Dose reduction in dynamic synaptic vesicle glycoprotein 2A PET imaging using artificial neural networks

Objective. Reducing dose in positron emission tomography (PET) imaging increases noise in reconstructed dynamic frames, which inevitably results in higher noise and possible bias in subsequently estimated images of kinetic parameters than those estimated in the standard dose case. We report the development of a spatiotemporal denoising technique for reduced-count dynamic frames through integrating a cascade artificial neural network (ANN) with the highly constrained back-projection (HYPR) scheme to improve low-dose parametric imaging.Approach. We implemented and assessed the proposed method using imaging data acquired with11C-UCB-J, a PET radioligand bound to synaptic vesicle glycoprotein 2A (SV2A) in the human brain. The patch-based ANN was trained with a reduced-count frame and its full-count correspondence of a subject and was used in cascade to process dynamic frames of other subjects to further take advantage of its denoising capability. The HYPR strategy was then applied to the spatial ANN processed image frames to make use of the temporal information from the entire dynamic scan.Main results. In all the testing subjects including healthy volunteers and Parkinson's disease patients, the proposed method reduced more noise while introducing minimal bias in dynamic frames and the resulting parametric images, as compared with conventional denoising methods.Significance. Achieving 80% noise reduction with a bias of -2% in dynamic frames, which translates into 75% and 70% of noise reduction in the tracer uptake (bias, -2%) and distribution volume (bias, -5%) images, the proposed ANN+HYPR technique demonstrates the denoising capability equivalent to a 11-fold dose increase for dynamic SV2A PET imaging with11C-UCB-J.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Total-Body Perfusion Imaging with [11C]-Butanol

Tissue perfusion can be affected by physiology or disease. With the advent of total-body PET, quantitative measurement of perfusion across the entire body is possible. [11C]-butanol is a perfusion tracer with a superior extraction fraction compared with [15O]-water and [13N]-ammonia. To develop the methodology for total-body perfusion imaging, a pilot study using [11C]-butanol on the uEXPLORER total-body PET/CT scanner was conducted. Methods: Eight participants (6 healthy volunteers and 2 patients with peripheral vascular disease [PVD]) were injected with a bolus of [11C]-butanol and underwent 30-min dynamic acquisitions. Three healthy volunteers underwent repeat studies at rest (baseline) to assess test-retest reproducibility; 1 volunteer underwent paired rest and cold pressor test (CPT) studies. Changes in perfusion were measured in the paired rest-CPT study. For PVD patients, local changes in perfusion were investigated and correlated with patient medical history. Regional and parametric kinetic analysis methods were developed using a 1-tissue compartment model and leading-edge delay correction. Results: Estimated baseline perfusion values ranged from 0.02 to 1.95 mL·min-1·cm-3 across organs. Test-retest analysis showed that repeat baseline perfusion measurements were highly correlated (slope, 0.99; Pearson r = 0.96, P < 0.001). For the CPT subject, the largest regional increases were in skeletal muscle (psoas, 142%) and the myocardium (64%). One of the PVD patients showed increased collateral vessel growth in the calf because of a peripheral stenosis. Comorbidities including myocardial infarction, hypothyroidism, and renal failure were correlated with variations in organ-specific perfusion. Conclusion: This pilot study demonstrates the ability to obtain reproducible measurements of total-body perfusion using [11C]-butanol. The methods are sensitive to local perturbations in flow because of physiologic stressors and disease.

High-Temporal-Resolution Lung Kinetic Modeling Using Total-Body Dynamic PET with Time-Delay and Dispersion Corrections

Tracer kinetic modeling in dynamic PET has the potential to improve the diagnosis, prognosis, and research of lung diseases. The advent of total-body PET systems with much greater detection sensitivity enables high-temporal-resolution (HTR) dynamic PET imaging of the lungs. However, existing models may become insufficient for modeling the HTR data. In this paper, we investigate the necessity of additional corrections to the input function for HTR lung kinetic modeling. Methods: Dynamic scans with HTR frames of as short as 1 s were performed on 13 healthy subjects with a bolus injection of about [Formula: see text] of 18F-FDG using the uEXPLORER total-body PET/CT system. Three kinetic models with and without time-delay and dispersion corrections were compared for the quality of lung time-activity curve fitting using the Akaike information criterion. The impact on quantification of 18F-FDG delivery rate [Formula: see text], net influx rate [Formula: see text] and fractional blood volume [Formula: see text] was assessed. Parameter identifiability analysis was also performed to evaluate the reliability of kinetic quantification with respect to noise. Correlation of kinetic parameters with age was investigated. Results: HTR dynamic imaging clearly revealed the rapid change in tracer concentration in the lungs and blood supply (i.e., the right ventricle). The uncorrected input function led to poor time-activity curve fitting and biased quantification in HTR kinetic modeling. The fitting was improved by time-delay and dispersion corrections. The proposed model resulted in an approximately 85% decrease in [Formula: see text], an approximately 75% increase in [Formula: see text], and a more reasonable [Formula: see text] (∼0.14) than the uncorrected model (∼0.04). The identifiability analysis showed that the proposed models had good quantification stability for [Formula: see text], [Formula: see text], and [Formula: see text] The [Formula: see text] estimated by the proposed model with simultaneous time-delay and dispersion corrections correlated inversely with age, as would be expected. Conclusion: Corrections to the input function are important for accurate lung kinetic analysis of HTR dynamic PET data. The modeling of both delay and dispersion can improve model fitting and significantly impact quantification of [Formula: see text], [Formula: see text], and [Formula: see text].

