Single-scan dual-tracer FLT+FDG PET tumor characterization

Kinetic model-informed deep learning for multiplexed PET image separation

BACKGROUND

Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair.

METHODS

Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm.

RESULTS

The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [18 F]FDG+[11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples.

CONCLUSIONS

This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data.

Signal separation of simultaneous dual-tracer PET imaging based on global spatial information and channel attention

BACKGROUND

Simultaneous dual-tracer positron emission tomography (PET) imaging efficiently provides more complete information for disease diagnosis. The signal separation has long been a challenge of dual-tracer PET imaging. To predict the single-tracer images, we proposed a separation network based on global spatial information and channel attention, and connected it to FBP-Net to form the FBPnet-Sep model.

RESULTS

Experiments using simulated dynamic PET data were conducted to: (1) compare the proposed FBPnet-Sep model to Sep-FBPnet model and currently existing Multi-task CNN, (2) verify the effectiveness of modules incorporated in FBPnet-Sep model, (3) investigate the generalization of FBPnet-Sep model to low-dose data, and (4) investigate the application of FBPnet-Sep model to multiple tracer combinations with decay corrections. Compared to the Sep-FBPnet model and Multi-task CNN, the FBPnet-Sep model reconstructed single-tracer images with higher structural similarity, peak signal-to-noise ratio and lower mean squared error, and reconstructed time-activity curves with lower bias and variation in most regions. Excluding the Inception or channel attention module resulted in degraded image qualities. The FBPnet-Sep model showed acceptable performance when applied to low-dose data. Additionally, it could deal with multiple tracer combinations. The qualities of predicted images, as well as the accuracy of derived time-activity curves and macro-parameters were slightly improved by incorporating a decay correction module.

CONCLUSIONS

The proposed FBPnet-Sep model was considered a potential method for the reconstruction and signal separation of simultaneous dual-tracer PET imaging.

AI for PET image reconstruction

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET's spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times. While the arrival of new PET scanners (such as total body PET) is helping, there is always a need to improve reconstructed image quality due to the time and count limited imaging conditions. Artificial intelligence (AI) methods are now at the frontier of research for PET image reconstruction. While AI can learn the imaging physics as well as the noise in the data (when given sufficient examples), one of the most common uses of AI arises from exploiting databases of high-quality reference examples, to provide advanced noise compensation and resolution recovery. There are three main AI reconstruction approaches: (i) direct data-driven AI methods which rely on supervised learning from reference data, (ii) iterative (unrolled) methods which combine our physics and statistical models with AI learning from data, and (iii) methods which exploit AI with our known models, but crucially can offer benefits even in the absence of any example training data whatsoever. This article reviews these methods, considering opportunities and challenges of AI for PET reconstruction.

Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

BACKGROUND

Simultaneous dual-tracer positron emission tomography (PET) imaging can observe two molecular targets in a single scan, which is conducive to disease diagnosis and tracking. Since the signals emitted by different tracers are the same, it is crucial to separate each single tracer from the mixed signals. The current study proposed a novel deep learning-based method to reconstruct single-tracer activity distributions from the dual-tracer sinogram.

METHODS

We proposed the Multi-task CNN, a three-dimensional convolutional neural network (CNN) based on a framework of multi-task learning. One common encoder extracted features from the dual-tracer dynamic sinogram, followed by two distinct and parallel decoders which reconstructed the single-tracer dynamic images of two tracers separately. The model was evaluated by mean squared error (MSE), multiscale structural similarity (MS-SSIM) index and peak signal-to-noise ratio (PSNR) on simulated data and real animal data, and compared to the filtered back-projection method based on deep learning (FBP-CNN).

RESULTS

In the simulation experiments, the Multi-task CNN reconstructed single-tracer images with lower MSE, higher MS-SSIM and PSNR than FBP-CNN, and was more robust to the changes in individual difference, tracer combination and scanning protocol. In the experiment of rats with an orthotopic xenograft glioma model, the Multi-task CNN reconstructions also showed higher qualities than FBP-CNN reconstructions.

