Evaluation of Frequency-Distance Relation Validity for FORE Optimization in 3-D PET

Bernardi, Elisabetta De; Mazzoli, Marco; Zito, Felicia; Baselli, Giuseppe

doi:10.1109/tns.2007.905175

Cited by 3 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sinograms were corrected for random but not for scatter coincidences (Pönisch et al 2003). The time-of-flight localization was not taken into account and the 3D sinogram was rebinned according to the Fourier Rebinning (FORE) algorithm (Defrise et al 1997, Matej et al 1998,De Bernardi et al 2007. Hence, a slice-by-slice reconstruction was enabled.…”

Section: Real Acquisitions: the Physical Phantom And The Patient Studymentioning

confidence: 99%

Regional MLEM reconstruction strategy for PET-based treatment verification in ion beam radiotherapy

et al. 2014

Self Cite

View full text Add to dashboard Cite

In ion beam radiotherapy, PET-based treatment verification provides a consistency check of the delivered treatment with respect to a simulation based on the treatment planning. In this work the region-based MLEM reconstruction algorithm is proposed as a new evaluation strategy in PET-based treatment verification. The comparative evaluation is based on reconstructed PET images in selected regions, which are automatically identified on the expected PET images according to homogeneity in activity values. The strategy was tested on numerical and physical phantoms, simulating mismatches between the planned and measured β+ activity distributions. The region-based MLEM reconstruction was demonstrated to be robust against noise and the sensitivity of the strategy results were comparable to three voxel units, corresponding to 6 mm in numerical phantoms. The robustness of the region-based MLEM evaluation outperformed the voxel-based strategies. The potential of the proposed strategy was also retrospectively assessed on patient data and further clinical validation is envisioned.

show abstract

Section: Real Acquisitions: the Physical Phantom And The Patient Studymentioning

confidence: 99%

Regional MLEM reconstruction strategy for PET-based treatment verification in ion beam radiotherapy

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…This rebinning algorithm is based on the frequency-distance relationship (Champley et al 2008, Bernardi et al 2007). The planogram coordinate system is a native coordinate system for PET scanners with parallel planar detectors.…”

Section: Introductionmentioning

confidence: 99%

“…The content of this paper is concerned with further advancements to the planogram frequency-distance rebinning (PFDR) algorithm (Champley et al 2008) which is an approximate rebinning algorithm derived in the planogram coordinate system. This rebinning algorithm is based on the frequency-distance relationship (Champley et al 2008, Bernardi et al 2007. The planogram coordinate system is a native coordinate system for PET scanners with parallel planar detectors.…”

Section: Introductionmentioning

confidence: 99%

Advancements to the planogram frequency–distance rebinning algorithm

Champley¹,

Raylman²,

Kinahan³

2010

Inverse Problems

View full text Add to dashboard Cite

In this paper we consider the task of image reconstruction in positron emission tomography (PET) with the planogram frequency–distance rebinning (PFDR) algorithm. The PFDR algorithm is a rebinning algorithm for PET systems with panel detectors. The algorithm is derived in the planogram coordinate system which is a native data format for PET systems with panel detectors. A rebinning algorithm averages over the redundant four-dimensional set of PET data to produce a three-dimensional set of data. Images can be reconstructed from this rebinned three-dimensional set of data. This process enables one to reconstruct PET images more quickly than reconstructing directly from the four-dimensional PET data. The PFDR algorithm is an approximate rebinning algorithm. We show that implementing the PFDR algorithm followed by the (ramp) filtered backprojection (FBP) algorithm in linogram coordinates from multiple views reconstructs a filtered version of our image. We develop an explicit formula for this filter which can be used to achieve exact reconstruction by means of a modified FBP algorithm applied to the stack of rebinned linograms and can also be used to quantify the errors introduced by the PFDR algorithm. This filter is similar to the filter in the planogram filtered backprojection algorithm derived by Brasse et al. The planogram filtered backprojection and exact reconstruction with the PFDR algorithm require complete projections which can be completed with a reprojection algorithm. The PFDR algorithm is similar to the rebinning algorithm developed by Kao et al. By expressing the PFDR algorithm in detector coordinates, we provide a comparative analysis between the two algorithms. Numerical experiments using both simulated data and measured data from a positron emission mammography/tomography (PEM/PET) system are performed. Images are reconstructed by PFDR+FBP (PFDR followed by 2D FBP reconstruction), PFDRX (PFDR followed by the modified FBP algorithm for exact reconstruction) and planogram filtered backprojection image reconstruction algorithms. We show that the PFDRX algorithm produces images that are nearly as accurate as images reconstructed with the planogram filtered backprojection algorithm and more accurate than images reconstructed with the PFDR+FBP algorithm. Both the PFDR+FBP and PFDRX algorithms provide a dramatic improvement in computation time over the planogram filtered backprojection algorithm.

show abstract