Positron Emission Tomography Data Sets Research Articles

Retrospective data-driven respiratory gating for PET using TOF information.

Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.

Joint image reconstruction and segmentation using the Potts model

We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp–Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.

Surface-based partial-volume correction for high-resolution PET

Tissue radioactivity concentrations, measured with positron emission tomography (PET) are subject to partial volume effects (PVE) due to the limited spatial resolution of the scanner. Last generation high-resolution PET cameras with a full width at half maximum (FWHM) of 2–4mm are less prone to PVEs than previous generations. Corrections for PVEs are still necessary, especially when studying small brain stem nuclei or small variations in cortical neuroreceptor concentrations which may be related to cytoarchitectonic differences. Although several partial-volume correction (PVC) algorithms exist, these are frequently based on a priori assumptions about tracer distribution or only yield corrected values of regional activity concentrations without providing PVE corrected images. We developed a new iterative deconvolution algorithm (idSURF) for PVC of PET images that aims to overcome these limitations by using two innovative techniques: 1) the incorporation of anatomic information from a cortical gray matter surface representation, extracted from magnetic resonance imaging (MRI) and 2) the use of anatomically constrained filtering to attenuate noise. PVE corrected images were generated with idSURF implemented into a non-interactive processing pipeline. idSURF was validated using simulated and clinical PET data sets and compared to a frequently used standard PVC method (Geometric Transfer Matrix: GTM). The results on simulated data sets show that idSURF consistently recovers accurate radiotracer concentrations within 1–5% of true values. Both radiotracer concentrations and non-displaceable binding potential (BPnd) values derived from clinical PET data sets with idSURF were highly correlated with those obtained with the standard PVC method (R2=0.99, error=0%–3.2%). These results suggest that idSURF is a valid and potentially clinically useful PVC method for automatic processing of large numbers of PET data sets.

FDG-PET/CT imaging for tumor staging and definition of tumor volumes in radiation treatment planning in non-small cell lung cancer.

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) has the potential to improve the staging and radiation treatment (RT) planning of various tumor sites. However, from a clinical standpoint, questions remain with regard to what extent PET/CT changes the target volume and whether PET/CT reduces interobserver variability in target volume delineation. The present study analyzed the use of FDG-PET/CT images for staging and evaluated the impact of FDG-PET/CT on the radiotherapy volume delineation compared with CT in patients with non-small cell lung cancer (NSCLC) who were candidates for radiotherapy. Intraobserver variation in delineating tumor volumes was also observed. In total, 23 patients with stage I-III NSCLC were enrolled and treated with fractionated RT-based therapy with or without chemotherapy. FDG-PET/CT scans were acquired within two weeks prior to RT. PET and CT data sets were sent to the treatment planning system, Pinnacle, through compact discs. The CT and PET images were subsequently fused by means of a dedicated RT planning system. Gross tumor volume (GTV) was contoured by four radiation oncologists on CT (GTV-CT) and PET/CT images (GTV-PET/CT). The resulting volumes were analyzed and compared. For the first phase, two radiation oncologists outlined the contours together, achieving a final consensus. Based on PET/CT, changes in tumor-node-metastasis categories occurred in 8/23 cases (35%). Radiation targeting with fused FDG-PET and CT images resulted in alterations in radiation therapy planning in 12/20 patients (60%) in comparison with CT targeting. The most prominent changes in GTV were observed in cases with atelectasis. For the second phase, the variation in delineating tumor volumes was assessed by four observers. The mean ratio of largest to smallest CT-based GTV was 2.31 (range, 1.01–5.96). The addition of the PET results reduced the mean ratio to 1.46 (range, 1.02–2.27). PET/CT fusion images may have a potential impact on tumor staging and treatment planning. Implementing matched PET/CT results reduced observer variation in delineating tumor volumes significantly with respect to CT only.

