System models for PET statistical iterative reconstruction: A review

Iriarte, Ana; Marabini, Roberto; Matej, Samuel; Sorzano, C.O.S.; Lewitt, R.M.

doi:10.1016/j.compmedimag.2015.12.003

Cited by 42 publications

(45 citation statements)

References 172 publications

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“…It considers the main physical effects during PET acquisitions such as positron range [19], non-collinearity, nuclear decay of the isotope, etc. 18 F point sources (radius 0.5 mm) were simulated for a geometry similar to the Biograph TPTV (Siemens) scanner [58] to evaluate the axial resolution of the different rebinning strategies discussed. The image noise was evaluated with another simulation of two homogeneous cylinders.…”

Section: Simulated Datamentioning

confidence: 99%

“…Furthermore, in Figure 8, we present the results for a Derenzo filled with 250 µCi of 18 FDG measured with a well-counter with an accuracy of ±5% [11]. It can be seen as an improvement in resolution for 2D-PINV reconstruction when compared to FBP.…”

Section: Preclinical Datamentioning

confidence: 99%

“…Another disadvantage of analytical methods is that the resolution of the images is not uniform across the field of view (FOV), which affects quantification [17]. On other hand, iterative reconstructions provide better results than analytical methods [9,18], thanks to the incorporation in the reconstruction process of the physics effects involved in the emission, transport, and detection of PET radiation [14] such as positron range [19], non-collinearity, depth-of-interaction (DOI) in the crystals [13], geometrical considerations of the scanner [18,20], etc. However, iterative methods with realistic SRM are slow, and therefore, their application to real-time fully 3D PET imaging is still challenging [3,7].One of the most common approaches to speeding-up the reconstruction process involves introducing axial rebinning of the data, followed by 2D reconstructions [7,21,22].…”

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confidence: 99%

“…A more accurate alternative to SSRB is Fourier rebinning (FORE) [24], but unfortunately, it is much slower.Here, we propose the pseudoinverse (PINV) of the SRM [25] to achieve real-time reconstructions with uniform resolution [26]. The goal was to achieve some of the features of iterative methods with the computational cost of analytical reconstructions, thus providing fast reconstructions while incorporating the physics involved [18].…”

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confidence: 99%

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Real-Time 3D PET Image with Pseudoinverse Reconstruction

et al. 2020

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Real-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.

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Section: Simulated Datamentioning

confidence: 99%

Section: Preclinical Datamentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Real-Time 3D PET Image with Pseudoinverse Reconstruction

et al. 2020

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“…17,18 Advanced algorithms have significantly improved image quality with corrections for physical effects of attenuation, detector efficiency, detector geometric response, scatter, randoms, prompt gammas, dead time, and changes in TOF resolution as a function of measured detector count rate. 19 Acquisition of list-mode data temporally marks every event according to TOF position in the acquired data. Anatomical positions of heart tissue during the cardiac and respiratory cycle can be sorted by these timing marks for which TOF list-mode iterative maximum likelihood algorithms with correction for motion can accurately reconstruct the position of events, significantly improving cardiac image quality.…”

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confidence: 99%