Patient-Specific Method of Generating Parametric Maps of Patlak <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math> without Blood Sampling or Metabolite Correction: A Feasibility Study

Sayre, George A.; Franc, Benjamin L.; Seo, Youngho

doi:10.1155/2011/185083

Cited by 9 publications

(7 citation statements)

References 26 publications

(34 reference statements)

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“…Thus, an extended version of the sPatlak model, accepting the possibility of relatively small uptake reversibility, could enhance its accuracy by attributing the observed progressive signal intensity loss to an additional kinetic parameter rate constant: the tracer efflux rate constant. In fact, the theoretically expected K i underestimation, when an irreversible compartment replaces a reversible one, has been also confirmed by a number of clinical dynamic PET studies, thus further suggesting the possibility for underlying FDG uptake reversibility in certain normal organs and tumors (Lammertsma et al 1987, Messa et al 1992, Choi et al 1994, Hasselbalch et al 2001, Graham et al 2002, Wu et al 2003, Sayre et al 2011, Hoh et al 2011). …”

Section: Introductionmentioning

confidence: 82%

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

et al. 2015

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We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16–18% and 20–40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

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Section: Introductionmentioning

confidence: 82%

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

et al. 2015

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“…However a considerable number of studies suggest uptake reversibility for a range of tracers, as presented previously (Holden et al 1997, Lodge et al 1999, Karakatsanis et al 2015a). Since the sPatlak model assumes irreversible uptake, it may underestimate K i to compensate for lack of reversibility modeling (Messa et al 1992, Sayre et al 2011, Hoh et al 2011). …”

Section: Methodsmentioning

confidence: 99%

“…Although the sPatlak method is robust and therefore attractive for clinical usage, it does not account for uptake reversibility and therefore it may lead to biased K i estimates (Sayre et al 2011, Hoh et al 2011). In fact a number of studies have reported mild reversibility for normal tissues (Fischman and Alpert 1992, Hawkins et al 1992, Okazumi et al 1992, Choi et al 1994, Nelson et al 1996, Huang et al 2000, Graham et al 2000, Zhuang et al 2001, Iozzo et al 2003, Lin et al 2005, Prytz et al 2006) as well as some oncologic malignancy types, such as hepatocellular carcinoma (HCC) tumors (Messa et al 1992, Torizuka et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation–maximization reconstruction

Karakatsanis

Casey

Lodge³

et al. 2016

Phys. Med. Biol.

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Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the tracer influx rate Ki as a complimentary metric to the semi-quantitative standardized uptake value (SUV). The resulting Ki images may suffer from high noise due to the need for short acquisition frames. Meanwhile, a generalized Patlak (gPatlak) WB post-reconstruction method had been suggested to limit Ki bias of sPatlak analysis at regions with non-negligible 18F-FDG uptake reversibility; however, gPatlak analysis is non-linear and thus can further amplify noise. In the present study, we implemented, within the open-source Software for Tomographic Image Reconstruction (STIR) platform, a clinically adoptable 4D WB reconstruction framework enabling efficient estimation of sPatlak and gPatlak images directly from dynamic multi-bed PET raw data with substantial noise reduction. Furthermore, we employed the optimization transfer methodology to accelerate 4D expectation-maximization (EM) convergence by nesting the fast image-based estimation of Patlak parameters within each iteration cycle of the slower projection-based estimation of dynamic PET images. The novel gPatlak 4D method was initialized from an optimized set of sPatlak ML-EM iterations to facilitate EM convergence. Initially, realistic simulations were conducted utilizing published 18F-FDG kinetic parameters coupled with the XCAT phantom. Quantitative analyses illustrated enhanced Ki target-to-background ratio (TBR) and especially contrast-to-noise ratio (CNR) performance for the 4D vs. the indirect methods and static SUV. Furthermore, considerable convergence acceleration was observed for the nested algorithms involving 10–20 sub-iterations. Moreover, systematic reduction in Ki % bias and improved TBR were observed for gPatlak vs. sPatlak. Finally, validation on clinical WB dynamic data demonstrated the clinical feasibility and superior Ki CNR performance for the proposed 4D framework compared to indirect Patlak and SUV imaging.

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“…erroneously assumes it is zero, resulting in underestimation of the estimated tracer influx rate parameter K i in regions with non-negligible uptake reversibility [18][19][20][21], with the bias becoming larger for stronger underlying k loss rate constants [16,18,19]. However, this model supports a linear graphical analysis method and, thus, it is very robust to noise.…”

Section: Introductionmentioning

confidence: 99%

“…The FDG tracer kinetics may result in considerable changes of the count and contrast levels in both suspected tumors and their background regions, especially in the early frames, where the rise of the TAC is steep with time [1,11]. In addition, it has been shown that FDG may exhibit in some tumor or normal tissue regions a small, but non-negligible, tracer uptake reversibility, whose net tracer efflux effect can be modeled by the kinetic macro-parameter ( k loss ) or tracer efflux rate constant and is linearly dependent on k 4 kinetic micro-parameter [18][19][20][21]. Tracer uptake reversibility, or k loss , is expected to reduce, though to a relatively small extent, the absorbed or metabolized amount of activity, with the apparent effect on TACs being negligible at early frames but gradually becoming stronger at later time frames or TAC segments [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Impact of acquisition time-window on clinical whole-body PET parametric imaging

Karakatsanis

Lodge

Casey³

et al. 2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Abstract-Whole-body PET parametric imaging can combine the benefit of extended axial field-of-view (FOV) in multi-bed scans with that of generating time activity curves (TACs) in dynamic scans. We have recently proposed such a framework capable of delivering whole-body FDG Patlak images in clinically feasible scan times. The design of the acquisition protocol was limited to a single time-window and the standard Patlak graphical analysis method. However, the relatively long FDG half-life and uptake, compared to clinically acceptable acquisition time-windows, render the choice of this window critical. The major FDG kinetic components can be estimated from the early and intermediate TAC segments. On the contrary, at later timewindows, tumor contrast may be overall higher. In addition, the standard Patlak method does not account for tracer uptake reversibility, a property that becomes more apparent at later acquisition time-windows for certain tumors, thus increasing the probability for larger bias at later times. Consequently, the choice of the optimal time-window can be critical and should constitute an important design aspect of multi-bed dynamic protocols. In the present work we assessed the impact of a sliding acquisition time-window on whole-body FDG PET parametric images. This included incremental shift of a 6-pass acquisition time-window (~35min) along an extended scan period of 0-90min post injection, using both real patient kinetic data as well as realistic 4D simulations of the state-of-the-art Siemens Biograph mCT scanner. We also propose the selective application of a generalized Patlak method accounting for uptake reversibility. Our simulated and clinical results demonstrate that both Patlak methods (standard and generalized) result in enhanced tumor-tobackground contrast as well as contrast-to-noise ratios with minimal bias at an early acquisition time window (10-45min post injection) with the generalized method exhibiting systematically superior performance.

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Patient-Specific Method of Generating Parametric Maps of Patlak Ki without Blood Sampling or Metabolite Correction: A Feasibility Study

Cited by 9 publications

References 26 publications

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation–maximization reconstruction

Impact of acquisition time-window on clinical whole-body PET parametric imaging

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