Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer

Knoll, Florian; Höller, Martin; Koesters, Thomas; Otazo, Ricardo; Bredies, Kristian; Sodickson, Daniel K.

doi:10.1109/tmi.2016.2564989

Cited by 116 publications

(135 citation statements)

References 52 publications

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“…Essentially, the optimization problem of joint MR-PET image reconstruction can be written as (Ehrhardt et al, 2015;Knoll et al, 2016;Mehranian et al, 2018;Sudarshan, Chen, & Awate, 2018): …”

Section: Recent Developments In Mr-pet Image Reconstruction 341 |mentioning

confidence: 99%

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

Chen

Jamadar

et al. 2018

Human Brain Mapping

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Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.

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Section: Recent Developments In Mr-pet Image Reconstruction 341 |mentioning

confidence: 99%

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

Chen

Jamadar

et al. 2018

Human Brain Mapping

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“…We also compared our method with the joint TGV method using the phantom and software publicly provided by Ref. . The results presented in Supporting Information Figure show that both synergistic methods perform similarly for modality‐shared edges but differently for modality‐unique lesions.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of computational complexity compared to previous methods, the added computational load of our algorithm is not substantial because there is no need for optimization with respect to primal and dual variables as used in Ref. ; and there is no optimization of an augmented Lagrangian problem as used in Ref. .…”

Section: Discussionmentioning

confidence: 99%

Multi‐modal synergistic PET and MR reconstruction using mutually weighted quadratic priors

Mehranian

Belzunce

McGinnity

et al. 2018

Magnetic Resonance in Med

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Purpose To propose a framework for synergistic reconstruction of PET‐MR and multi‐contrast MR data to improve the image quality obtained from noisy PET data and from undersampled MR data. Theory and Methods Weighted quadratic priors were devised to preserve common boundaries between PET‐MR images while reducing noise, PET Gibbs ringing, and MR undersampling artifacts. These priors are iteratively reweighted using normalized multi‐modal Gaussian similarity kernels. Synergistic PET‐MR reconstructions were built on the PET maximum a posteriori expectation maximization algorithm and the MR regularized sensitivity encoding method. The proposed approach was compared to conventional methods, total variation, and prior‐image weighted quadratic regularization methods. Comparisons were performed on a simulated [ 18 F]fluorodeoxyglucose‐PET and T 1 /T 2 ‐weighted MR brain phantom, 2 in vivo T 1 /T 2 ‐weighted MR brain datasets, and an in vivo [ 18 F]fluorodeoxyglucose‐PET and fluid‐attenuated inversion recovery/T 1 ‐weighted MR brain dataset. Results Simulations showed that synergistic reconstructions achieve the lowest quantification errors for all image modalities compared to conventional, total variation, and weighted quadratic methods. Whereas total variation regularization preserved modality‐unique features, this method failed to recover PET details and was not able to reduce MR artifacts compared to our proposed method. For in vivo MR data, our method maintained similar image quality for 3× and 14× accelerated data. Reconstruction of the PET‐MR dataset also demonstrated improved performance of our method compared to the conventional independent methods in terms of reduced Gibbs and undersampling artifacts. Conclusion The proposed methodology offers a robust multi‐modal synergistic image reconstruction framework that can be readily built on existing established algorithms.

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“…The algorithm is general enough to solve the optimization problem with other priors such as the directional total variation / parallel level sets, 45, 46 total generalized variation (TGV), [47][48][49] directional TGV 50 or structure tensor-based total variation 51 to name a few. In addition to PET reconstruction, SPDHG may help easing the computational effort in multi-modal medical imaging as well, where the PDHG has been used to jointly reconstruct from simultaneously acquired PET-MRI data with a nuclear norm variant of TGV 52 or coupled Bregman iterations. 53 Similarly, a comparable speed up is expected in other imaging modalities that involve line integrals like computerized tomography (CT) where PDHG also found its applications.…”

Section: Discussionmentioning

confidence: 99%

Faster PET reconstruction with a stochastic primal-dual hybrid gradient method

Ehrhardt

Markiewicz

Chambolle

et al. 2017

Wavelets and Sparsity XVII

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Image reconstruction in positron emission tomography (PET) is computationally challenging due to Poisson noise, constraints and potentially non-smooth priors-let alone the sheer size of the problem. An algorithm that can cope well with the first three of the aforementioned challenges is the primal-dual hybrid gradient algorithm (PDHG) studied by Chambolle and Pock in 2011. However, PDHG updates all variables in parallel and is therefore computationally demanding on the large problem sizes encountered with modern PET scanners where the number of dual variables easily exceeds 100 million. In this work, we numerically study the usage of SPDHG-a stochastic extension of PDHG-but is still guaranteed to converge to a solution of the deterministic optimization problem with similar rates as PDHG. Numerical results on a clinical data set show that by introducing randomization into PDHG, similar results as the deterministic algorithm can be achieved using only around 10 % of operator evaluations. Thus, making significant progress towards the feasibility of sophisticated mathematical models in a clinical setting.

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Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer

Cited by 116 publications

References 52 publications

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

From simultaneous to synergistic MR‐PET brain imaging: A review of hybrid MR‐PET imaging methodologies

Multi‐modal synergistic PET and MR reconstruction using mutually weighted quadratic priors

Faster PET reconstruction with a stochastic primal-dual hybrid gradient method

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