Real-Time Image Reconstruction for Time-of-Flight Positron Emission Tomography (TOFPET)

Philippe, E. A.; Mullani, Nizar A.; Wong, Wai-Hoi; Hartz, Ross K.

doi:10.1109/tns.1982.4335900

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…Early TOF image reconstruction methods were analytical algorithms that made use of the TOF information, including the most likely position (MLP) [2] and confidence-weighted (CW) back-projection [2,4,97,98] algorithms. Subsequently, the iterative MLEM algorithm was adapted to include the TOF probability response function with 2D list-mode data, yielding superior image quality [32,99].…”

Section: Tof Pet Image Reconstructionmentioning

confidence: 99%

Advances in time-of-flight PET

2016

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This paper provides a review and an update on time-of-flight PET imaging with a focus on PET instrumentation, ranging from hardware design to software algorithms. We first present a short introduction to PET, followed by a description of TOF PET imaging and its history from the early days. Next, we introduce the current state-of-art in TOF PET technology and briefly summarize the benefits of TOF PET imaging. This is followed by a discussion of the various technological advancements in hardware (scintillators, photo-sensors, electronics) and software (image reconstruction) that have led to the current widespread use of TOF PET technology, and future developments that have the potential for further improvements in the TOF imaging performance. We conclude with a discussion of some new research areas that have opened up in PET imaging as a result of having good system timing resolution, ranging from new algorithms for attenuation correction, through efficient system calibration techniques, to potential for new PET system designs.

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Section: Tof Pet Image Reconstructionmentioning

confidence: 99%

Advances in time-of-flight PET

2016

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“…We have found that annular background ROIs drawn around the "real" spheres and spherical VOIs drawn on the image without inserted spheres have comparable average values and spreads. The CRC was calculated as (5) where H is the average value in the sphere VOI, B is the average value in the background region, and a is the true sphere:background activity ratio. Perfect contrast recovery would have a CRC of 1.0.…”

Section: E Contrast Recovery Coefficient Calculationmentioning

confidence: 99%

Comparison of list-mode and DIRECT approaches for time-of-flight PET reconstruction

Daube-Witherspoon

Matej

Werner

et al. 2010

IEEE Nuclear Science Symposuim &Amp; Medical Imaging Conference

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Early clinical results with time-of-flight (TOF) PET systems have demonstrated the advantages of TOF information in PET reconstruction. Reconstruction approaches in TOF-PET systems include list-mode and binned iterative algorithms as well as confidence-weighted analytic methods. Listmode iterative TOF reconstruction retains the resolutions of the data in the spatial and temporal domains without any binning approximations but is computationally intensive. We have developed an approach [DIRECT (direct image reconstruction for TOF)] to speed up TOF-PET reconstruction that takes advantage of the reduced angular sampling requirement of TOF data by grouping list-mode data into a small number of azimuthal views and co-polar tilts and depositing the grouped events into histo-images, arrays with the sampling and geometry of the final image. All physical effects are included in the system model and deposited in the same histo-image structure. Using histo-images allows efficient computation during reconstruction without raytracing or interpolation operations. The DIRECT approach was compared with 3D list-mode TOF ordered subsets expectation maximization (OSEM) reconstruction for phantom and patient data taken on the University of Pennsylvania research LaBr 3 TOF-PET scanner. The total processing and reconstruction time for these studies with DIRECT without attention to code optimization is approximately 25-30% that of list-mode TOF-OSEM to achieve comparable image quality. Furthermore, the reconstruction time for DIRECT is independent of the number of events and/or sizes of the spatial and TOF kernels, while the time for list-mode TOFOSEM increases with more events or larger kernels. The DIRECT approach is able to reproduce the image quality of listmode iterative TOF reconstruction both qualitatively and quantitatively in measured data with a reduced time.

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“…It is worth noting that an updating procedure using (23) can also be implemented using column vectors representing filter functions derived from the FBP algorithm, as was proposed previously [7][8][9][10][11]. Images reconstructed by this technique would not incorporate accurate physical modeling of the imaging system and would suffer from the same artefacts as the standard FBP reconstructed images with low statistics data.…”

Section: Basic Algorithmmentioning

confidence: 99%

“…Previous attempts at reconstructing tomographic data in real-time were based on the principle of superposition of filter functions for individual events derived from the filtered-backprojection (FBP) or a similar algorithm. Such reconstruction techniques were considered for conventional two-dimensional (2D) image reconstruction [7], time-of-flight positron emission tomography [8], and 3D PET [9][10][11]. These implementations required special dedicated processors to cope with the computational load of backprojection onto a grid of voxels online [12][13][14].…”

Section: Introductionmentioning

confidence: 99%