MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

Novosad, Philip; Reader, Andrew J.

doi:10.1088/0031-9155/61/12/4624

Cited by 68 publications

(79 citation statements)

References 34 publications

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“…The choice of basis function can range from blobs, to sophisticated structural‐based basis functions, for example, patch‐based dictionaries learned from MR data . Row‐normalized kernel matrices have also been used for reparameterization of PET images within reconstruction . When using a row‐normalized kernel matrix extracted from a guide image for reparameterization, each column i (i.e., basis function i ) indicates the similarity of voxel i to other voxels in the guide image.…”

Section: Methodsmentioning

confidence: 99%

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Two novel PET image restoration methods guided by PET‐MR kernels: Application to brain imaging

2019

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Purpose Post‐reconstruction positron emission tomography (PET) image restoration methods that take advantage of available anatomical information can play an important role in accurate quantification of PET images. However, when using anatomical information, the resulting PET image may lose resolution in certain regions where the anatomy does not agree with the change in functional activity. In this work, this problem is addressed by using both magnetic resonance (MR) and filtered PET images to guide the denoising process. Methods In this work, two novel post‐reconstruction methods for restoring PET images using the subject’s registered T1‐weighted MR image are proposed. The first method is based on a representation of the image using basis functions extracted from T1‐weighted MR and filtered PET image. The coefficients for these basis functions are estimated using a sparsity‐penalized least squares objective function. The second method is a noniterative fast method that uses guided kernel filtering in combination with twicing to restore the noisy PET image. When applied after conventional PVE correction, these methods can be considered as voxel‐based MR‐guided partial volume effect (PVE) correction methods. Results Using simulation analyses of [18F]FDG PET images of the brain with lesions, the proposed methods are compared to other denoising methods through different figures of merit. The results show promising improvements in image quality as well as reduction in bias and variance of the lesions. We also show the application of the proposed methods on real [18F]FDG data. Conclusion Two methods for restoring PET images were proposed. The methods were evaluated on simulation and real brain images. Most MR‐guided PVE correction methods are only based on segmented T1‐weighted images and their accuracy is very sensitive to segmentation errors, especially in regions of abnormalities and lesions. However, both proposed methods can use the T1‐weighted image without segmentation. The simplicity and the very low computational cost of the second method make it suitable for clinical applications and large data studies. The proposed methods can be naturally extended to PVE correction and denoising of other functional modalities using corresponding anatomical information.

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Section: Methodsmentioning

confidence: 99%

“…10 Row-normalized kernel matrices have also been used for reparameterization of PET images within reconstruction. 26,27 When using a row-normalized kernel matrix extracted from a guide image for reparameterization, each column i (i.e., basis function i) indicates the similarity of voxel i to other voxels in the guide image.…”

Section: C Reparameterizationmentioning

confidence: 99%

Two novel PET image restoration methods guided by PET‐MR kernels: Application to brain imaging

2019

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“…Anatomical MR images, such as T1 or T2 weighted, provide structural information that the PET radiotracer distribution is likely to correlate with, at least in part. This functional anatomical correspondence is particularly evident for neurological [ 18 F]fluorodeoxyglucose (FDG) PET scans, where the radiotracer distribution is well delineated between the white and gray matter boundaries. Numerous such MR-informed reconstruction methodologies have now been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of MR-Informed Methods for PET Image Reconstruction

Bland

Mehranian

Belzunce

et al. 2018

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC)

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Purpose: Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data. Methods: The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [ 18 F] fluorodeoxyglucose (FDG) threedimensional datasets were used. The real [ 18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. Results: For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in te...

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“…Several papers have investigated the combination and association of functional and anatomical images for a specific purpose, such as denoising or reconstruction of PET data using anatomical priors coming from MRI or CT, correction of partial volume effects in PET or SPECT by exploiting the associated morphological information, the use of associated CT images to spatially register several low‐resolution PET images, for instance during treatment or several different radiotracers, or the definition of tumor target volumes in radiotherapy by considering both PET and CT images features …”

Section: Introductionmentioning

confidence: 99%

A framework based on hidden Markov trees for multimodal PET/CT image co‐segmentation

et al. 2017

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MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

Cited by 68 publications

References 34 publications

Two novel PET image restoration methods guided by PET‐MR kernels: Application to brain imaging

Two novel PET image restoration methods guided by PET‐MR kernels: Application to brain imaging

Intercomparison of MR-Informed Methods for PET Image Reconstruction

A framework based on hidden Markov trees for multimodal PET/CT image co‐segmentation

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