Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Polycarpou, Irene; Soultanidis, Georgios; Tsoumpas, Charalampos

doi:10.1098/rsta.2020.0207

Cited by 15 publications

(13 citation statements)

References 112 publications

(157 reference statements)

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“…In addition, Kyme & Fulton (2021) review motion estimation and correction methods in PET, single-photon emission computed tomography (SPECT) and CT. Finally, Polycarpou et al (2021) provides an up-to-date review on the synergistic use of both PET and MR data for PET motion correction in simultaneous PET/MR.…”

Section: January 2021mentioning

confidence: 99%

PET respiratory motion correction: quo vadis?

Lamare¹,

Bousse²,

Thielemans³

et al. 2022

Phys. Med. Biol.

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Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

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Section: January 2021mentioning

confidence: 99%

PET respiratory motion correction: quo vadis?

Lamare¹,

Bousse²,

Thielemans³

et al. 2022

Phys. Med. Biol.

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“…The issue commences with a review article by Polycarpou et al [ 3 ] which summarizes various recently proposed motion compensation methods in PET-MRI. Physiological motion of the subject during scanning can affect the diagnostic value of PET-MRI substantially.…”

Section: Contributions In the Second Issuementioning

confidence: 99%

Synergistic tomographic image reconstruction: part 2

Tsoumpas

Jørgensen

Kolbitsch

et al. 2021

Phil. Trans. R. Soc. A.

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This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

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“…To overcome this problem, motion estimation has been a subject of significant research interest in a range of imaging modalities [8,9,10,11,12]. Several computational techniques for acquisitions and correction of motion and motion-corrected image reconstruction have been developed in parallel to the advancement of hardware [13,14,15,16,17,18,19]. Image-based motion synchronization and detection has been studied in [20,21,22], where filtering is used to separate respiratory and cardiac signals in PET. In this paper we consider examples in dynamic MRI, where both deliberate imaging dynamics such as morphological motion or contrast inflow as well as undesired dynamics such as breathing motion play an important role.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI

Abdullah¹,

Höller²,

Kunisch³

et al. 2022

Preprint

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In this paper, a nonlinear approach to separate different motion types in video data is proposed. This is particularly relevant in dynamic medical imaging (e.g. PET, MRI), where patient motion poses a significant challenge due to its effects on the image reconstruction as well as for its subsequent interpretation. Here, a new method is proposed where dynamic images are represented as the forward mapping of a sequence of latent variables via a generator neural network. The latent variables are structured so that temporal variations in the data are represented via dynamic latent variables, which are independent of static latent variables characterizing the general structure of the frames. In particular, different kinds of motion are also characterized independently of each other via latent space disentanglement using one-dimensional prior information on all but one of the motion types. This representation allows to freeze any selection of motion types, and to obtain accurate independent representations of other dynamics of interest. Moreover, the proposed algorithm is training-free, i.e., all the network parameters are learned directly from a single video. We illustrate the performance of this method on phantom and real-data MRI examples, where we successfully separate respiratory and cardiac motion.Preprint. Under review.

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Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Cited by 15 publications

References 112 publications

PET respiratory motion correction: quo vadis?

PET respiratory motion correction: quo vadis?

Synergistic tomographic image reconstruction: part 2

Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI

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