PET/CT-guided percutaneous liver mass biopsies and ablations: Targeting accuracy of a single 20 s breath-hold PET acquisition

To determine whether a single 20 s breath-hold positron-emission tomography (PET) acquisition obtained during combined PET/computed tomography (CT)-guided percutaneous liver biopsy or ablation procedures has the potential to target 2-[(18)F]-fluoro-2-deoxy-d-glucose (FDG)-avid liver masses as accurately as up to 180 s breath-hold PET acquisitions. This retrospective study included 10 adult patients with 13 liver masses who underwent FDG PET/CT-guided percutaneous biopsies (n = 5) or ablations (n = 5). PET was acquired as nine sequential 20 s, monitored, same-level breath-hold frames and CT was acquired in one monitored breath-hold. Twenty, 40, 60, and 180 s PET datasets were reconstructed. Two blinded readers marked tumour centres on randomized PET and CT datasets. Three-dimensional spatial localization differences between PET datasets and either 180 s PET or CT were analysed using multiple regression analyses. Statistical tests were two-sided and p < 0.05 was considered significant. Targeting differences between 20 s PET and 180 s PET ranged from 0.7-20.3 mm (mean 5.3 ± 4.4 mm; median 4.3) and were not statistically different from 40 or 60 s PET (p = 0.74 and 0.91, respectively). Targeting differences between 20 s PET and CT ranged from 1.4-36 mm (mean 9.6 ± 7.1 mm; median 8.2 mm) and were not statistically different from 40, 60, or 180 s PET (p = 0.84, 0.77, and 0.35, respectively). Single 20 s breath-hold PET acquisitions from PET/CT-guided percutaneous liver procedures have the potential to target FDG-avid liver masses with equivalent accuracy to 180 s summed, breath-hold PET acquisitions and may facilitate strategies that improve image registration and shorten procedure times.

Influence and Compensation of Truncation Artifacts in MR-Based Attenuation Correction in PET/MR

The goal of this article is to quantify the influence of truncation artifacts in the magnetic resonance (MR)-based attenuation map (MRMap) on reconstructed positron emission tomography (PET) image volumes and to propose a new method for minimizing this influence. PET data sets of 20 patients investigated in a Philips Ingenuity PET/MR were reconstructed with and without applying two different methods for truncation compensation (TC1 vendor-provided, TC2 newly developed). In this patient group, the extent of truncation artifacts and quality of the truncation compensation (TC) was assessed visually in the MRMaps. In three additional patients MRMaps generated by algorithm TC2 could be compared to the ground truth of transmission-based attenuation maps obtained with a Siemens ECAT HR(+) scanner. The influence of truncation on regional SUVs in lesions, other hot structures (bladder, kidney, myocardium) and the arms was assessed in suitable volume of interests (VOI). Truncation compensated MRMaps exhibited residual artifacts in the arms in 16 patients for algorithm TC1 and to a lesser extent in eight patients for algorithm TC2. Compared to the transmission-based attenuation maps algorithm TC2 slightly overestimated the size of the truncated arms by 0.3 cm in the radial direction. Without truncation compensation, VOIs located in the trunk showed an average SUVmax underestimation of less than 5.4% relative to the results obtained with TC2. Inside the patients' arms underestimations up to 46.5% were found. In the trunk, standardized uptake values (SUV) underestimations due to truncation artifacts in the MRMap are rather small. Inside the arms, severe SUV underestimations can occur. Therefore, reliable TC is mandatory and can be achieved by applying the newly developed algorithm TC2 which has yielded promising results so far. Implementation of the proposed method is straightforward and should be easily adaptable to other PET/MR systems.

A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning

Background. Combined positron emission tomography (PET)/magnetic resonance imaging (MRI) is highly promising for biologically individualized radiotherapy (RT). Hence, the purpose of this work was to develop an accurate and robust registration strategy to integrate combined PET/MR data into RT treatment planning. Material and methods. Eight patient datasets consisting of an FDG PET/computed tomography (CT) and a subsequently acquired PET/MR of the head and neck (HN) region were available. Registration strategies were developed based on CT and MR data only, whereas the PET components were fused with the resulting deformation field. Following a rigid registration, deformable registration was performed with a transform parametrized by B-splines. Three different optimization metrics were investigated: global mutual information (GMI), GMI combined with a bending energy penalty (BEP) for regularization (GMI+ BEP) and localized mutual information with BEP (LMI+ BEP). Different quantitative registration quality measures were developed, including volumetric overlap and mean distance measures for structures segmented on CT and MR as well as anatomical landmark distances. Moreover, the local registration quality in the tumor region was assessed by the normalized cross correlation (NCC) of the two PET datasets. Results. LMI+ BEP yielded the most robust and accurate registration results. For GMI, GMI+ BEP and LMI+ BEP, mean landmark distances (standard deviations) were 23.9 mm (15.5 mm), 4.8 mm (4.0 mm) and 3.0 mm (1.0 mm), and mean NCC values (standard deviations) were 0.29 (0.29), 0.84 (0.14) and 0.88 (0.06), respectively. Conclusion. Accurate and robust multimodal deformable image registration of CT and MR in the HN region can be performed using a B-spline parametrized transform and LMI+ BEP as optimization metric. With this strategy, biologically individualized RT based on combined PET/MRI in terms of dose painting is possible.