Functional PET Neuroimaging in Consciousness Evaluation: Study Protocol

Ensuring a robust and reliable evaluation of coma deepness and prognostication of neurological outcome is challenging. We propose to develop PET neuroimaging as a new diagnostic and prognosis tool for comatose patients using a recently published methodology to perform functional PET (fPET). This exam permits the quantification of task-specific changes in neuronal metabolism in a single session. The aim of this protocol is to determine whether task-specific changes in glucose metabolism during the acute phase of coma are able to predict recovery at 18 months. Participation will be proposed for all patients coming for a standard PET-CT in our center in order to evaluate global cerebral metabolism during the comatose state. Legally appointed representative consent will be obtained to slightly modify the exam protocol: (1) ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) bolus plus continuous infusion instead of a simple bolus and (2) more time under camera to perform dynamic acquisition. Participants will undergo a 55-min fPET session with a 20% bolus + 80% infusion protocol. Two occurrences of three block (5-min rest, 10-min auditory stimulation and 10-min emotional auditory stimulation) will be performed after reaching equilibrium of FDG arterial concentration. We will compare the regional brain metabolism at rest and during the sessions of auditory and emotional auditory stimulation to search for a determinant of coma recovery (18 months of follow-up after the exam). Emotional auditory stimulation should induce an activation of: the auditory cortex, the consciousness areas and the neural circuitry for emotion (function to coma deepness). An activation analysis will be carried out to highlight regional brain activation using dedicated custom-made software based on Python statistical and image processing toolboxes. The association between activation levels and the Coma Recovery Scale-Revisited (CRS-R) will be assessed using multivariate analysis. If successful, the results from this study will help improve coma prognosis evaluation based on the pattern of neuronal metabolism at the onset of the pathology. The study protocol, rationale and methods are described in this paper.

Diagnostic Performance of Dynamic Whole-Body Patlak [18F]FDG-PET/CT in Patients with Indeterminate Lung Lesions and Lymph Nodes

BACKGROUND

Static [¹⁸F]FDG-PET/CT is the imaging method of choice for the evaluation of indeterminate lung lesions and NSCLC staging; however, histological confirmation of PET-positive lesions is needed in most cases due to its limited specificity. Therefore, we aimed to evaluate the diagnostic performance of additional dynamic whole-body PET.

METHODS

A total of 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent static (60 min p.i.) and dynamic (0-60 min p.i.) whole-body [¹⁸F]FDG-PET/CT (300 MBq) using the multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modeling factors were calculated using a two-compartment linear Patlak model (FDG influx rate constant = Ki, metabolic rate = MR-FDG, distribution volume = DV-FDG) and compared to SUV using ROC analysis.

RESULTS

MR-FDG_mean provided the best discriminatory power between benign and malignant lung lesions with an AUC of 0.887. The AUC of DV-FDG_mean (0.818) and SUV_mean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDG_mean (0.987) and SUV_mean (0.993) were comparable. Moreover, the DV-FDG_mean in liver metastases was three times higher than in bone or lung metastases.

CONCLUSIONS

Metabolic rate quantification was shown to be a reliable method to detect malignant lung tumors, LNM, and distant metastases at least as accurately as the established SUV or dual-time-point PET scans.

Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic positron emission tomography

BACKGROUND

Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial blood sampling.

PURPOSE

The aim of this study was to (A) optimize the geometry for a novel and cost efficient non-invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and (B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venous component.

METHODS

The NID was optimized by using an in-house Geant4-based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1 mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated: ¹⁸ F, ¹¹ C, ¹⁵ O, and ⁶⁸ Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half maximum (FWHM) of the signal. In addition, contribution by the annihilation photons versus positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the total signal.

RESULTS

The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for ¹¹ C simulation. All simulations had a significant annihilation photon contribution. The signal for ¹⁸ F and ¹¹ C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will be required.

CONCLUSIONS

The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non-invasively for dPET scans.

Static and Dynamic 68Ga-FAPI PET/CT for the Detection of Malignant Transformation of Intraductal Papillary Mucinous Neoplasia of the Pancreas

Pancreatic ductal adenocarcinoma (PDAC) may arise from intraductal papillary mucinous neoplasms (IPMN) with malignant transformation, but a significant portion of IPMN remains to show benign behavior. Therefore, it is important to differentiate between benign IPMN and IPMN lesions undergoing malignant transformation. However, nonoperative differentiation by ultrasound, CT, MRI, and carbohydrate antigen 19-9 (CA19-9) is still unsatisfactory. Here, we assessed the clinical feasibility of additional assessment of malignancy by PET using ⁶⁸Ga-labeled fibroblast activation protein inhibitors (⁶⁸Ga-FAPI PET) in 25 patients with MRI- or CT-proven cystic pancreatic lesions. Methods: Twenty-five patients with cystic pancreatic lesions who were followed up in the European Pancreas Center of Heidelberg University hospital and who were led to surgical resection or fine-needle aspiration due to suspicious clinical, laboratory chemistry, or radiologic findings were examined by static (all patients) and dynamic (20 patients) ⁶⁸Ga-FAPI PET. Cystic pancreatic lesions were delineated and SUV_max and SUV_mean were determined. Time-activity curves and dynamic parameters (time to peak, K ₁, k ₂, K3, k ₄) were extracted from dynamic PET data. Receiver-operating curves of static and dynamic PET parameters were calculated. Results: Eleven of the patients had menacing IPMN (high-grade IPMN with [6 cases] or without [5 cases] progression into PDAC) and 11 low-grade IPMN; 3 patients had other benign entities. Menacing IMPN showed significantly elevated ⁶⁸Ga-FAPI uptake compared with low-grade IPMN and other benign cystic lesions. In dynamic imaging, menacing IPMN showed increasing time-activity curves followed by slow decrease afterward; time-activity curves of low-grade IPMN showed an immediate peak followed by rapid decrease for about 10 min and slower decrease for the rest of the time. Receiver-operating curves showed high sensitivity and specificity (area under the curve greater than 80%) of static and dynamic PET parameters for the differentiation of IPMN subtypes. Conclusion: ⁶⁸Ga-FAPI PET is a helpful new tool for the differentiation of menacing and low-grade IPMN and shows the potential to avoid unnecessary surgery for nonmalignant pancreatic IPMN.

