Joint Activity and Attenuation Reconstruction From Multiple Energy Window Data With Photopeak Scatter Re-Estimation in Non-TOF 3-D PET

Brusaferri, Ludovica; Bousse, Alexandre; Emond, Elise; Brown, Richard; Tsai, Yi‐Fang; Atkinson, David; Ourselin, Sébastien; Watson, Charles C.; Hutton, Brian F.; Arridge, Simon; Thielemans, Kris

doi:10.1109/trpms.2020.2978449

Cited by 12 publications

(18 citation statements)

References 44 publications

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“…Moreover, an overlooked aspect, which has not been considered as an input to the networks, is the distribution of scattered photons that is closely related with μ-map. Given that the attenuation in PET is mainly due to Compton scattering and several studies are showing the usefulness of the scattered coincidence measurements for improving the PET AC [226][227][228][229], the DL-based scatter-to-attenuation approach looks promising. AC is also important for SPECT because of the increasing use of theranostic agents that emit single gamma ray photons [5,[230][231][232].…”

Section: Deep Learning: Future Directionmentioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Attenuation correction (AC) is essential for the generation of artifact-free and quantitaAtively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuationcorrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high energy photon attenuation. Deep learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT-and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.

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Section: Deep Learning: Future Directionmentioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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“…We finally present results from GATE Monte Carlo simulated data and provide a comparison of the proposed method against MLAA from a single energy window acquisition. Similar to previous published work [15], [19], the current study is restricted to single scatter only.…”

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confidence: 90%

“…Several authors have attempted to overcome cross-talk in non-TOF PET by exploiting the energy information contained in multi-energy window acquired data. This was first investigated in Single Photon Emission Computed Tomography (SPECT) [11], [12] and then extended to the case of PET [13]- [15]. However, these studies were conducted based on detection efficiency models ignoring detector scatter and neglecting the presence of true events in the low-energy windows.…”

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confidence: 99%

Detection Efficiency Modeling and Joint Activity and Attenuation Reconstruction in Non-TOF 3-D PET From Multiple-Energy Window Data

Brusaferri

Emond

Bousse

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Emission-based attenuation correction (AC) methods offer the possibility of overcoming quantification errors induced by conventional MR-based approaches in PET/MR imaging. However, the joint problem of determining AC and the activity of interest is strongly ill-posed in non-TOF PET. This can be improved by exploiting the extra information arising from low energy window photons, but the feasibility of this approach has only been studied with relatively simplistic analytic simulations so far. This manuscript aims to address some of the remaining challenges needed to handle realistic measurements; in particular, the detection efficiency ("normalisation") estimation for each energy window is investigated. An energy-dependent detection efficiency model is proposed, accounting for the presence of unscattered events in the lower energy window due to detector scatter. Geometric calibration factors are estimated prior to the reconstruction for both scattered and unscattered events. Different reconstruction methods are also compared. Results show that geometric factors differ markedly between the energy windows and that our analytical model correspond in good approximation to Monte Carlo simulation; the multiple energy window reconstruction appears sensitive to input/model mismatch. Our method applies to Monte Carlo generated data but can be extended to measured data. This study is restricted to single scatter events.

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“…An example is the reconstruction of both attenuation and tracer concentration from detection of both unscattered and scattered photons, the latter having reduced energy. This has been demonstrated in both SPECT [29–31] and PET [32–34]. MCMI: Multi-Time Imaging.…”

Section: A Taxonomy Of Problemsmentioning

confidence: 99%

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

Arridge

Ehrhardt

Thielemans

2021

Phil. Trans. R. Soc. A.

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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’.

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Joint Activity and Attenuation Reconstruction From Multiple Energy Window Data With Photopeak Scatter Re-Estimation in Non-TOF 3-D PET

Cited by 12 publications

References 44 publications

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Detection Efficiency Modeling and Joint Activity and Attenuation Reconstruction in Non-TOF 3-D PET From Multiple-Energy Window Data

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

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