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

Deidda, Daniel; Karakatsanis, Nicolas A.; Robson, Philip M.; Efthimiou, Nikos; Fayad, Zahi A.; Aykroyd, Robert G.; Tsoumpas, Charalampos

doi:10.1109/trpms.2018.2884176

Cited by 25 publications

(24 citation statements)

References 39 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HKEM implementation by Ref. [50,51], uses all voxels within a spatial neighborhood to contribute to a basis function. An alternative implementation of HKEM would be TABLE II.…”

Section: A1 Kernel Methodsmentioning

confidence: 99%

“…46,47 One potential avenue currently under investigation for reducing the suppression of these high-intensity PET-unique regions is to extend the MR guidance to include the reconstructed PET image at each iteration. This concept was implemented firstly via MAP (regularization) 3,48,49 and has recently been extended to a kernel (reparameterization) 50,51 implementation. Such methods shall be referred to as PET-MR-informed, from which this work shall compare the anato-functional (a MAP method), 52 and the hybrid kernel method (HKEM).…”

Section: Introductionmentioning

confidence: 99%

“…Such methods shall be referred to as PET-MR-informed, from which this work shall compare the anato-functional (a MAP method), 52 and the hybrid kernel method (HKEM). 50,51 Alternative PET-MR-informed methods are present in the literature, which incorporate both PET and MR information in the regularization term, including joint Shannon entropy 4,53 and parallel level sets priors. 54,55 While such methods remain active within the PET reconstruction field, they do not lie within the same group of Medical Physics, 46 (11), November 2019 algorithms under comparison that determine PET-MR similarity via a local neighborhood similarity matrix.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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)

View full text Add to dashboard Cite

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...

show abstract

“…The HKEM implementation by Ref. [50,51], uses all voxels within a spatial neighborhood to contribute to a basis function. An alternative implementation of HKEM would be TABLE II.…”

Section: A1 Kernel Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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)

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Hybrid Kernel Expectation Maximization (Hkem)mentioning

confidence: 99%

“…where and are the feature vectors calculated from the attenuation image and the iteration PET image, , respectively, while , , and are scaling parameters for the distances in 7and (8). Further details and implementation of the HKEM technique can be found in [14] and [15].…”

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Intercomparison of MR‐informed PET image reconstruction methods

et al. 2019

View full text Add to dashboard Cite

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 [18F] fluorodeoxyglucose (FDG) three‐dimensional datasets were used. The real [18F]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 terms of t...

show abstract

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

Cited by 25 publications

References 39 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

Intercomparison of MR‐informed PET image reconstruction methods

Contact Info

Product

Resources

About