Tracer Kinetic Modeling Research Articles

Initial Steps to Tracer Kinetic Modeling and MBF Quantification

Initial Steps to Tracer Kinetic Modeling and MBF Quantification

Quantification of Tau Load in Alzheimer's Disease Clinical Trials Using Positron Emission Tomography.

Alzheimer's disease is a neurodegenerative condition that is neuropathologically characterized by the presence of amyloid-β plaques and neurofibrillary tangles consisting of tau. Recently, several positron emission tomography (PET) tracers have been developed that yielded promising initial results. In this chapter, we discuss how tau PET can be used in the context in clinical trials. We argue that simplified reference tissue models based on dynamic data acquisition are most suitable for accurately measuring changes in tau pathology in trials tailored to reduce cerebral tau load. Therefore, we discuss the importance of tracer kinetic modeling and describe in detail how a reliable measurement of specific binding can be obtained.

Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T

BackgroundDynamic-contrast-enhanced (DCE) MRI data acquired using gradient echo based sequences is affected by errors in flip angle (FA) due to transmit B1 inhomogeneity (B1inh). The purpose of the study was to evaluate the effect of B1inh on quantitative analysis of DCE-MRI data of human brain tumor patients and to evaluate the clinical significance of B1inh correction of perfusion parameters (PPs) on tumor grading.MethodsAn MRI study was conducted on 35 glioma patients at 3T. The patients had histologically confirmed glioma with 23 high-grade (HG) and 12 low-grade (LG). Data for B1-mapping, T1-mapping and DCE-MRI were acquired. Relative B1 maps (B1rel) were generated using the saturated-double-angle method. T1-maps were computed using the variable flip-angle method. Post-processing was performed for conversion of signal–intensity time (S(t)) curve to concentration–time (C(t)) curve followed by tracer kinetic analysis (Ktrans, Ve, Vp, Kep) and first pass analysis (CBV, CBF) using the general tracer-kinetic model. DCE-MRI data was analyzed without and with B1inh correction and errors in PPs were computed. Receiver-operating-characteristic (ROC) analysis was performed on HG and LG patients. Simulations were carried out to understand the effect of B1 inhomogeneity on DCE-MRI data analysis in a systematic way. S(t) curves mimicking those in tumor tissue, were generated and FA errors were introduced followed by error analysis of PPs. Dependence of FA-based errors on the concentration of contrast agent and on the duration of DCE-MRI data was also studied. Simulations were also done to obtain Ktrans of glioma patients at different B1rel values and see whether grading is affected or not.ResultsCurrent study shows that B1rel value higher than nominal results in an overestimation of C(t) curves as well as derived PPs and vice versa. Moreover, at same B1rel values, errors were large for larger values of C(t). Simulation results showed that grade of patients can change because of B1inh.ConclusionsB1inh in the human brain at 3T-MRI can introduce substantial errors in PPs derived from DCE-MRI data that might affect the accuracy of tumor grading, particularly for border zone cases. These errors can be mitigated using B1inh correction during DCE-MRI data analysis.

Validation of Noninvasive Tracer Kinetic Analysis of 18F-Florbetaben PET Using a Dual-Time-Window Acquisition Protocol.

Accurate amyloid PET quantification is necessary for monitoring amyloid-β accumulation and response to therapy. Currently, most of the studies are analyzed using the static SUV ratio (SUVR) approach because of its simplicity. However, this approach may be influenced by changes in cerebral blood flow (CBF) or radiotracer clearance. Full tracer kinetic models require arterial blood sampling and dynamic image acquisition. The objectives of this work were, first, to validate a noninvasive kinetic modeling approach for 18F-florbetaben PET using an acquisition protocol with the best compromise between quantification accuracy and simplicity and, second, to assess the impact of CBF changes and radiotracer clearance on SUVRs and noninvasive kinetic modeling data in 18F-florbetaben PET. Methods: Using data from 20 subjects (10 patients with probable Alzheimer dementia and 10 healthy volunteers), the nondisplaceable binding potential (BPND) obtained from the full kinetic analysis was compared with the SUVR and with noninvasive tracer kinetic methods (simplified reference tissue model and multilinear reference tissue model 2). Various approaches using shortened or interrupted acquisitions were compared with the results of the full acquisition (0-140 min). Simulations were performed to assess the effect of CBF and radiotracer clearance changes on SUVRs and noninvasive kinetic modeling outputs. Results: An acquisition protocol using time windows of 0-30 and 120-140 min with appropriate interpolation of the missing time points provided the best compromise between patient comfort and quantification accuracy. Excellent agreement was found between BPND obtained using the full protocol and BPND obtained using the dual-window protocol (for multilinear reference tissue model 2, BPND [dual-window] = 0.01 + 1.00·BPND [full], R2 = 0.97; for simplified reference tissue model, BPND [dual-window] = 0.05 + 0.92·BPND [full], R2 = 0.93). Simulations showed a limited impact of CBF and radiotracer clearance changes on multilinear reference tissue model parameters and SUVR. Conclusion: This study demonstrated accurate noninvasive kinetic modeling of 18F-florbetaben PET data using a dual-window acquisition, thus providing a good compromise between quantification accuracy, scan duration, and patient burden. The influence of CBF and radiotracer clearance changes on amyloid-β load estimates was small. For most clinical research applications, the SUVR approach is appropriate. However, for longitudinal studies in which maximum quantification accuracy is desired, this noninvasive dual-window acquisition with kinetic analysis is recommended.