CONCLUSIONS

The proposed Multi-task CNN could effectively reconstruct the dynamic activity images of two single tracers from the dual-tracer dynamic sinogram, which was potential in the direct reconstruction for real simultaneous dual-tracer PET imaging data in future.

Multiplexed imaging in oncology

In oncology, technologies for clinical molecular imaging are used to diagnose patients, establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed methods increase the number of disease-specific biomarkers that can be detected simultaneously, such as the overexpression of oncogenic proteins, aberrant metabolite uptake and anomalous blood perfusion. The quantitative localization of each biomarker could considerably increase the specificity and the accuracy of technologies for clinical molecular imaging to facilitate granular diagnoses, patient stratification and earlier assessments of the responses to administered therapeutics. In this Review, we discuss established techniques for multiplexed imaging and the most promising emerging multiplexing technologies applied to the imaging of isolated tissues and cells and to non-invasive whole-body imaging. We also highlight advances in radiology that have been made possible by multiplexed imaging.

Principles of Tracer Kinetic Analysis in Oncology, Part II: Examples and Future Directions

Learning Objectives: On successful completion of this activity, participants should be able to (1) describe examples of the application of PET tracer kinetic analysis to oncology; (2) list applications research and possible clinical applications in oncology where kinetic analysis is helpful; and (3) discuss future applications of kinetic modeling to cancer research and possible clinical cancer imaging practice.Financial Disclosure: This work was supported by KL2 TR001879, R01 CA211337, R01 CA113941, R33 CA225310, Komen SAC130060, R50 CA211270, and K01 DA040023. Dr. Pantel is a consultant or advisor for Progenics and Blue Earth Diagnostics and is a meeting participant or lecturer for Blue Earth Diagnostics. Dr. Mankoff is on the scientific advisory boards of GE Healthcare, Philips Healthcare, Reflexion, and ImaginAb and is the owner of Trevarx; his wife is the chief executive officer of Trevarx. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest.CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through April 2025.Kinetic analysis of dynamic PET imaging enables the estimation of biologic processes relevant to disease. Through mathematic analysis of the interactions of a radiotracer with tissue, information can be gleaned from PET imaging beyond static uptake measures. Part I of this 2-part continuing education paper reviewed the underlying principles and methodology of kinetic modeling. In this second part, the benefits of kinetic modeling for oncologic imaging are illustrated through representative case examples that demonstrate the principles and benefits of kinetic analysis in oncology. Examples of the model types discussed in part I are reviewed here: a 1-tissue-compartment model (15O-water), an irreversible 2-tissue-compartment model (18F-FDG), and a reversible 2-tissue-compartment model (3'-deoxy-3'-18F-fluorothymidine). Kinetic approaches are contrasted with static uptake measures typically used in the clinic. Overall, this 2-part review provides the reader with background in kinetic analysis to understand related research and improve the interpretation of clinical nuclear medicine studies with a focus on oncologic imaging.

Simultaneous radiomethylation of [11C]harmine and [11C]DASB and kinetic modeling approach for serotonergic brain imaging in the same individual

Simultaneous characterization of pathologies by multi-tracer positron emission tomography (PET) is among the most promising applications in nuclear medicine. Aim of this work was the simultaneous production of two PET-tracers in one module and test the relevance for human application. [11C]harmine and [11C]DASB were concurrently synthesized in a 'two-in-one-pot' reaction in quality for application. Dual-tracer protocol was simulated using 16 single PET scans in different orders of tracer application separated by different time intervals. Volume of distribution was calculated for single- and dual-tracer measurements using Logan's plot and arterial input function in 13 brain regions. The 'two-in-one-pot' reaction yielded equivalent amounts of both radiotracers with comparable molar activities. The simulations of the dual-tracer application were comparable to the single bolus injections in 13 brain regions, when [11C]harmine was applied first and [11C]DASB second, with an injection time interval of 45 min (rxy = 0.90). Our study shows the successful simultaneous dual-tracer production leading to decreased radiation burden and costs. The simulation of dual subject injection to quantify the monoamine oxidase-A and serotonin transporter distribution proved its high potential. Multi-tracer imaging may drive more sophisticated study designs and diminish the day-to-day differences in the same individual as well as increase PET scanner efficiency.

