Total variation regulated EM algorithm [SPECT reconstruction]

Panin, Vladimir; Zeng, G.L.; Gullberg, Grant T.

doi:10.1109/23.819305

Cited by 163 publications

(77 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the possibilities are surrogate functions that are computationally easy to minimize [20]; a hybrid approach where the data fidelity term and the penalty term are minimized alternatively [17]- [19], [21]; or the one-step-late algorithm [15], [16], [27].…”

Section: B Image Reconstruction: Total Variation 1) Map-em and Totalmentioning

confidence: 99%

PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring

Cabello

Torres-Espallardó

Gillam

et al. 2013

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

Hadron therapy exploits the properties of ion beams to treat tumors by maximizing the dose released to the target and sparing healthy tissue. With hadron beams, the dose distribution shows a relatively low entrance dose which rises sharply at the end of the range, providing the characteristic Bragg peak that drops quickly thereafter. It is of critical importance in order not to damage surrounding healthy tissues and/or avoid targeting underdosage to know where the delivered dose profile ends-the location of the Bragg peak. During hadron therapy, short-lived -emitters are produced along the beam path, their distribution being correlated with the delivered dose. Following positron annihilation, two photons are emitted, which can be detected using a positron emission tomography (PET) scanner. The low yield of emitters, their short half-life, and the wash out from the target region make the use of PET, even only a few minutes after hadron irradiation, a challenging application. In-beam PET represents a potential candidate to estimate the distribution of -emitters during or immediately after irradiation, at the cost of truncation effects and degraded image quality due to the partial rings required of the PET scanner. Time-of-flight (ToF) information can potentially be used to compensate for truncation effects and to enhance image contrast. However, the highly demanding timing performance required in ToF-PET makes this option costly. Alternatively, the use of maximum-a-posteriori-expectation-maximization (MAP-EM), including total variation (TV) in the cost function, produces images with low noise, while preserving spatial resolution. In this paper, we compare data reconstructed with maximum-likelihood-expectation-maximization (ML-EM) and MAP-EM using TV as prior, and the impact of including ToF information, from data acquired with a complete and a partial-ring PET scanner, of simulated hadron beams interacting Manuscript with a polymethyl methacrylate (PMMA) target. The results show that MAP-EM, in the absence of ToF information, produces lower noise images and more similar data compared to the simulated distributions than ML-EM with ToF information in the order of 200-600 ps. The investigation is extended to the combination of MAP-EM and ToF information to study the limit of performance using both approaches.Index Terms-Hadron therapy, in-beam PET, maximuma-posteriori-expectation-maximization (MAP-EM) reconstruction, time-of-flight positron emission tomography (PET), total variation.

show abstract

Section: B Image Reconstruction: Total Variation 1) Map-em and Totalmentioning

confidence: 99%

PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring

Cabello

Torres-Espallardó

Gillam

et al. 2013

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

show abstract

“…The traditional regularization term usually produces unfavorable smoothing effects on edge regions. To address this issue, several edge-preserving regularization terms have been presented, such as total variation (TV) regularization with a piecewise constant assumption (PCA) 15–17 . Numerous investigations have shown that high-quality CT images can be reconstructed via TV minimization from sparse-view sampling data.…”

Section: Introductionmentioning

confidence: 99%

An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction

Gao

Zhang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Because radiation is harmful to patients, it is important to reduce X-ray exposure in the clinic. For CT, reconstructions from sparse views or limited angle tomography are being used more frequently for low dose imaging. However, insufficient sampling data causes severe streak artifacts in images reconstructed using conventional methods. To solve this issue, various methods have recently been developed. In this paper, we improve a statistical iterative algorithm based on the minimization of the image total variation (TV) for sparse or limited projection views during CT image reconstruction. Considering the statistical nature of the projection data, the TV is performed under a penalized weighted least-squares (PWLS-TV) criterion. During implementation of the proposed method, the image reconstructed using the filtered back-projection (FBP) method is used as the initial value of the first iteration. Next, the feature refinement (FR) step is performed after each PWLS-TV iteration to extract the fine features lost in the TV minimization, which we refer to as ‘PWLS-TV-FR’.

show abstract

“…Reconstruction of the intermediate three‐dimensional activity distribution from a limited number of projections and a limited angular coverage is a challenging task. Iterative algorithms based on total variation minimization have been fairly successful for few‐view CT image reconstruction as well as for SPECT imaging . Total variation minimization encourages piecewise constant objects, while preserving edges .…”

Section: Discussionmentioning

confidence: 99%

Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

et al. 2017

View full text Add to dashboard Cite

show abstract

Total variation regulated EM algorithm [SPECT reconstruction]

Cited by 163 publications

References 16 publications

PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring

PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring

An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction

Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

Contact Info

Product

Resources

About