Efficient Delay Correction for Total-Body PET Kinetic Modeling Using Pulse Timing Methods

Quantitative kinetic modeling requires an input function. A noninvasive image-derived input function (IDIF) can be obtained from dynamic PET images. However, a robust IDIF location (e.g., aorta) may be far from a tissue of interest, particularly in total-body PET, introducing a time delay between the IDIF and the tissue. The standard practice of joint estimation (JE) of delay, along with model fitting, is computationally expensive. To improve the efficiency of delay correction for total-body PET parametric imaging, this study investigated the use of pulse timing methods to estimate and correct for delay. Methods: Simulation studies were performed with a range of delay values, frame lengths, and noise levels to test the tolerance of 2 pulse timing methods-leading edge (LE) and constant fraction discrimination and their thresholds. The methods were then applied to data from 21 subjects (14 healthy volunteers, 7 cancer patients) who underwent a 60-min dynamic total-body ¹⁸F-FDG PET acquisition. Region-of-interest kinetic analysis was performed and parametric images were generated to compare LE and JE methods of delay correction, as well as no delay correction. Results: Simulations demonstrated that a 10% LE threshold resulted in biases and SDs at tolerable levels for all noise levels tested, with 2-s frames. Pooled region-of-interest-based results (n = 154) showed strong agreement between LE (10% threshold) and JE methods in estimating delay (Pearson r = 0.96, P < 0.001) and the kinetic parameters v_b (r = 0.96, P < 0.001), K_i (r = 1.00, P < 0.001), and K₁ (r = 0.97, P < 0.001). When tissues with minimal delay were excluded from pooled analyses, there were reductions in v_b (69.4%) and K₁ (4.8%) when delay correction was not performed. Similar results were obtained for parametric images; additionally, lesion K_i contrast was improved overall with LE and JE delay correction compared with no delay correction and Patlak analysis. Conclusion: This study demonstrated the importance of delay correction in total-body PET. LE delay correction can be an efficient surrogate for JE, requiring a fraction of the computational time and allowing for rapid delay correction across more than 10⁶ voxels in total-body PET datasets.

Assessment of left and right ventricular functional parameters using dynamic dual-tracer [13N]NH3 and [18F]FDG PET/MRI

BACKGROUND

Cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) can assess various cardiovascular diseases. In this study, we intra-individually compared right (RV) and left ventricular (LV) parameters obtained from dual-tracer PET/MRI scan.

METHODS

In 22 patients with coronary heart disease (69 ± 9 years) dynamic [13N]NH3 (NH3) and [18F]FDG (FDG) PET scans were acquired. The first 2 minutes were used to calculate LV and RV first-pass ejection fraction (FPEF). Additionally, LV end-systolic (LVESV) and end-diastolic (LVEDV) volume and ejection fraction (LVEF) were calculated from the early (EP) and late-myocardial phases (LP). MRI served as a reference.

RESULTS

RVFPEF and LVFPEF from FDG and NH3 as well as RVEF and LVEF from MRI were (28 ± 11%, 32 ± 15%), (32 ± 11%, 41 ± 14%) and (42 ± 16%, 45 ± 19%), respectively. LVESV, LVEDV and LVEF from EP FDG and NH3 in 8 and 16 gates were [71 (15 to 213 mL), 98 (16 to 241 mL), 32 ± 17%] and [50 (17 to 206 mL), 93 (13 to 219 mL), 36 ± 17%] as well as [60 (19 to 360 mL), 109 (56 to 384 mL), 41 ± 22%] and [54 (16 to 371 mL), 116 (57 to 431 mL), 46 ± 24%], respectively. Moreover, LVESV, LVEDV and LVEF acquired from LP FDG and NH3 were (85 ± 63 mL, 138 ± 63 mL, 47 ± 19%) and (79 ± 56 mL, 137 ± 63 mL, 47 ± 20%), respectively. The LVESV, LVEDV from MRI were 93 ± 66 mL and 153 ± 71 mL, respectively. Significant correlations were observed for RVFPEF and LVFPEF between FDG and MRI (R = .51, P = .01; R = .64, P = .001), respectively. LVESV, LVEDV, and LVEF revealed moderate to strong correlations to MRI when they acquired from EP FDG and NH3 in 16 gates (all R > .7, P = .000). Similarly, all LV parameters from LP FDG and NH3 correlated good to strongly positive with MRI (all R > .7, and P < .001), except EDV from NH3 weakly correlated to EDV of MRI (R = .54, P < .05). Generally, Bland-Altman plots showed good agreements between PET and MRI.

CONCLUSIONS

Deriving LV and RV functional values from various phases of dynamic NH3 and FDG PET is feasible. These results could open a new perspective for further clinical applications of the PET examinations.

Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions

Objective

We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET.

Methods

Twenty-one patients underwent whole-body dynamic ¹⁸F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MR_FDG and DV_FDG images (MR_FDG-_std and DV_FDG-_std) and two short dynamic MR_FDG and DV_FDG images (MR_FDG-_tsd and DV_FDG-_tsd), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis.

Results

High correlations were obtained between MR_FDG-_tsd and MR_FDG-_std, and between DV_FDG-_tsd and DV_FDG-_std for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MR_FDG-max, MR_FDG-mean, and MR_FDG-peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DV_FDG-max, DV_FDG-mean, and DV_FDG-peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MR_FDG-max, MR_FDG-mean, and MR_FDG-peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DV_FDG-max, DV_FDG-mean, and DV_FDG-peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively.

Conclusions

This study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis.

Total-body parametric imaging using the patlak model: Feasibility of reduced scan time

PURPOSE

This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER.

METHODS

A total of 65 patients (27 females and 38 males, age 56.1±10.4) were recruited in this study. ¹⁸ F-FDG was injected and its dose was adjusted by body weight (4.07 MBq / kg).Total-body dynamic scanning was performed on the uEXPLORER Total-Body PET/CT scanner with a total scan time of 60 minutes from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the K_i images designated as G_IDIF with different acquisition times (20-60, 30-60, 40-60, and 44-60 min). The population-based input function (PBIF) was constructed from the mean value of the IDIF from the population, and K_i images designated as G_PBIF were generated using the PBIF. Non-localmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: G_IDIF+NLM and G_PBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the K_i images from different groups. The 20-60 min scans in G_IDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. K_i values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy.

RESULTS

The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all 4 groups (all p<0.001). The K_i images in the G_IDIF and G_PBIF groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of G_IDIF . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between K_i values estimated with G_IDIF and G_PBIF in muscle and lesion regions (all p>0.5). After applying the NLM filter, The coefficient of variation could be reduced on the order of [1%, 15%, 19%,37%] and [110%, 125%, 94%, 69%] with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The K_i images in the G_IDIF+NLM and G_PBIF+NLM groups generated from the 20-min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16-min scans. No significant difference was found between K_i values produced with G_IDIF+NLM and G_PBIF+NLM in muscle and lesion regions(all p>0.7).