Machine Learning-Based Noninvasive Quantification of Single-Imaging Session Dual-Tracer 18F-FDG and 68Ga-DOTATATE Dynamic PET-CT in Oncology

⁶⁸Ga-DOTATATE PET-CT is routinely used for imaging neuroendocrine tumor (NET) somatostatin receptor subtype 2 (SSTR2) density in patients, and is complementary to FDG PET-CT for improving the accuracy of NET detection, characterization, grading, staging, and predicting/monitoring NET responses to treatment. Performing sequential ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET scans would require 2 or more days and can delay patient care. To align temporal and spatial measurements of ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET, and to reduce scan time and CT radiation exposure to patients, we propose a single-imaging session dual-tracer dynamic PET acquisition protocol in the study. A recurrent extreme gradient boosting (rXGBoost) machine learning algorithm was proposed to separate the mixed ¹⁸F-FDG and ⁶⁸Ga-DOTATATE time activity curves (TACs) for the region of interest (ROI) based quantification with tracer kinetic modeling. A conventional parallel multi-tracer compartment modeling method was also implemented for reference. Single-scan dual-tracer dynamic PET was simulated from 12 NET patient studies with ¹⁸F-FDG and ⁶⁸Ga-DOTATATE 45-min dynamic PET scans separately obtained within 2 days. Our experimental results suggested an ¹⁸F-FDG injection first followed by ⁶⁸Ga-DOTATATE with a minimum 5 min delayed injection protocol for the separation of mixed ¹⁸F-FDG and ⁶⁸Ga-DOTATATE TACs using rXGBoost algorithm followed by tracer kinetic modeling is highly feasible.

Transpathology: molecular imaging-based pathology

Pathology is the medical specialty concerned with the study of the disease nature and causes, playing a key role in bridging basic researches and clinical medicine. In the course of development, pathology has significantly expanded our understanding of disease, and exerted enormous impact on the management of patients. However, challenges facing pathology, the inherent invasiveness of pathological practice and the persistent concerns on the sample representativeness, constitute its limitations. Molecular imaging is a noninvasive technique to visualize, characterize, and measure biological processes at the molecular level in living subjects. With the continuous development of equipment and probes, molecular imaging has enabled an increasingly precise evaluation of pathophysiological changes. A new pathophysiology visualization system based on molecular imaging is forming and shows the great potential to reform the pathological practice. Several improvements in "trans-," including trans-scale, transparency, and translation, would be driven by this new kind of pathological practice. Pathological changes could be evaluated in a trans-scale imaging mode; tissues could be transparentized to better present the underlying pathophysiological information; and the translational processes of basic research to the clinical practice would be better facilitated. Thus, transpathology would greatly facilitate in deciphering the pathophysiological events in a multiscale perspective, and supporting the precision medicine in the future.

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.