Optimization of imaging protocols for myocardial blood flow (MBF) quantification with 18F-flurpiridaz PET

The new PET tracer, 18F-flurpiridaz, with high myocardial extraction allows quantitative myocardial blood flow (MBF) estimation from dynamic PET data and tracer kinetic modeling. The goal of this study is to determine the optimal imaging protocols and parameters using a realistic simulation study. The time activity curves (TACs) of different tissue organs from a 30-s infusion time (IT) of 18F-flurpiridaz in a dynamic PET study were extracted from a previous study. The TACs at different time points were incorporated in a series of realistic 3D XCAT phantoms from which the parameters of a 2-compartment model and the ‘true’ MBF of 18F-flurpiridaz were determined. The compartmental model was used to generate TACs from 7 additional ITs. PET projection data from the XCAT phantoms were generated using Monte Carlo simulation. They were reconstructed using an OS-EM reconstruction algorithm with different update number (N) to obtain dynamic PET images. The blood and myocardial TACs were derived from the dynamic images from which the MBF and %MBF error was estimated. The %MBF error decreases with increasing N of the OS-EM and levels off after ∼42. The 30-s IT gave the smallest %MBF error that decreases from ∼0.57% to ∼19.40%. The MBF for 2-min, 4-min, 8-min and 16-min IT were statistically significant different from the MBF for 30-s IT (P<0.05). Too fast or too slow infusion time gave higher %MBF error. The optimal imaging protocol in dynamic 18F-flurpiridaz PET for accurate quantitative MBF estimation was 30-s IT and N of ∼42 for the OS-EM.

Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer

In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T1 weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and {{boldsymbol{chi }}}^{{bf{2}}} were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF2CX = 68 ± 44 ml/100 ml/min, PF2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, {{boldsymbol{chi }}}^{{bf{2}}}:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and {{boldsymbol{chi }}}^{{bf{2}}} values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.

Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI using a model consistency constraint.

To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data. The proposed method poses the tracer-kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI. In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF. The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med 79:2804-2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Transport of Liposome Encapsulated Drugs in Voxelized Computational Model of Human Brain Tumors.

There are many obstacles in the transport of chemotherapeutic drugs to tumor cells that lead to irregular and non-uniform uptake of drugs inside tumors. The study of these transport problems will help with accurate prediction of drug transport and optimizing treatment strategy. To this end, liposome mediated drug delivery has emerged as an excellent anticancer therapy due to its ability to deliver drugs at site of action and reducing the chances of side effects to the healthy tissues. In this paper, a computational fluid dynamics (CFD) model based on realistic vasculature of human brain tumor is presented. This model utilizes dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data to account for heterogeneity in tumor vasculature. Porosity of the interstitial space inside the tumor and normal tissue is determined voxel-wise by processing the DCE-MRI images by general tracer kinetic model (GTKM). The CFD model is applied to predict transport of two different types of liposomes (stealth and conventional) in tumors. The amount of accumulated liposomes is compared with accumulated free drug (doxorubicin) in the interstitial space. Simulation results indicate that stealth liposomes accumulate more and remain for longer periods of time in tumors as compared with conventional liposomes and free drug. The present model provides us a qualitative and quantitative examination on the transport and deposition of liposomes as well as free drugs in actual human brain tumors.