Introduction to the analysis of PET data in oncology

Several reviews on specific topics related to positron emission tomography (PET) ranging in complexity from introductory to highly technical have already been published. This introduction to the analysis of PET data was written as a simple guide of the different phases of analysis of a given PET dataset, from acquisition to preprocessing, to the final data analysis. Although sometimes issues specific to PET in neuroimaging will be mentioned for comparison, most of the examples and applications provided will refer to oncology. Due to the limitations of space we couldn't address each issue comprehensively but, rather, we provided a general overview of each topic together with the references that the interested reader should consult. We will assume a familiarity with the basic principles of PET imaging.

Cerebrovascular Occlusive Disease: Quantitative Cerebral Blood Flow Using Dynamic Susceptibility Contrast MR Imaging Correlates with Quantitative H2[15O] PET

To compare quantitative values of cerebral blood flow (CBF) derived from dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging with reference standard positron emission tomography (PET) in patients with confirmed cerebrovascular occlusive disease. Local institutional review board approval and informed consent were obtained for a prospective study of 18 patients (six men, 12 women; age range, 28-71 years; mean age, 45 years ± 10.4 [standard deviation]) with angiographically confirmed Moyamoya (n = 8) or internal carotid artery occlusions (n = 10). DSC MR images and oxygen 15-labeled water (H(2)[(15)O]) PET images were acquired on the same day. DSC images were postprocessed to yield parametric images of CBF (in mL/100 g/min), coregistered, and analyzed using grid-based regions of interest. Mean values of CBF in each region of interest from MR imaging and PET data sets were compared. Correlations for each patient were determined and overall agreement between pooled MR imaging and PET CBF was reported using linear regression analysis and Bland-Altman plots. Strong correlations (r(2) ≥ 0.55) were found between MR imaging and PET CBF values in all patients. Use of the bookend approach was found to underestimate CBF predictably across the patient cohort (mean slope, 0.82; standard deviation, 0.18; slope of aggregated data, 0.75). This allowed for a simple rescaling of MR imaging values producing strong agreement with PET values in the aggregated data (r(2) = 0.66; slope = 1.00; intercept = 0.00). The data show that the bookend MR imaging technique produces similar results for quantitative CBF between DSC MR imaging and H(2)[(15)O] PET. Although MR-derived CBF underestimated PET-derived CBF, the patient-to-patient variability in the slopes of the linear MR and PET relationships was significantly smaller than a competing quantitation technique. As a result, the bookend technique appears to more predictably measure quantitative CBF in a clinical setting.

Four-dimensional (4D) Motion Detection to Correct Respiratory Effects in Treatment Response Assessment Using Molecular Imaging Biomarkers

Observing early metabolic changes in positron emission tomography (PET) is an essential tool to assess treatment efficiency in radiotherapy. However, for thoracic regions, the use of three-dimensional (3D) PET imaging is unfeasible because the radiotracer activity is smeared by the respiratory motion and averaged during the imaging acquisition process. This motion-induced degradation is similar in magnitude with the treatment-induced changes, and the two occurrences become indiscernible. We present a customized temporal-spatial deformable registration method for quantifying respiratory motion in a four-dimensional (4D) PET dataset. Once the motion is quantified, a motion-corrected (MC) dataset is created by tracking voxels to eliminate breathing-induced changes in the 4D imaging scan. The 4D voxel-tracking data is then summed to yield a 3D MC-PET scan containing only treatment-induced changes. This proof of concept is exemplified on both phantom and clinical data, where the proposed algorithm tracked the trajectories of individual points through the 4D datasets reducing motion to less than 4 mm in all phases. This correction approach using deformable registration can discern motion blurring from treatment-induced changes in treatment response assessment using PET imaging.

Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.

Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification

The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.