CONCLUSIONS

The K_i images generated by the PBIF-based Patlak model using a 20-min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with G_IDIF from 40-min dynamic data, and there is no significant difference between K_i images generated using IDIF or PBIF (p>0.5). This article is protected by copyright. All rights reserved.

Phantom Validation of a Conservation of Activity-Based Partial Volume Correction Method for Arterial Input Function in Dynamic PET Imaging

Dynamic PET (dPET) imaging can be utilized to perform kinetic modelling of various physiologic processes, which are exploited by the constantly expanding range of targeted radiopharmaceuticals. To date, dPET remains primarily in the research realm due to a number of technical challenges, not least of which is addressing partial volume effects (PVE) in the input function. We propose a series of equations for the correction of PVE in the input function and present the results of a validation study, based on a purpose built phantom. ¹⁸F-dPET experiments were performed using the phantom on a set of flow tubes representing large arteries, such as the aorta (1" 2.54 cm ID), down to smaller vessels, such as the iliac arteries and veins (1/4" 0.635 cm ID). When applied to the dPET experimental images, the PVE correction equations were able to successfully correct the image-derived input functions by as much as 59 ± 35% in the presence of background, which resulted in image-derived area under the curve (AUC) values within 8 ± 9% of ground truth AUC. The peak heights were similarly well corrected to within 9 ± 10% of the scaled DCE-CT curves. The same equations were then successfully applied to correct patient input functions in the aorta and internal iliac artery/vein. These straightforward algorithms can be applied to dPET images from any PET-CT scanner to restore the input function back to a more clinically representative value, without the need for high-end Time of Flight systems or Point Spread Function correction algorithms.

An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

As a non-invasive imaging tool, positron emission tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with¹⁸F-FDG,¹⁸F-Fallypride and¹⁸F-AV45. Fifteen dynamic¹⁸F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitating the applications of dynamic PET in clinics and research.

Dynamic PET Imaging Using Dual Texture Features

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results. Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM. Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions. Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.

Multiparametric cardiac18F-FDG PET in humans: pilot comparison of FDG delivery rate with82Rb myocardial blood flow

Myocardial blood flow (MBF) and flow reserve are usually quantified in the clinic with positron emission tomography (PET) using a perfusion-specific radiotracer (e.g.⁸²Rb-chloride). However, the clinical accessibility of existing perfusion tracers remains limited. Meanwhile,¹⁸F-fluorodeoxyglucose (FDG) is a commonly used radiotracer for PET metabolic imaging without similar limitations. In this paper, we explore the potential of¹⁸F-FDG for myocardial perfusion imaging by comparing the myocardial FDG delivery rateK₁with MBF as determined by dynamic⁸²Rb PET in fourteen human subjects with heart disease. Two sets of FDGK₁were derived from one-hour dynamic FDG scans. One was the original FDGK₁estimates and the other was the correspondingK₁values that were linearly normalized for blood glucose levels. A generalized Renkin-Crone model was used to fit FDGK₁with Rb MBF, which then allowed for a nonlinear extraction fraction correction for converting FDGK₁to MBF. The linear correlation between FDG-derived MBF and Rb MBF was moderate (r= 0.79) before the glucose normalization and became much improved (r> 0.9) after glucose normalization. The extraction fraction of FDG was also similar to that of Rb-chloride in the myocardium. The results from this pilot study suggest that dynamic cardiac FDG-PET with tracer kinetic modeling has the potential to provide MBF in addition to its conventional use for metabolic imaging.

Quantitation of multiple injection dynamic PET scans: an investigation of the benefits of pooling data from separate scans when mapping kinetics

Multiple injection dynamic positron emission tomography (PET) scanning is used in the clinical management of certain groups of patients and in medical research. The analysis of these studies can be approached in two ways: (i) separate analysis of data from individual tracer injections, or (ii), concatenate/pool data from separate injections and carry out a combined analysis. The simplicity of separate analysis has some practical appeal but may not be statistically efficient. We use a linear model framework associated with a kinetic mapping scheme to develop a simplified theoretical understanding of separate and combined analysis. The theoretical framework is explored numerically using both 1D and 2D simulation models. These studies are motivated by the breast cancer flow-metabolism mismatch studies involving15O-water (H2O) and18F-Fluorodeoxyglucose (FDG) and repeat15O-H2O injections used in brain activation investigations. Numerical results are found to be substantially in line with the simple theoretical analysis: mean square error characteristics of alternative methods are well described by factors involving the local voxel-level resolution of the imaging data, the relative activities of the individual scans and the number of separate injections involved. While voxel-level resolution has dependence on scan dose, after adjustment for this effect, the impact of a combined analysis is understood in simple terms associated with the linear model used for kinetic mapping. This is true for both data reconstructed by direct filtered backprojection or iterative maximum likelihood. The proposed analysis has potential to be applied to the emerging long axial field-of-view PET scanners.

[Dynamic11C-methionine PET/CT in differential diagnosis of brain gliomas]

OBJECTIVE

To evaluate the possibilities of dynamic preoperative ¹¹C-methionine (MET) PET/CT in differential diagnosis of various types of brain gliomas in adults.

MATERIAL AND METHODS

The study included 74 patients aged 48±14 years with supratentorial gliomas: Grade IV - glioblastoma (GB, n=33), Grade III - anaplastic oligodendroglioma (AOD, n=10) and anaplastic astrocytoma (AA, n=12), Grade II - diffuse astrocytoma (DA, n=13) and oligodendroglioma (OD, n=6). All patients underwent standard MRI and dynamic MET PET/CT within 20 minutes after intravenous injection of radiopharmaceutical. Then, we compared MRI and PET/CT data and comprehensively analyzed the early stages of time-activity curve using 2 parameters: the first pass peak (FPP) and the first peak of maximum uptake (Pmax).

RESULTS

We have significantly distinguished high-grade tumors (GB and AA+AOD) and certain benign gliomas (DA and OD) (p<0.05). AUC was over 0.7 and 0.8 for FPP and Pmax in differential diagnosis of various gliomas, respectively. We found that difficulties in differential diagnosis of gliomas arise mainly if oligodendrogliomas are included in the control group.

