PET Parametric Imaging: Past, Present, and Future

Wang, Guobao; Rahmim, Arman; Gunn, Roger N.

doi:10.1109/trpms.2020.3025086

Cited by 79 publications

(62 citation statements)

References 137 publications

(155 reference statements)

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“…But there are also a number of techniques for voxel-level analysis. Spectral techniques use a positive linear combination of exponentials to approximate the voxel-level residue ( Cunningham and Jones, 1993 ; Veronese et al, 2016 ; Wang et al, 2020 ). The NPRM approach models voxel-level time-course data by a positive linear combination of basis vectors, { μ k , k = 1, …, K } – referred to a sub-TACs (TAC stands for time-activity curve).…”

Section: Applications To Parametric Imagingmentioning

confidence: 99%

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

O’Sullivan

et al. 2021

Medical Image Analysis

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PET imaging is an important diagnostic tool for management of patients with cancer and other diseases. Medical decisions based on quantitative PET information could potentially benefit from the availability of tools for evaluation of associated uncertainties. Raw PET data can be viewed as a sample from an inhomogeneous Poisson process so there is the possibility to directly apply bootstrapping to raw projection-domain list-mode data. Unfortunately this is computationally impractical, particularly if data reconstruction is iterative or the acquisition protocol is dynamic. We develop a flexible statistical linear model analysis to be used with multi-frame PET image data to create valid bootstrap samples. The technique is illustrated using data from dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients. As is often the case with dynamic PET studies, data have been archived without raw list-mode information. Using the bootstrapping technique maps of kinetic parameters and associated uncertainties are obtained. The quantitative performance of the approach is assessed by simulation. The proposed image-domain bootstrap is found to substantially match the projection-domain alternative. Analysis of results points to a close relation between relative uncertainty in voxel-level kinetic parameters and local reconstruction error. This is consistent with statistical theory.

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Section: Applications To Parametric Imagingmentioning

confidence: 99%

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

O’Sullivan

et al. 2021

Medical Image Analysis

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“…Apart from static acquisition, whole-body dynamic PET imaging better reflects the kinetic aspect of tracer uptake, through continuous acquisition over an extended period of time ( 34 ). Different types of parametric maps have been developed to visualize the kinetic parameters based on the dynamic imaging ( 35 ). Patlak plot is an important parametric map which was found useful in 99m Tc-MDP ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

68Ga-P15-041, A Novel Bone Imaging Agent for Diagnosis of Bone Metastases

et al. 2021

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Objectives68Ga-P15-041 (68Ga-HBED-CC-BP) is a novel bone-seeking PET radiotracer, which can be readily prepared by using a simple kit formulation and an in-house 68Ga/68Ge generator. The aim of this study is to assess the potential human application of 68Ga-P15-041 for clinical PET/CT imaging and to compare its efficacy to detect bone metastases of different cancers with 99mTc-MDP whole-body bone scintigraphy (WBBS).MethodsInitial kinetic study using Patlak analysis and parametric maps were performed in five histopathologically proven cancer patients (three males, two females) using 68Ga-P15-041 PET/CT scan only. Another group of 51 histopathologically proven cancer patients (22 males, 29 females) underwent both 99mTc-MDP WBBS and 68Ga-P15-041 PET/CT scans within a week, sequentially. Using either pathology examination or follow-up CT or MRI scans as the gold standard, the diagnostic efficacy and receiver operating characteristic curve (ROC) of the two methods in identifying bone metastases were compared (p <0.05, statistically significant).ResultsFifty-one patients were imaged, and 174 bone metastatic sites were identified. 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS detected 162 and 81 metastases, respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS were 93.1% vs 81.8%, 89.8% vs 90.7%, 77.5% vs 69.2%, 97.2% vs 93.4% and 90.7% vs 88.4%, respectively. Our results showed that the mean of SUVmax was significantly higher in metastases than that in benign lesions, 15.1 ± 6.9 vs. 5.6 ± 1.3 (P <0.001). Using SUVmax = 7.6 as the cut-off value by PET/CT, it was possible to predict the occurrence of metastases (AUC = 0.976; P <0.001; 95% CI: 0.946–0.999). However, it was impossible to distinguish osteoblastic bone metastases from osteolytic bone lesions. Parametric maps based on Patlak analysis provided excellent images and highly valuable quantitative information.Conclusions68Ga-P15-041 PET/CT, offering a rapid bone scan and high contrast images in minutes, is superior to the current method of choice in detecting bone metastases. It is reasonable to suggest that 68Ga-P15-041 PET/CT could become a valuable routine nuclear medicine procedure in providing excellent images for detecting bone metastases in cancer patients. 68Ga-P15-041 could become a valuable addition expanding the collection of 68Ga-based routine nuclear medicine procedures where 18F fluoride is not currently available.

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“…The state of the art of dynamic positron emission tomography is reviewed in survey papers [4,19,25,26]. Our paper belongs to the category of nonlinear fitting since the compartmental models are essentially nonlinear [29,31].…”

Section: Previous Work Problem Statement and Objectivesmentioning

confidence: 99%

Robust compartmental model fitting in direct emission tomography reconstruction

et al. 2021

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Dynamic tomography reconstructs a time activity curve (TAC) for every voxel assuming that the algebraic form of the function is known a priori. The algebraic form derived from the analysis of compartmental models depends nonlinearly on the nonnegative parameters to be determined. Direct methods apply fitting in every iteration step. Because of the iterative nature of the maximum likelihood–expectation maximization (ML–EM) reconstruction, the fitting result of the previous step can serve as a good starting point in the current step; thus, after the first iteration we have a guess that is not far from the solution, which allows the use of gradient-based local optimization methods. However, finding good initial guesses for the first ML–EM iteration is a critical problem since gradient-based local optimization algorithms do not guarantee convergence to the global optimum if they are started at an inappropriate location. This paper examines the robust solution of the fitting problem both in the initial phase and during the ML–EM iteration. This solution is implemented on GPUs and is built into the 4D reconstruction module of the TeraTomo software.

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PET Parametric Imaging: Past, Present, and Future

Cited by 79 publications

References 137 publications

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

68Ga-P15-041, A Novel Bone Imaging Agent for Diagnosis of Bone Metastases

Robust compartmental model fitting in direct emission tomography reconstruction

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