Objective.This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics.Approach.We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis.Main results.The proposed model demonstrates a significant linear relationship between the variablexand the slopeDVTof the RE Logan plot, and the variablex\' and the slopeDVT\'of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of thex\' to thexequal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, thervalues for the linear fitting ofDVT\'toDVTequal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data.Significance.These results demonstrate the model\'s capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

Recently, deep learning-based denoising methods have been gradually used for PET images denoising and have shown great achievements. Among these methods, one interesting framework is conditional deep image prior (CDIP) which is an unsupervised method that does not need prior training or a large number of training pairs. In this work, we combined CDIP with Logan parametric image estimation to generate high-quality parametric images. In our method, the kinetic model is the Logan reference tissue model that can avoid arterial sampling. The neural network was utilized to represent the images of Logan slope and intercept. The patient\'s computed tomography (CT) image or magnetic resonance (MR) image was used as the network input to provide anatomical information. The optimization function was constructed and solved by the alternating direction method of multipliers (ADMM) algorithm. Both simulation and clinical patient datasets demonstrated that the proposed method could generate parametric images with more detailed structures. Quantification results showed that the proposed method results had higher contrast-to-noise (CNR) improvement ratios (PET/CT datasets: 62.25%±29.93%; striatum of brain PET datasets : 129.51%±32.13%, thalamus of brain PET datasets: 128.24%±31.18%) than Gaussian filtered results (PET/CT datasets: 23.33%±18.63%; striatum of brain PET datasets: 74.71%±8.71%, thalamus of brain PET datasets: 73.02%±9.34%) and nonlocal mean (NLM) denoised results (PET/CT datasets: 37.55%±26.56%; striatum of brain PET datasets: 100.89%±16.13%, thalamus of brain PET datasets: 103.59%±16.37%).

Recent studies have shown that standard compartmental models using plasma input or the cerebellum reference tissue input are generally not reliable for quantifying tau burden in dynamic 18F-flortaucipir PET studies of Alzheimer disease. So far, the optimal reference region for estimating 18F-flortaucipir delivery and specific tau binding has yet to be determined. The objective of the study is to improve 18F-flortaucipir brain tau PET quantification using a spatially constrained kinetic model with dual reference tissues.
Participants were classified as either cognitively normal (CN) or cognitively impaired (CI) based on clinical assessment. T1-weighted structural MRI and 105-min dynamic 18F-flortaucipir PET scans were acquired for each participant. Using both a simplified reference tissue model (SRTM2) and Logan plot with either cerebellum gray matter or centrum semiovale (CS) white matter as the reference tissue, we estimated distribution volume ratios (DVRs) and the relative transport rate constant R1 for region of interest-based (ROI) and voxelwise-based analyses. Conventional linear regression (LR) and LR with spatially constrained (LRSC) parametric imaging algorithms were then evaluated. Noise-induced bias in the parametric images was compared to estimates from ROI time activity curve-based kinetic modeling. We finally evaluated standardized uptake value ratios at early phase (SUVREP, 0.7-2.9 min) and late phase (SUVRLP, 80-105 min) to approximate R1 and DVR, respectively.
The percent coefficients of variation of R1 and DVR estimates from SRTM2 with spatially constrained modeling were comparable to those from the Logan plot and SUVRs. The SRTM2 using CS reference tissue with LRSC reduced noise-induced underestimation in the LR generated DVR images to negligible levels (< 1%). Inconsistent overestimation of DVR in the SUVRLP only occurred using the cerebellum reference tissue-based measurements. The CS reference tissue-based DVR and SUVRLP, and cerebellum-based SUVREP and R1 provided higher Cohen\'s effect size d to detect increased tau deposition and reduced relative tracer transport rate in CI individuals.
Using a spatially constrained kinetic model with dual reference tissues significantly improved quantification of relative perfusion and tau binding. Cerebellum and CS are the suggested reference tissues to estimate R1 and DVR, respectively, for dynamic 18F-flortaucipir PET studies. Cerebellum-based SUVREP and CS-based SUVRLP may be used to simplify 18F-flortaucipir PET study.

BACKGROUND: Reference tissue-based quantification of brain PET data does not typically include correction for signal originating from blood vessels, which is known to result in biased outcome measures. The bias extent depends on the amount of radioactivity in the blood vessels. In this study, we seek to revisit the well-established Logan plot and derive alternative formulations that provide estimation of distribution volume ratios (DVRs) that are corrected for the signal originating from the vasculature.
RESULTS: New expressions for the Logan plot based on arterial input function and reference tissue were derived, which included explicit terms for whole blood radioactivity. The new methods were evaluated using PET data acquired using [11C]raclopride and [18F]MNI-659. The two-tissue compartment model (2TCM), with which signal originating from blood can be explicitly modeled, was used as a gold standard. DVR values obtained for [11C]raclopride using the either blood-based or reference tissue-based Logan plot were systematically underestimated compared to 2TCM, and for [18F]MNI-659, a proportionality bias was observed, i.e., the bias varied across regions. The biases disappeared when optimal blood-signal correction was used for respective tracer, although for the case of [18F]MNI-659 a small but systematic overestimation of DVR was still observed.
CONCLUSIONS: The new method appears to remove the bias introduced due to absence of correction for blood volume in regular graphical analysis and can be considered in clinical studies. Further studies are however required to derive a generic mapping between plasma and whole-blood radioactivity levels.