CONCLUSION

Dynamic PET/CT with analysis of FPP and Pmax increases specificity of differential diagnosis of various gliomas compared to standard static imaging. These data are valuable for choice of optimal treatment strategy, as well as fundamental research of metabolic processes and vascularization of various tumors.

Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-¹¹C]methionine ([¹¹C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [¹¹C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [¹¹C]MET PET in gliomas.

Dynamic PET/CT with the Hepatobiliary Tracer [68Ga]Ga-Tmos-DAZA for Characterization of a Hepatic Tumor

Established imaging modalities for the characterization of liver tumors are computed tomography (CT), magnetical resonance (MR) imaging, sonography, and hepatobiliary scintigraphy. In some cases, their results may be inconclusive or certain examinations not possible due to contraindications. Positron emission tomography (PET)/CT has the capability of dynamic imaging with high temporal resolution. With radiolabeled tri-alkoxysalicyl-1,4-diazepan-6-amine (TAoS-DAZA) tracers, imaging of liver perfusion and hepatobiliary function is possible in a single examination. In the presented case, the PET/CT was performed in a patient with suspected hepatocellular carcinoma and atypical CT findings. PET imaging characteristics were consistent with a hepatocellular carcinoma (HCC). PET with DAZA ligands may be a supplemental method for liver tumor characterization in difficult cases.

FBP-Net for direct reconstruction of dynamic PET images

Dynamic positron emission tomography (PET) imaging can provide information about metabolic changes over time, used for kinetic analysis and auxiliary diagnosis. Existing deep learning-based reconstruction methods have too many trainable parameters and poor generalization, and require mass data to train the neural network. However, obtaining large amounts of medical data is expensive and time-consuming. To reduce the need for data and improve the generalization of network, we combined the filtered back-projection (FBP) algorithm with neural network, and proposed FBP-Net which could directly reconstruct PET images from sinograms instead of post-processing the rough reconstruction images obtained by traditional methods. The FBP-Net contained two parts: the FBP part and the denoiser part. The FBP part adaptively learned the frequency filter to realize the transformation from the detector domain to the image domain, and normalized the coarse reconstruction images obtained. The denoiser part merged the information of all time frames to improve the quality of dynamic PET reconstruction images, especially the early time frames. The proposed FBP-Net was performed on simulation and real dataset, and the results were compared with the state-of-art U-net and DeepPET. The results showed that FBP-Net did not tend to overfit the training set and had a stronger generalization.

Pharmacokinetic Assessment of 18F-(2S,4R)-4-Fluoroglutamine in Patients with Cancer

¹⁸F-(2S,4R)-4-fluoroglutamine (¹⁸F-FGln) is an investigational PET radiotracer for imaging tumor glutamine flux and metabolism. The aim of this study was to investigate its pharmacokinetic properties in patients with cancer. Methods: Fifty lesions from 41 patients (21 men and 20 women, aged 54 ± 14 y) were analyzed. Thirty-minute dynamic PET scans were performed concurrently with a rapid intravenous bolus injection of 232 ± 82 MBq of ¹⁸F-FGln, followed by 2 static PET scans at 97 ± 14 and 190 ± 12 min after injection. Five patients also underwent a second ¹⁸F-FGln study 4-13 wk after initiation of therapy with glutaminase, dual TORC1/2, or programmed death-1 inhibitors. Blood samples were collected to determine plasma and metabolite fractions and to scale the image-derived input function. Regions of interest were manually drawn to calculate SUVs. Pharmacokinetic modeling with both reversible and irreversible 1- and 2-tissue-compartment models was performed to calculate the kinetic rate constants K ₁, k ₂, k ₃, and k ₄ The analysis was repeated with truncated 30-min dynamic datasets. Results: Intratumor ¹⁸F-FGln uptake patterns demonstrated substantial heterogeneity in different lesion types. In most lesions, the reversible 2-tissue-compartment model was chosen as the most appropriate according to the Akaike information criterion. K ₁, a surrogate biomarker for ¹⁸F-FGln intracellular transport, was the kinetic rate constant that was most correlated both with SUV at 30 min (Spearman ρ = 0.71) and with SUV at 190 min (ρ = 0.51). Only K ₁ was reproducible from truncated 30-min datasets (intraclass correlation coefficient, 0.96). k ₃, a surrogate biomarker for glutaminolysis rate, was relatively low in about 50% of lesions. Treatment with glutaminase inhibitor CB-839 substantially reduced the glutaminolysis rates as measured by k ₃ Conclusion: ¹⁸F-FGln dynamic PET is a sensitive tool for studying glutamine transport and metabolism in human malignancies. Analysis of dynamic data facilitates better understanding of ¹⁸F-FGln pharmacokinetics and may be necessary for response assessment to targeted therapies that impact intracellular glutamine pool size and tumor glutaminolysis rates.

Simultaneous estimation and segmentation from projection data in dynamic PET

PURPOSE

Dynamic positron emission tomography (PET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. Information of different functional regions based on an accurate reconstruction of the activity images and kinetic parametric images has been widely studied and can be useful in research and clinical setting for diagnosis and other quantitative tasks. In this paper, our purpose is to present a novel framework for estimating the kinetic parametric images directly from the raw measurement data together with a simultaneous segmentation accomplished through kinetic parameters clustering.

METHOD

An iterative framework is proposed to estimate the kinetic parameter image, activity map and do the segmentation simultaneously from the complete dynamic PET projection data. The clustering process is applied to the kinetic parameter variable rather than to the traditional activity distribution so as to achieve accurate discrimination between different functional areas. Prior information such as total variation regularization is incorporated to reduce the noise in the PET images and a sparseness constraint is integrated to guarantee the solution for kinetic parameters due to the over complete dictionary. Alternating direction method of multipliers (ADMM) method is used to solve the optimization problem. The proposed algorithm was validated with experiments on Monte Carlo-simulated phantoms and real patient data. Symbol error rate (SER) was defined to evaluate the performance of clustering. Bias and variance of the reconstruction activity images were calculated based on ground truth. Relative mean square error (MSE) was used to evaluate parametric results quantitatively.