Research progress of quantitative perfusion parameters of dynamic contrast-enhanced MRI in diagnosis and efficacy evaluation of uterine tumors

The quantitative parameters based on dynamic contrast-enhanced MRI (DCE-MRI) scan, simulate the distribution of contrast inside and outside of the blood vessels through a variety of tracer kinetic models. The DCE-MRI perfusion parameters now are widely applied in clinical management of uterine tumors, to analyze the microcirculation characteristics in tumor and to guide the diagnosis, tumor grading and efficacy evaluation. Key words: Magnetic resonance imaging; Uterine neoplasms; Diagnoses

Understanding Ktrans: a simulation study based on a multiple-pathway model

The transfer constant Ktrans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of Ktrans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of Ktrans. Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between Ktrans and plasma flow Fp, vessel permeability PS and extraction rate EFp under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of Ktrans based on the vascular transit time. Ktrans is primarily associated with Fp and EFp respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and Fp only serve as secondary constraints for which Ktrans can be further associated with EFp and PS in the slow and rapid transit states, respectively.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80% of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Total variation based DCE-MRI decomposition by separating lesion from background for time-intensity curve estimation.

This study aims to obtain the accurate time intensity curve (TIC) of a dynamic contrast-enhanced magnetic resonance image (DCE-MRI) by eliminating the normal tissue enhancement and obtaining pure lesion information. The TIC of DCE-MRI is sometimes distorted because of the influence of normal tissue. In this paper, a new tracer-kinetic modeling based on total variation (DC-TV) is proposed to address this problem by decomposing the DCE-MRI into the normal tissue image and the lesion image. As TIC generation is not standardized and a credible program is expected, an accurate TIC generation is presented in this paper. We propose a new tracer-kinetic model DC-TV to decompose the lesion region in breast DCE-MRIs. The original image is decomposed into a normal tissue image and a lesion image to obtain the pure lesion enhancement information. The acquired lesion images are smooth and correspond to the diffusion of the contrast agent in the lesion. The normal tissue image sequences are stable and correspond to the enhanced normal tissue. To speed up the computational process of our convergent algorithm, the split Bregman iteration algorithm is applied. To compare the algorithm results, images generated by decomposed methods without normal tissue constraint based on total variation are compared with those generated by our method. The performance of the proposed method is evaluated by the correlation of normal tissue images with the lesion classification accuracy of lesion images. Ninety-eight lesions, including 40 benign and 58 malignant, are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinoma in situ, tubular carcinoma, phyllodes tumor, hyperplasia, and fibroadenoma, among others. The area under the ROC for the pure lesion enhancement images acquired by DC-TV is greater than that acquired by the original DCE-MRIs. The pure enhancement information from the original breast DCE-MRI lesions can be successfully obtained using our DC-TV. The TICs based on the acquired pure enhancement information closely conform to three-time-point model, which is a classic diagnosis rule. The experiment shows that DC-TV provide a credible TIC generation program.

Application on the quantitative perfusion parameters of dynamic contrast-enhanced MRI in the pathological subtype of uterine leiomyoma

Objective: To analyze the value of the quantitative perfusion parameters of dynamic contrast-enhanced MRI(DCE-MRI) in the pathological subtype of uterine leiomyoma. Methods: A total of 35 cases of uterine leiomyoma confirmed by surgery and pathology were retrospectively analyzed in Shaoxing People's Hospital from October 2015 to May 2016.All cases underwent DCE-MRI. Quantitative perfusion parameters were prospectively measured and analyzed, including transfer constant (K(trans)) , efflux rate constant (K(ep)), extravascular extracellular space volume ratio (V(e)), blood plasma volume ratio (V(p)), permeability surface area product (PS) and plasma flow (F(p)) , using signal-input two-compartment tracer kinetic models (Extended Tofts model and Exchange model) in 35 leiomyoma cases.After the operation, the 35 cases were divided into three groups according to the pathological classfication , the ordinary, the cellular and the degeneration type.To analyze the differences among the three groups about the quantitative perfusion parameters of uterine leiomyoma. Compared with the gold standard of pathological findings, the ROC curves were drawn to evaluate the diagnostic efficacy of different quantitative perfusion parameters. Results: In the ordinary, cellular and degeneration type of uterine leiomyoma, K(trans) value were respectively(0.684±0.341), (1.897±0.458), (0.554±0.514)/min; K(ep) were respectively(1.004±0.685), (2.362±1.001), (1.274±1.093)/min; V(e) were respectively 0.789%±0.186%, 0.806%±0.203%, 0.537%±0.314%; V(p) were respectively 0.145%±0.196%, 0.502%±0.338%, 0.062%±0.106% and F(p) were respectively(0.792±0.461), (2.426±0.509), (0.628±0.551)ml/min.Among three groups, the value of K(trans), K(ep), V(e), V(p) and F(p) had statistical difference(all P<0.05), the value of PS didn't have statistical difference. The value of K(trans), K(ep), V(p) and F(p) in cellular type were higher than the ordinary type(all P<0.01); the value of K(trans), V(p) and F(p) in cellular type were higher than the degeneration type(all P<0.01); the value of V(e) in ordinary type was higher than the degeneration type(P<0.05). The area under ROC curve was 0.981 for K(trans), 0.904 for K(ep), 0.622 for V(e), 0.840 for V(p) and 0.994 for F(p). Conclusion: The quantitative perfusion parameters of DCE-MRI , especially the value of K(trans), K(ep), V(p) and F(p) have a great diagnostic efficacy in the pathological classfication of uterine leiomyoma which will become a predictive factor of pathological classfication in uterine leiomyoma.

