (60 views). In the conventional filteredbackprojection (FBP) reconstruction, the corresponding RRE is more than 15% on the same projection data. The superior performance of ABOCS with the UPN implementation is further demonstrated on the head-and-neck patient. Using 25% projections (91 views), the proposed method reduces the RRE from 21% as in the FBP results to 7.3%. In conclusion, we propose UPN for ABOCS implementation. As compared to GPBB and the Bregman-type methods, the new method significantly improves the convergence with higher stability and less iterations.

show abstract

Image reconstruction from sparse data in synchrotron-radiation-based microtomography

Cited by 10 publications

References 29 publications

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence

Accelerated Barrier Optimization Compressed Sensing (ABOCS) for CT Reconstruction With Improved Convergence

Contact Info

Product

Resources

About