RESULT

In brain phantom experiment, when counting rate is 1 × 10⁶ , the bias (variance) of our method is 0.1270 (0.0281), which is lower than maximum likelihood expectation maximization (MLEM) 0.1637 (0.0410) and direct estimation without segmentation (DE) 0.1511 (0.0326). In the Zubal phantom experiment, our method has the lowest bias (variance) 0.1559 (0.0354) with 1 × 10⁵ counting rate, compared with DE 0.1820 (0.0435) and MLEM 0.3043 (0.0644). As for classification, the SER of our method is 18.87% which is the lowest among MLEM + k-means, DE + k-means, and kinetic spectral clustering (KSC). Brain data with MR reference and real patient results also show that the proposed method can get images with clear structure by visual inspection.

CONCLUSION

In this paper, we presented a joint reconstruction framework for simultaneously estimating the activity distribution, parametric images, and parameter-based segmentation of the ROIs into different functional areas. Total variation regularization is performed on the activity distribution domain to suppress noise and preserve the edges between ROIs. An over complete dictionary for time activity curve basis is constructed. SER, bias, variance, and MSE were calculated to show the effectiveness of the proposed method.

11C-Choline Pharmacokinetics in Recurrent Prostate Cancer

The aim of this study was to investigate the value of pharmacokinetic modeling for quantifying 11C-choline uptake in patients with recurrent prostate cancer. Methods: In total, 194 patients with clinically suspected recurrence of prostate cancer underwent 11C-choline dynamic PET over the pelvic region (0-8 min), followed by a 6-min static acquisition at about 25 min after injection. Regions of interest were drawn over sites of disease identified by a radiologist with experience in nuclear medicine. 11C-choline uptake and pharmacokinetics were evaluated by SUV, graphical analysis (Patlak plot; KiP), and 1- and 2-compartment pharmacokinetic models (K1, K1/k2, k3, k4, and the macro parameter KiC). Twenty-four local recurrences, 65 metastatic lymph nodes, 19 osseous metastases, and 60 inflammatory lymph nodes were included in the analysis, which was subsequently repeated for regions of interest placed over the gluteus maximus muscle and adipose tissue as a control. Results: SUVmean and KiP were 3.60 ± 2.16 and 0.28 ± 0.22 min-1 in lesions, compared with 2.11 ± 1.33 and 0.15 ± 0.10 min-1 in muscle and 0.26 ± 0.07 and 0.02 ± 0.01 min-1 in adipose tissue. According to the Akaike information criterion, the 2-compartment irreversible model was most appropriate in 85% of lesions and resulted in a K1 of 0.79 ± 0.98 min-1 (range, 0.11-7.17 min-1), a K1/k2 of 2.92 ± 3.52 (range, 0.31-20.00), a k3 of 0.36 ± 0.30 min-1 (range, 0.00-1.00 min-1) and a KiC of 0.28 ± 0.22 min-1 (range, 0.00-1.33 min-1). The Spearman ρ between SUV and KiP, between SUV and KiC, and between KiP and KiC was 0.94, 0.91, and 0.97, respectively, and that between SUV and K1, between SUV and K1/k2, and between SUV and k3 was 0.70, 0.44, and 0.33, respectively. Malignant lymph nodes exhibited a higher SUV, KiP, and KiC than benign lymph nodes. Conclusion: Although 11C-choline pharmacokinetic modeling has potential to uncouple the contributions of different processes leading to intracellular entrapment of 11C-choline, the high correlation between SUV and both KiP and KiC supports the use of simpler SUV methods to evaluate changes in 11C-choline uptake and metabolism for treatment monitoring.

Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping

15O-H2O PET imaging is an accurate method to measure cerebral blood flow (CBF) but it requires an arterial input function (AIF). Historically, image-derived AIF estimation suffers from low temporal resolution, spill-in, and spill-over problems. Here, we optimized tracer dose on a time-of-flight PET/MR according to the acquisition-specific noise-equivalent count rate curve. An optimized dose of 850 MBq of 15O-H2O was determined, which allowed sufficient counts to reconstruct a short time-frame PET angiogram (PETA) during the arterial phase. This PETA enabled the measurement of the extent of spill-over, while an MR angiogram was used to measure the true arterial volume for AIF estimation. A segment of the high cervical arteries outside the brain was chosen, where the measured spill-in effects were minimal. CBF studies were performed twice with separate [15O]-H2O injections in 10 healthy subjects, yielding values of 88 ± 16, 44 ± 9, and 58 ± 11 mL/min/100 g for gray matter, white matter, and whole brain, with intra-subject CBF differences of 5.0 ± 4.0%, 4.1 ± 3.3%, and 4.5 ± 3.7%, respectively. A third CBF measurement after the administration of 1 g of acetazolamide showed 35 ± 23%, 29 ± 20%, and 33 ± 22% increase in gray matter, white matter, and whole brain, respectively. Based on these findings, the proposed noninvasive AIF method provides robust CBF measurement with 15O-H2O PET.

18 F-fluoromisonidazole uptake in advanced stage non-small cell lung cancer: A voxel-by-voxel PET kinetics study

PURPOSE

The aim of this study was to determine the relative abilities of compartment models to describe time-courses of 18 F-fluoromisonidazole (FMISO) uptake in tumor voxels of patients with non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography. Also to use fits of the best-performing model to investigate changes in fitted rate-constants with distance from the tumor edge.

METHODS

Reversible and irreversible two- and three-tissue compartment models were fitted to 24 662 individual voxel time activity curves (TACs) obtained from tumors in nine patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC). Two different models (two- and three-tissue) were fitted to 30 measured voxel TACs to provide ground-truth TACs for a statistical simulation study. Appropriately scaled noise was added to each of the resulting ground-truth TACs, generating 1000 simulated noisy TACs for each ground-truth TAC. The simulation study was carried out to provide estimates of the accuracy and precision with which parameter values are determined, the estimates being obtained for both assumptions about the ground-truth kinetics. A BIC clustering technique was used to group the fitted rate-constants, taking into consideration the underlying uncertainties on the fitted rate-constants. Voxels were also categorized according to their distance from the tumor edge.

