SIRF: Synergistic Image Reconstruction Framework

Ovtchinnikov, Evgueni; Brown, Richard; Kolbitsch, Christoph; Pasca, E.; Costa-Luis, Casper da; Gillman, Ashley; Thomas, Benjamin A.; Efthimiou, Nikos; Mayer, Johannes; Wadhwa, Palak; Ehrhardt, Matthias J.; Ellis, Sam; Jørgensen, Jakob Sauer; Matthews, Julian C.; Prieto, Claudia; Reader, Andrew J.; Tsoumpas, Charalampos; Turner, Martin; Atkinson, David; Thielemans, Kris

doi:10.1016/j.cpc.2019.107087

Cited by 47 publications

(29 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The draft PET BIDS standard does not yet extend to listmode data [37], so we applied our BIDS-like standard [21] to ensure that it is consistent with the Interoperable principle of the FAIR philosophy. We [21] have previously demonstrated that listmode fPET data can be accurately reconstructed using open source methods STIR [38] and SIRF [39], confirming the Monash DaCRA fPET-fMRI dataset is also consistent with the Reusable principle of the FAIR philosophy.…”

Section: Concluding Remarks and Re-use Potentialsupporting

confidence: 61%

Monash DaCRA fPET-fMRI: A DAtaset for Comparison of Radiotracer Administration for high temporal resolution functional FDG-PET

Jamadar

Liang

Zhong

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Functional [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the two primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. Findings: In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts three radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n=5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The Bolus group received the full FDG dose in a standard bolus administration; the Infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the Bolus-Infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. Conclusions: The Monash DaCRA fPET-fMRI dataset provides significant re-use value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.

show abstract

Section: Concluding Remarks and Re-use Potentialsupporting

confidence: 61%

Monash DaCRA fPET-fMRI: A DAtaset for Comparison of Radiotracer Administration for high temporal resolution functional FDG-PET

Jamadar

Liang

Zhong

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A practical difficulty with synergistic methods, especially in multi-modality imaging, is the need for software that can handle large amounts of data, is capable of accurately computing the system models (2.3), and ideally allows easy experimentation with novel algorithms. It is therefore often necessary to combine several software packages, ideally via an overarching framework [132] or by writing interfaces to other packages such that they can be used in an optimization library such as [133][134][135].…”

Section: Discussion and Outlookmentioning

confidence: 99%

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

Arridge

Ehrhardt

Thielemans

2021

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

show abstract

“…We used Software for Tomographic Image Reconstruction (STIR) (v.4.0) [38], [39] for the reconstruction of the simulated data, using TOF Listmode Maximum Likelihood-Expectation Maximisation (LM-MLEM) as it is the most robust option and is guaranteed to converge to a solution [40]. The validation of the TOF reconstruction with Gaussian [40]- [42] and non-Gaussian [43] kernels has been presented in detail previously.…”

Section: Image Reconstruction Toolkitmentioning

confidence: 99%

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Efthimiou

Kratochwil

Gundacker

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

Self Cite

View full text Add to dashboard Cite

Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO–Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO–Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO–Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

show abstract

SIRF: Synergistic Image Reconstruction Framework

Cited by 47 publications

References 51 publications

Monash DaCRA fPET-fMRI: A DAtaset for Comparison of Radiotracer Administration for high temporal resolution functional FDG-PET

Monash DaCRA fPET-fMRI: A DAtaset for Comparison of Radiotracer Administration for high temporal resolution functional FDG-PET

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Contact Info

Product

Resources

About