MRI estimates of extracellular volume and tumor exudate flux in peritumoral tissue are demonstrated in an experimental model of cerebral tumor. Peritumoral extracellular volume predicted the tumor exudate flux. Eighteen RNU athymic rats were inoculated intracerebrally with U251MG tumor cells and studied with dynamic contrast enhanced MRI (DCE-MRI) approximately 18 days post implantation. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the distribution volume (i.e. tissue porosity) in the leaky rim of the tumor and that in the tissue external to the rim (the outer rim) were estimated, as was the tumor exudate flow from the inner rim of the tumor through the outer rim. Distribution volume in the outer rim was approximately half that of the inner adjacent region (p < 1 × 10(-4)). The distribution volume of the outer ring was significantly correlated (R(2) = 0.9) with tumor exudate flow from the inner rim. Thus, peritumoral extracellular volume predicted the rate of tumor exudate flux. One explanation for these data is that perfusion, i.e. the delivery of blood to the tumor, was regulated by the compression of the mostly normal tissue of the tumor rim, and that the tumor exudate flow was limited by tumor perfusion.

The distribution of dynamic contrast-enhanced MRI (DCE-MRI) parametric estimates in a rat U251 glioma model was analyzed. Using Magnevist as contrast agent (CA), 17 nude rats implanted with U251 cerebral glioma were studied by DCE-MRI twice in a 24 h interval. A data-driven analysis selected one of three models to estimate either (1) plasma volume (vp), (2) vp and forward volume transfer constant (K(trans)) or (3) vp, K(trans) and interstitial volume fraction (ve), constituting Models 1, 2 and 3, respectively. CA distribution volume (VD) was estimated in Model 3 regions by Logan plots. Regions of interest (ROIs) were selected by model. In the Model 3 ROI, descriptors of parameter distributions--mean, median, variance and skewness--were calculated and compared between the two time points for repeatability. All distributions of parametric estimates in Model 3 ROIs were positively skewed. Test-retest differences between population summaries for any parameter were not significant (p ≥ 0.10; Wilcoxon signed-rank and paired t tests). These and similar measures of parametric distribution and test-retest variance from other tumor models can be used to inform the choice of biomarkers that best summarize tumor status and treatment effects.

Neuroimaging techniques, including positron emission tomography (PET), are widely used in clinical settings and in basic neuroscience research. Education in these methods and their applications may be incorporated into curricula to keep pace with this expanding field. Here, we have developed pedagogical materials on the fundamental principles of PET that incorporate a hands-on laboratory activity to view and analyze human brain scans. In this activity, students will use authentic PET brain scans generated from original research at Brookhaven National Laboratory (Volkow et al., 2009) to explore the neurobiological effects of a drug on the dopamine system. We provide lecture and assignment materials (including a 50-minute PowerPoint presentation introducing PET concepts), written background information for students and instructors, and explicit instructions for a 4-hour, computer-based laboratory to interested educators. Also, we discuss our experience implementing this exercise as part of an advanced undergraduate laboratory course at Stony Brook University in 2010 and 2011. Observing the living human brain is intriguing, and this laboratory is designed to illustrate how PET neuroimaging techniques are used to directly probe biological processes occurring in the living brain. Laboratory course modules on imaging techniques such as PET can pique the interest of students potentially interested in neuroscience careers, by exposing them to current research methods. This activity provides practical experience analyzing PET data using a graphical analysis method known as the Logan plot, and applies core neuropharmacology concepts. We hope that this manuscript inspires college instructors to incorporate education in PET neuroimaging into their courses.

OBJECTIVE: To test the hypothesis that a noninvasive dynamic contrast enhanced MRI (DCE-MRI) derived interstitial volume fraction (ve ) and/or distribution volume (VD ) were correlated with tumor cellularity in cerebral tumor.
METHODS: T1 -weighted DCE-MRI studies were performed in 18 athymic rats implanted with U251 xenografts. After DCE-MRI, sectioned brain tissues were stained with Hematoxylin and Eosin for cell counting. Using a Standard Model analysis and Logan graphical plot, DCE-MRI image sets during and after the injection of a gadolinium contrast agent were used to estimate the parameters plasma volume (vp ), forward transfer constant (K(trans) ), ve , and VD .
RESULTS: Parameter values in regions where the standard model was selected as the best model were: (mean ± S.D.): vp = (0.81 ± 0.40)%, K(trans) = (2.09 ± 0.65) × 10(-2) min(-1) , ve = (6.65 ± 1.86)%, and VD = (7.21 ± 1.98)%. The Logan-estimated VD was strongly correlated with the standard model\'s vp + ve (r = 0.91, P < 0.001). The parameters, ve and/or VD , were significantly correlated with tumor cellularity (r ≥ -0.75, P < 0.001 for both).
CONCLUSIONS: These data suggest that tumor cellularity can be estimated noninvasively by DCE-MRI, thus supporting its utility in assessing tumor pathophysiology.