Whole-body MR/PET: applications in abdominal imaging

Over the last few decades it has been shown that novel technologies and technological progress rapidly change the working environment of radiologists and nuclear medicine physicians. Thus, new possibilities, e.g., in tumor staging and therapy monitoring, but also new challenges arise. Recently, it could be shown that the integration of magnetic resonance imaging (MRI) and positron emission tomography (PET) is technically possible. The evolvement of new dedicated hybrid MR/PET systems for whole-body imaging in humans offers new potential in multimodal imaging. Especially simultaneous measurement of PET and MRI datasets allows for insights in metabolic and functional processes, particularly in oncologic demands, but also in cardiovascular and cerebral imaging. In this work-in-progress review article, a technical summary including the method-inherent challenges are given. Furthermore, possible clinical applications and research interests are addressed.

Minimizing Image Misregistration during PET/CT–guided Percutaneous Interventions with Monitored Breath-hold PET and CT Acquisitions

Purpose To validate a monitored, breath-hold positron emission tomography (PET)/computed tomography (CT) acquisition technique for the minimization of respiratory PET/CT image misregistration and lesion distortion during PET/CT–guided percutaneous interventional procedures. Materials and Methods Eleven patients referred for percutaneous biopsy or thermal ablation of tumors near the diaphragm were prospectively enrolled. Initial PET/CT scanning was performed by using a bellows device and monitored, same-level breath-holds for PET and CT acquisitions. Breath-hold PET consisted of nine 20-second breath-hold frames, yielding a 3-minute equivalent PET dataset. A second PET/CT scan was obtained without monitoring by using end-expiration breath-hold CT and free-breathing PET. PET/CT tumor misregistration and craniocaudal tumor diameter were measured on monitored and unmonitored PET/CT datasets. Data were analyzed by using nonparametric, two-sided, signed-rank statistical tests. Results Mean PET/CT image misregistrations in the craniocaudal, anteroposterior, and transverse planes were 2.6 mm (range, 0–7 mm), 3.3 mm (range, 1–8 mm), and 2.7 mm (range, 0–8 mm) with monitoring and 14.7 mm (range, 0–49 mm), 7.6 mm (range, 1–24 mm), and 4.0 mm (range, 0–12 mm) without monitoring, respectively. Differences were significant for craniocaudal ( P = .0087) and anteroposterior ( P = .014) planes, but not for the transverse plane ( P = .23). Mean craniocaudal target diameter was 2.5 mm (range, −2 to 9 mm) larger (ie, distorted) for unmonitored versus monitored PET ( P = .061). Conclusions Acquiring PET/CT datasets with respiratory bellows-assisted, monitored breath-holds improves PET/CT image registration versus unmonitored PET/CT and may facilitate accurate targeting during PET/CT–guided interventions in anatomic regions subject to respiratory motion.

Analytical model for long-distance tracer-transport in plants

Recent investigations of long-distance transport in plants using non-invasive tracer techniques such as 11C radiolabeling monitored by positron emission tomography (PET) combined with magnetic resonance imaging (MRI) revealed the need of dedicated methods to allow a quantitative data analysis and comparison of such experiments. A mechanistic compartmental tracer transport model is presented, defined by a linear system of partial differential equations (PDEs). This model simplifies the complexity of axial transport and lateral exchanges in the transport pathways of plants (e.g. the phloem) by simulating transport and reversible exchange within three compartments using just a few parameters which are considered to be constant in space and time. For this system of PDEs an analytical solution in Fourier-space was found allowing a fast and numerically precise evaluation. From the steady-state behavior of the model, the system loss (steadily fixed tracer along the transport conduits) was derived as an additional parameter that can be readily interpreted in a physiological way. The presented framework allows the model to be fitted to spatio-temporal tracer profiles including error and sensitivity analysis of the estimated parameters. This is demonstrated for PET data sets obtained from radish, sugar beet and maize plants.

Die Rolle der PET bei der bildgebenden Untersuchung von Patienten mit malignen Lymphomen

Positron-Emission-Tomography (PET) has emerged as a diagnostic gold standard for most tumor entities during the last 20 years, especially for patients suffering from malignant lymphoma. The development and distribution of machines allowing for hybrid imaging, i.e. the simultaneous acquisition of PET and CT datasets, and the possibility to assess even small pathologic findings with fused PET/CT image visualization, once more significantly improved the diagnostic accuracy of PET. Based on an excellent sensitivity the metabolic imaging with PET or PET/CT allows for a reliable overall assessment of patients with malignant lymphoma before therapy, for the early identification of non-responders during therapy, and for the diagnosis of relapse after therapy.