RESULTS

For uptake time-courses of individual voxels an irreversible two-tissue compartment model was found to be most precise. The simulation study indicated that this model had a one standard deviation precision of 39% for tumor fractional blood volumes and 37% for the FMISO binding rate-constant. Weighted means of fitted FMISO binding rate-constants of voxels in all tumors rose significantly with increasing distance from the tumor edge, whereas fitted fractional blood volumes fell significantly. When grouped using the BIC clustering, many centrally located voxels had high-fitted FMISO binding rate-constants and low rate-constants for tracer flow between the vasculature and tumor, both indicative of hypoxia. Nevertheless, many of these voxels had tumor-to-blood (TBR) values lower than the 1.4 level commonly expected for hypoxic tissues, possibly due to the low rate-constants for tracer flow between the vasculature and tumor cells in these voxels.

CONCLUSIONS

Time-courses of FMISO uptake in NSCLC tumor voxels are best analyzed using an irreversible two-tissue compartment model, fits of which provide more precise parameter values than those of a three-tissue model. Changes in fitted model parameter values indicate that levels of hypoxia rise with increasing distance from tumor edges. The average FMISO binding rate-constant is higher for voxels in tumor centers than in the next tumor layer out, but the average value of the more simplistic TBR metric is lower in tumor centers. For both metrics, higher values might be considered indicative of hypoxia, and the mismatch in this case is likely to be due to poor perfusion at the tumor center. Kinetics analysis of dynamic PET images may therefore provide more accurate measures of the hypoxic status of such regions than the simpler TBR metric, a hypothesis we are presently exploring in a study of tumor imaging versus histopathology.

Cerebrospinal Fluid Clearance in Alzheimer Disease Measured with Dynamic PET

Evidence supporting the hypothesis that reduced cerebrospinal fluid (CSF) clearance is involved in the pathophysiology of Alzheimer disease (AD) comes primarily from rodent models. However, unlike rodents, in which predominant extracranial CSF egress is via olfactory nerves traversing the cribriform plate, human CSF clearance pathways are not well characterized. Dynamic PET with ¹⁸F-THK5117, a tracer for tau pathology, was used to estimate the ventricular CSF time-activity as a biomarker for CSF clearance. We tested 3 hypotheses: extracranial CSF is detected at the superior turbinates; CSF clearance is reduced in AD; and CSF clearance is inversely associated with amyloid deposition. Methods: Fifteen subjects, 8 with AD and 7 normal control volunteers, were examined with ¹⁸F-THK5117. Ten subjects additionally underwent ¹¹C-Pittsburgh compound B (¹¹C-PiB) PET scanning, and 8 were ¹¹C-PiB-positive. Ventricular time-activity curves of ¹⁸F-THK5117 were used to identify highly correlated time-activity curves from extracranial voxels. Results: For all subjects, the greatest density of CSF-positive extracranial voxels was in the nasal turbinates. Tracer concentration analyses validated the superior nasal turbinate CSF signal intensity. AD patients showed ventricular tracer clearance reduced by 23% and 66% fewer superior turbinate CSF egress sites. Ventricular CSF clearance was inversely associated with amyloid deposition. Conclusion: The human nasal turbinate is part of the CSF clearance system. Lateral ventricle and superior nasal turbinate CSF clearance abnormalities are found in AD. Ventricular CSF clearance reductions are associated with increased brain amyloid depositions. These data suggest that PET-measured CSF clearance is a biomarker of potential interest in AD and other neurodegenerative diseases.

Multiparametric Imaging of Tumor Hypoxia and Perfusion with 18F-Fluoromisonidazole Dynamic PET in Head and Neck Cancer

Tumor hypoxia and perfusion are independent prognostic indicators of patient outcome. We developed the methodology for and investigated the utility of multiparametric imaging of tumor hypoxia and perfusion with ¹⁸F-fluoromisonidazole (¹⁸F-FMISO) dynamic PET (dPET) in head and neck cancer. Methods: One hundred twenty head and neck cancer patients underwent 0- to 30-min ¹⁸F-FMISO dPET in a customized immobilization mask, followed by 10-min static acquisitions starting at 93 ± 6 and 160 ± 13 min after injection. A total of 248 lesions (≥2 cm³) were analyzed. Voxelwise pharmacokinetic modeling was conducted using an irreversible 1-plasma 2-tissue-compartment model to calculate surrogate biomarkers of tumor hypoxia (k₃), perfusion (K₁), and ¹⁸F-FMISO distribution volume. The analysis was repeated with truncated dPET datasets. Results: Substantial inter- and intratumor heterogeneity was observed for all investigated metrics. Equilibration between the blood and unbound ¹⁸F-FMISO was rapid in all tumors. ¹⁸F-FMISO distribution volume deviated from the expected value of unity, causing discrepancy between k₃ maps and total ¹⁸F-FMISO uptake and reducing the dynamic range of total ¹⁸F-FMISO uptake for quantifying the degree of hypoxia. Both positive and negative trends between hypoxia and perfusion were observed in individual lesions. All investigated metrics were reproducible when calculated from a truncated 20-min dataset. Conclusion:¹⁸F-FMISO dPET provides the data necessary to generate parametric maps of tumor hypoxia, perfusion, and radiotracer distribution volume. These data clarify the ambiguity in interpreting ¹⁸F-FMISO uptake and improve the characterization of lesions. We show total acquisition times can be reduced to 20 min, facilitating the translation of ¹⁸F-FMISO dPET into the clinic.

Automatic Extraction of Myocardial Mass and Volume Using Parametric Images from Dynamic Nongated PET

Dynamic cardiac PET is used to quantify molecular processes in vivo. However, measurements of left ventricular (LV) mass and volume require electrocardiogram-gated PET data. The aim of this study was to explore the feasibility of measuring LV geometry using nongated dynamic cardiac PET.

METHODS

Thirty-five patients with aortic-valve stenosis and 10 healthy controls underwent a 27-min (11)C-acetate PET/CT scan and cardiac MRI (CMR). The controls were scanned twice to assess repeatability. Parametric images of uptake rate K1 and the blood pool were generated from nongated dynamic data. Using software-based structure recognition, the LV wall was automatically segmented from K1 images to derive functional assessments of LV mass (mLV) and wall thickness. End-systolic and end-diastolic volumes were calculated using blood pool images and applied to obtain stroke volume and LV ejection fraction (LVEF). PET measurements were compared with CMR.