Quantification of Tumor Hypoxic Fractions Using Positron Emission Tomography with [18F]Fluoromisonidazole ([18F]FMISO) Kinetic Analysis and Invasive Oxygen Measurements

The purpose of this study is to use dynamic [18F]fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) to compare estimates of tumor hypoxic fractions (HFs) derived by tracer kinetic modeling, tissue-to-blood ratios (TBR), and independent oxygen (pO2) measurements. BALB/c mice with EMT6 subcutaneous tumors were selected for PET imaging and invasive pO2 measurements. Data from 120-min dynamic [18F]FMISO scans were fit to two-compartment irreversible three rate constant (K 1, k 2, k 3) and Patlak models (K i). Tumor HFs were calculated and compared using K i, k 3, TBR, and pO2 values. The clinical impact of each method was evaluated on [18F]FMISO scans for three non-small cell lung cancer (NSCLC) radiotherapy patients. HFs defined by TBR (≥1.2, ≥1.3, and ≥1.4) ranged from 2 to 85% of absolute tumor volume. HFs defined by K i (>0.004mlmincm-3) and k 3 (>0.008min-1) varied from 9 to 85%. HF quantification was highly dependent on metric (TBR, k 3, or K i) and threshold. HFs quantified on human [18F]FMISO scans varied from 38 to 67, 0 to 14, and 0.1 to 27%, for each patient, respectively, using TBR, k 3, and K i metrics. [18F]FMISO PET imaging metric choice and threshold impacts hypoxia quantification reliability. Our results suggest that tracer kinetic modeling has the potential to improve hypoxia quantification clinically as it may provide a stronger correlation with direct pO2 measurements.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7–8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of , the tracer transport rate (ml · min−1 · ml−1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI.

To evaluate an interleaved MRI sampling strategy that acquires both high temporal resolution (HTR) dynamic contrast-enhanced (DCE) data for quantifying breast tumor blood flow (TBF) and high spatial resolution (HSR) DCE data for clinical reporting, following a single standard injection of contrast agent. A simulation study was used to evaluate the performance of the interleaved technique under different conditions. In a prospective clinical study, 18 patients with primary breast cancer, who were due to undergo neoadjuvant chemotherapy (NACT), were examined using interleaved HTR and HSR DCE-MRI at 1.5 Tesla. Tumor regions of interest were analyzed with a two-compartment tracer kinetic model. Paired parameters (n = 10) from the data acquired before and post-cycle 2 of NACT were compared using the nonparametric Wilcoxon signed-rank test. Simulations demonstrated that TBF was reliably estimated using the proposed strategy. The region of interest analysis revealed significant changes in TBF (0.81-0.43 mL/min/mL; P = 0.002) following two cycles of NACT. The HSR data were reported in the normal way and enabled the assessment of tumor volume, which decreased by 53% following NACT (P = 0.065). TBF can be measured reliably using the proposed strategy without compromising a standard clinical protocol. Furthermore, in our feasibility study, TBF decreased significantly following NACT, whereas capillary permeability surface-area product did not. Magn Reson Med 79:317-326, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Quantification of Tau Load Using [18F]AV1451 PET