Bimodal MR–PET Agent for Quantitative pH Imaging

Bimodal MR–PET Agent for Quantitative pH Imaging

FDG-PET/CT imaging for staging and definition of tumor volumes in radiation treatment planning in non-small cell lung cancer

7574 Background: 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) /computed tomography (CT) has a potential improvement for staging and radiation treatment (RT) planning of various tumor sites. But from a clinical standpoint, the open questions are essentially the following: to what extent does PET/CT change the target volume? Can PET/CT reduce inter-observer variability in target volume delineation? We analyzed the use of FDG-PET/ CT images for staging and evaluated the impact of FDG- PET/CT on the radiotherapy volume delineation compared with CT in patients with non-small cell lung cancer (NSCLC) candidates for radiotherapy. Intraobserver variation in delineating tumor volumes was also observed. Methods: Twenty-three patients with stage I-III NSCLC were enrolled in this pilot study and were treated with fractionated RT based therapy with or without chemotherapy. FDG-PET/CT scans were acquired within 2 weeks prior to RT. PET and CT data sets were sent to the treatment planning system Pinnacle through compact disc. The CT and PET images were subsequently fused by means of a dedicated radiation treatment planning system. Gross Tumor Volume (GTV) was contoured by four radiation oncologists respectively on CT (CT-GTV) and PET/CT images (PET/CT-GTV). The resulting volumes were analyzed and compared. Results: For the first phase, two radiation oncologists outlined together the contours achieving a final consensus. Based on PET/CT, changes in TNM categories occurred in 8/23 cases (35%). Radiation targeting with fused FDG-PET and CT images resulted in alterations in radiation therapy planning in 12/20 patients (60%) by comparison with CT targeting. The most prominent changes in GTV have been observed in cases with atelectasis. For the second phase was four intraobserver variation in delineating tumor volumes. The mean ratio of largest to smallest CT-based GTV was 2.31 (range 1.01–5.96). The addition of the PET data reduced the mean ratio to 1.46 (range 1.12–2.27). Conclusions: PET/CT fusion images could have a potential impact on both tumor staging and treatment planning. Implementing matched PET/CT reduced observer variation in delineating tumor volumes significantly with respect to CT only. [Table: see text]

Men versus women on sexual brain function: Prominent differences during tactile genital stimulation, but not during orgasm

Biological differences in male and female sexuality are obvious in the behavioral domain, but the central mechanisms that might explain these behavioral gender differences remain unclear. In this study, we merged two earlier positron emission tomography data sets to enable systematic comparison of the brain responses in heterosexual men and women during sexual tactile genital (penile and clitoral) stimulation and during orgasm. Gender commonalities were most evident during orgasm, a phase which demonstrated activations in the anterior lobe of the cerebellar vermis and deep cerebellar nuclei, and deactivations in the left ventromedial and orbitofrontal cortex in both men and women. During tactile genital stimulation, deactivations in the right amygdala and left fusiform gyrus were found for both genders. Marked gender differences were seen during this phase: left fronto-parietal areas (motor cortices, somatosensory area 2 and posterior parietal cortex) were activated more in women, whereas in men, the right claustrum and ventral occipitotemporal cortex showed larger activation. The only prominent gender difference during orgasm was male-biased activation of the periaqueductal gray matter. From these results, we conclude that during the sexual act, differential brain responses across genders are principally related to the stimulatory (plateau) phase and not to the orgasmic phase itself. These results add to a better understanding of the neural underpinnings of human sexuality, which might benefit treatment of psychosexual disorders.

An Activity-Subspace Approach for Estimating the Integrated Input Function and Relative Distribution Volume in PET Parametric Imaging

Dynamic positron emission tomography (PET) imaging technique enables the measurement of neuroreceptor distributions corresponding to anatomic structures, and thus, allows image-wide quantification of physiological and biochemical parameters. Accurate quantification of the concentration of neuroreceptor has been the objective of many research efforts. Compartment modeling is the most widely used approach for receptor binding studies. However, current compartment-model-based methods often either require intrusive collection of accurate arterial blood measurements as the input function, or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function. We propose a novel concept of activity subspace, and estimate the input function by the analysis of the intersection of the activity subspaces. Then, the input function and the distribution volume (DV) parameter are refined and estimated iteratively. Thus, the underlying parametric image of the total DV is obtained. The proposed method is compared with a blind estimation method, iterative quadratic maximum-likelihood (IQML) via simulation, and the proposed method outperforms IQML. The proposed method is also evaluated in a brain PET dataset.