RESULTS

High, linear correlations were found for LV mass (r = 0.95), end-systolic volume (r = 0.93), and end-diastolic volume (r = 0.90), and slightly lower correlations were found for stroke volume (r = 0.74), LVEF (r = 0.81), and thickness (r = 0.78). Bland-Altman analyses showed significant differences for mLV and thickness only and an overestimation for LVEF at lower values. Intra- and interobserver correlations were greater than 0.95 for all PET measurements. PET repeatability accuracy in the controls was comparable to CMR.

CONCLUSION

LV mass and volume are accurately and automatically generated from dynamic (11)C-acetate PET without electrocardiogram gating. This method can be incorporated in a standard routine without any additional workload and can, in theory, be extended to other PET tracers.

Feasibility of 18F-Fluoromisonidazole Kinetic Modeling in Head and Neck Cancer Using Shortened Acquisition Times

(18)F-fluoromisonidazole dynamic PET (dPET) is used to identify tumor hypoxia noninvasively. Its routine clinical implementation, however, has been hampered by the long acquisition times required. We investigated the feasibility of kinetic modeling using shortened acquisition times in (18)F-fluoromisonidazole dPET, with the goal of expediting the clinical implementation of (18)F-fluoromisonidazole dPET protocols.

METHODS

Six patients with squamous cell carcinoma of the head and neck and 10 HT29 colorectal carcinoma-bearing nude rats were studied. In addition to an (18)F-FDG PET scan, each patient underwent a 45-min (18)F-fluoromisonidazole dPET scan, followed by 10-min acquisitions at 96 ± 4 and 163 ± 17 min after injection. Ninety-minute (18)F-fluoromisonidazole dPET scans were acquired in animals. Intratumor voxels were classified into 4 clusters based on their kinetic behavior using k-means clustering. Kinetic modeling was performed using the foregoing full datasets (FD) and repeated for each of 2 shortened datasets corresponding to the first approximately 100 min (SD1; patients only) or the first 45 min (SD2) of dPET data. The kinetic rate constants (KRCs) as calculated with a 2-compartment model for both SD1 and SD2 were compared with those derived from FD by correlation (Pearson), regression (Passing-Bablok), deviation (Bland-Altman), and classification (area-under-the-receiver-operating characteristic curve) analyses. Simulations were performed to assess uncertainties due to statistical noise.

RESULTS

Strong correlation (r ≥ 0.75, P < 0.001) existed between all KRCs deduced from both SD1 and SD2, and from FD. Significant differences between KRCs were found only for FD-SD2 correlations in patient studies. K1 and k3 were reproducible to within approximately 6% and approximately 30% (FD-SD1; patients) and approximately 4% and approximately 75% (FD-SD2; animals). Area-under-the-receiver-operating characteristic curve values for classification of patient clusters as hypoxic, using a tumor-to-blood ratio greater than 1.2, were 0.91 (SD1) and 0.86 (SD2). The percentage SD in estimating K1 and k3 from 45-min shortened datasets due to noise was less than 1% and between 2% and 12%, respectively.

CONCLUSION

Using single-session 45-min shortened (18)F-fluoromisonidazole dPET datasets appears to be adequate for the identification of intratumor regions of hypoxia. However, k3 was significantly overestimated in the clinical cohort. Further studies are necessary to evaluate the clinical significance of differences between the results as calculated from full and shortened datasets.

Synthesis of 2-[(18)F]Fluoro-2-deoxyisosorbide 5-mononitrate and Assessment of Its in vivo Biodistribution as Determined by Dynamic Positron Emission Tomography (PET)

Herein we disclose the synthesis of 2-fluoro-2-deoxyisosorbide 5-mononitrate (2F-IS-5MN), a fluorinated analogue of the commonly prescribed vasodilator isosorbide 5-mononitrate (IS-5MN). X-ray structural data for IS-5MN and its C2-epimeric congener IM-5MN are presented together with structural data for 2F-IS-5MN. Radioisotope labeling of 2F-IS-5MN has, for the first time, allowed observation of the in vivo biodistribution of this organic nitrate by means of dynamic positron emission tomography (PET) in wild-type mice.

A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET

Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD).

METHODS

The GATE Monte Carlo software was used to simulate 2 × 15 dynamic 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3-dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest.

RESULTS

The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6-15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%-70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less frame-sampling dependence and less uncertain results, compared with OSEM, but was on average more biased.

CONCLUSION

Of the 6 sampling schemes investigated in this study, an early frame duration of 6-15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Very-short frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be preferred over OSEM for short frames with poor statistics.

Uncertainty in measurements of ¹⁸F blood concentration and its effect on simplified dynamic PET analysis

The purpose of this study was to assess the accuracy and practicality of well counter- and thyroid probe-based methods, commonly available in nuclear medicine facilities, for measuring the concentration of (18)F-FDG in blood samples. The degree to which the accuracy of such methods influences quantitative analysis of dynamic PET scans was also assessed.

METHODS

Thirty-five patients with cancer of the head and neck underwent dynamic PET imaging as part of a study intended to evaluate the utility of quantitative, image-based metrics for assessment of early treatment response. The activity in blood samples from the patients, necessary to provide an estimate of the input function for quantitative analysis, was measured both using a thyroid probe and using a well counter. Three calibration techniques were compared: single-point calibration using a standard solution for the thyroid probe (ProbePoint technique), single-point calibration using a standard solution for the well counter (WellPoint technique), and multiple-point calibration over the full range of expected blood activities for the well counter (WellCurve technique). The WellCurve method was assumed to provide the most accurate estimate of blood activity. The precision of measuring blood volume using a micropipette was also evaluated by obtaining multiple blood samples. Simplified-kinetic-analysis multiple-time-point (SKA-M) uptake rates for the primary tumor were calculated for all 35 patients using PET images and each of the 3 methods for assessing blood concentration.

RESULTS

Errors in blood activity measurements ranging from -9.5% to 7.6% were found using the ProbePoint method, whereas the error range was much less (from -1.3% to 0.9%) for the WellPoint method. The precision in blood volume measurements ranged from -6% to 12% in the 10 patients assessed. The errors in blood activity and volume measurements were reflected in the SKA-M measurements in the same range.

CONCLUSION

The WellPoint method provides a compromise between accuracy and clinical practicality. Random errors in both blood activity and volume measurements accumulate and may compromise parameters--such as the SKA-M estimate of tumor uptake rate--that depend not only on images but also on blood concentration data.