PurposeThe tau tracer [18F]AV1451, also known as flortaucipir, is a promising ligand for imaging tau accumulation in Alzheimer’s disease (AD). Most of the previous studies have quantified tau load using standardized uptake value ratios (SUVr) derived from a static [18F]AV1451 scan. SUVr may, however, be flow dependent and, especially for longitudinal studies, should be validated against a fully quantitative approach. The objective of this study was to identify the optimal tracer kinetic model for measuring tau load using [18F]AV1451.ProceduresFollowing intravenous injection of 225 ± 16 MBq [18F]AV1451, 130 min dynamic PET scans were performed in five biomarker confirmed AD patients and five controls. Arterial blood sampling was performed to obtain a metabolite-corrected plasma input function. Next, regional time–activity curves were generated using PVElab software. These curves were analysed using several pharmacokinetic models.ResultsThe reversible single tissue compartment model (1T2k_VB) was the preferred model for all but one control. For AD patients, however, model preference shifted towards a reversible two tissue compartmental model (2T4k_VB). The simplified reference tissue model (SRTM) derived binding potential (BPND) showed good correlation (AD: r2 = 0.87, slope = 1.06; controls: r2 = 0.87, slope = 0.86) with indirect plasma input binding (distribution volume ratio-1). Standardized uptake value ratios (80–100 min) correlated well with DVR (r2 = 0.93, slope = 1.07) and SRTM-derived BPND (r2 = 0.84, slope = 0.95). In addition, regional differences in tracer binding between subject groups in different tau-specific regions were observed.ConclusionsModel preference of [18F]AV1451 appears to depend on subject status and, in particular, VT. The relationship between model preference and VT suggests that (higher) tau load may be reflected by a second tissue compartment. Nevertheless, consistent results can be obtained using a 2T4k_VB model. In addition, SRTM can be used to derive BPND.

Joint reconstruction of dynamic PET activity and kinetic parametric images using total variation constrained dictionary sparse coding

Dynamic positron emission tomography (PET) is capable of providing both spatial and temporal information of radio tracers in vivo. In this paper, we present a novel joint estimation framework to reconstruct temporal sequences of dynamic PET images and the coefficients characterizing the system impulse response function, from which the associated parametric images of the system macro parameters for tracer kinetics can be estimated. The proposed algorithm, which combines statistical data measurement and tracer kinetic models, integrates a dictionary sparse coding (DSC) into a total variational minimization based algorithm for simultaneous reconstruction of the activity distribution and parametric map from measured emission sinograms. DSC, based on the compartmental theory, provides biologically meaningful regularization, and total variation regularization is incorporated to provide edge-preserving guidance. We rely on techniques from minimization algorithms (the alternating direction method of multipliers) to first generate the estimated activity distributions with sub-optimal kinetic parameter estimates, and then recover the parametric maps given these activity estimates. These coupled iterative steps are repeated as necessary until convergence. Experiments with synthetic, Monte Carlo generated data, and real patient data have been conducted, and the results are very promising.

Dynamic multi-echo DCE- and DSC-MRI in rectal cancer: Low primary tumor Ktrans and ΔR2* peak are significantly associated with lymph node metastasis

To implement a dynamic contrast-based multi-echo MRI sequence in assessment of rectal cancer and evaluate associations between histopathologic data and the acquired dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) -MRI parameters. This pilot study reports results from 17 patients with resectable rectal cancer. Dynamic contrast-based multi-echo MRI (1.5T) was acquired using a three-dimensional multi-shot EPI sequence, yielding both DCE- and DSC-data following a single injection of contrast agent. The Institutional Review Board approved the study and all patients provided written informed consent. Quantitative analysis was performed by pharmacokinetic modeling on DCE data and tracer kinetic modeling on DSC data. Mann-Whitney U-test and receiver operating characteristics curve statistics was used to evaluate associations between histopathologic data and the acquired DCE- and DSC-MRI parameters. For patients with histologically confirmed nodal metastasis, the primary tumor demonstrated a significantly lower Ktrans and peak change in R2*, R2*-peakenh , than patients without nodal metastasis, showing a P-value of 0.010 and 0.005 for reader 1, and 0.043 and 0.019 for reader 2, respectively. This study shows the feasibility of acquiring DCE- and DSC-MRI in rectal cancer by dynamic multi-echo MRI. A significant association was found between both Ktrans and R2*-peakenh in the primary tumor and histological nodal status of the surgical specimen, which may improve stratification of patients to intensified multimodal treatment. 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:194-206.

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

The purpose of this work was to develop and evaluate a T1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.