MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging

Bland, James; Mehranian, Abolfazl; Belzunce, Martin A.; Ellis, Sam; McGinnity, Colm J.; Hammers, Alexander; Reader, Andrew J.

doi:10.1109/trpms.2017.2771490

Cited by 62 publications

(51 citation statements)

References 30 publications

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“…Therefore, in relatively uniform MR regions where the MR intensity values are similar, spatially close voxels will be selected over more disparate voxels, thereby helping recovery of PET-unique features. More generally, KEM has been applied to a range of reconstruction problems, [12][13][14][15][16][17][18][19][20][21] and is an example of a broader cohort of algorithms that reparameterize the emission image into an alternative set of basis functions. [22][23][24] In contrast to reparameterizing the reconstruction process, MR information can alternatively be included into the reconstruction process through the addition of a regularizing term in either a Bayesian maximum a posteriori (MAP) or penalized maximum likelihood (PL) framework.…”

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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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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“…The recently proposed hybrid kernel expectation maximization (HKEM) method [14], [15], which uses information from both PET and an anatomical image in order to compensate for partial volume effects, was used in this study. The advantage of the kernel method is that it does not require segmentation and it achieves improved resolution for each individual voxel and also for the edges of a region [16], [17]. This technique, although it is not a dedicated partial volume correction technique, was used so as to explore the edge-preserving and noise-suppression performance in enhancing resolution recovery and reducing the spill-in effect from the hot background into the colder ROIs [18].…”

Section: Hybrid Kernel Expectation Maximization (Hkem)mentioning

confidence: 99%

Comparison of Correction Techniques for the Spillin Effect in Emission Tomography

Akerele

Karakatsanis

Deidda

et al. 2020

IEEE Trans. Radiat. Plasma Med. Sci.

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In positron emission tomography (PET) imaging, accurate clinical assessment is often affected by the partial volume effect (PVE) leading to overestimation (spill-in) or underestimation (spill-out) of activity in various small regions. The spill-in correction, in particular, can be very challenging when the target region is close to a hot background region. Therefore, this study evaluates and compares the performance of various recently developed spill-in correction techniques, namely: background correction (BC), local projection (LP), and hybrid kernelized (HKEM) methods. We used a simulated digital phantom and [18F]-NaF PET data of three patients with abdominal aortic aneurysms (AAA) acquired with Siemens Biograph mMRTM and mCTTM scanners respectively. Region of Interest (ROI) analysis was performed and the extracted SUVmean, SUVmax and target-tobackground ratio (TBR) scores were compared. Results showed substantial spill-in effects from hot regions to targeted regions, which are more prominent in small structures. The phantom experiment demonstrated the feasibility of spill-in correction with all methods. For the patient data, large differences in SUVmean, SUVmax and TBRmax scores were observed between the ROIs drawn over the entire aneurysm and ROIs excluding some regions close to the bone. Overall, BC yielded the best performance in spill-in correction in both phantom and patient studies.

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“…Gong et al [7] used a hybrid kernel method to perform direct Patlak reconstruction from dynamic PET using MR and PET information where the latter was obtained by combining different frames. Bland et al (2017) [28] studied the effect of KEM on simulated dose-reduced datasets, showing improved contrast to noise ratio, but at the cost of possible over-smoothing of features unique to the PET data. To overcome this issue [29] proposed a method using a spatially constrained MR kernel in order to maintain the noise reduction properties of the conventional kernel method, whilst better retaining the features unique to the PET data.…”

Section: Introductionmentioning

confidence: 99%

“…This is important because in some cases a PET lesion may be detected at the border between two different regions, as shown in [24]. Moreover, it has been shown that PET unique features can be severely over-smoothed [28] with the MR-guided kernel. Strul and Bendriem [33] investigated the limitations, due to MR segmentation and PET-MR registration, of different segmentation-based partial volume correction techniques.…”

Section: Introductionmentioning

confidence: 99%

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Deidda

Karakatsanis

Robson³

et al. 2019

IEEE Trans. Radiat. Plasma Med. Sci.

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Anatomically driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This paper examines the effects of spatial inconsistency between MR and PET images in hot and cold regions of PET images using the hybrid kernelized expectation maximization (HKEM) machine learning method. Our evaluation was conducted on Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the "spillin" for cold regions and the "spill-out" for hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using kernelized expectation maximization, against the 37% and 8% obtained with HKEM. The findings of

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MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging

Cited by 62 publications

References 30 publications

Intercomparison of MR-Informed Methods for PET Image Reconstruction

Intercomparison of MR-Informed Methods for PET Image Reconstruction

Comparison of Correction Techniques for the Spillin Effect in Emission Tomography

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

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