Positron range-free and multi-isotope tomography of positron emitters

Despite improvements in small animal PET instruments, many tracers cannot be imaged at sufficiently high resolutions due to positron range, while multi-tracer PET is hampered by the fact that all annihilation photons have equal energies. Here we realize multi-isotope and sub-mm resolution PET of isotopes with several mm positron range by utilizing prompt gamma photons that are commonly neglected. A PET-SPECT-CT scanner (VECTor/CT, MILabs, The Netherlands) equipped with a high-energy cluster-pinhole collimator was used to image ¹²⁴I and a mix of ¹²⁴I and ¹⁸F in phantoms and mice. In addition to positrons (mean range 3.4 mm) ¹²⁴I emits large amounts of 603 keV prompt gammas that-aided by excellent energy discrimination of NaI-were selected to reconstruct ¹²⁴I images that are unaffected by positron range. Photons detected in the 511 keV window were used to reconstruct ¹⁸F images. Images were reconstructed iteratively using an energy dependent matrix for each isotope. Correction of ¹⁸F images for contamination with ¹²⁴I annihilation photons was performed by Monte Carlo based range modelling and scaling of the ¹²⁴I prompt gamma image before subtracting it from the ¹⁸F image. Additionally, prompt gamma imaging was tested for ⁸⁹Zr that emits very high-energy prompts (909 keV). In Derenzo resolution phantoms 0.75 mm rods were clearly discernable for ¹²⁴I, ⁸⁹Zr and for simultaneously acquired ¹²⁴I and ¹⁸F imaging. Image quantification in phantoms with reservoirs filled with both ¹²⁴I and ¹⁸F showed excellent separation of isotopes and high quantitative accuracy. Mouse imaging showed uptake of ¹²⁴I in tiny thyroid parts and simultaneously injected ¹⁸F-NaF in bone structures. The ability to obtain PET images at sub-mm resolution both for isotopes with several mm positron range and for multi-isotope PET adds to many other unique capabilities of VECTor's clustered pinhole imaging, including simultaneous sub-mm PET-SPECT and theranostic high energy SPECT.

PET Parametric Imaging: Past, Present, and Future

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.

Explicit measurement of multi-tracer arterial input function for PET imaging using blood sampling spectroscopy

Background

Conventional PET imaging has usually been limited to a single tracer per scan. We propose a new technique for multi-tracer PET imaging that uses dynamic imaging and multi-tracer compartment modeling including an explicitly derived arterial input function (AIF) for each tracer using blood sampling spectroscopy. For that purpose, at least one of the co-injected tracers must be based on a non-pure positron emitter.

Methods

The proposed technique was validated in vivo by performing cardiac PET/CT studies on three healthy pigs injected with ¹⁸FDG (viability) and ⁶⁸Ga-DOTA (myocardial blood flow and extracellular volume fraction) during the same acquisition. Blood samples were collected during the PET scan, and separated AIF for each tracer was obtained by spectroscopic analysis. A multi-tracer compartment model was applied to the myocardium in order to obtain the distribution of each tracer at the end of the PET scan. Relative activities of both tracers and tracer uptake were obtained and compared with the values obtained by ex vivo analysis of excised myocardial tissue segments.

Results

A high correlation was obtained between multi-tracer PET results, and those obtained from ex vivo analysis (¹⁸FDG relative activity: r = 0.95, p < 0.0001; SUV: r = 0.98, p < 0.0001).

Conclusions

The proposed technique allows performing PET scans with two tracers during the same acquisition obtaining separate information for each tracer.

Development of a blood sample detector for multi-tracer positron emission tomography using gamma spectroscopy

BACKGROUND

Multi-tracer positron emission tomography (PET) imaging can be accomplished by applying multi-tracer compartment modeling. Recently, a method has been proposed in which the arterial input functions (AIFs) of the multi-tracer PET scan are explicitly derived. For that purpose, a gamma spectroscopic analysis is performed on blood samples manually withdrawn from the patient when at least one of the co-injected tracers is based on a non-pure positron emitter. Alternatively, these blood samples required for the spectroscopic analysis may be obtained and analyzed on site by an automated detection device, thus minimizing analysis time and radiation exposure of the operating personnel. In this work, a new automated blood sample detector based on silicon photomultipliers (SiPMs) for single- and multi-tracer PET imaging is presented, characterized, and tested in vitro and in vivo.

RESULTS

The detector presented in this work stores and analyzes on-the-fly single and coincidence detected events. A sensitivity of 22.6 cps/(kBq/mL) and 1.7 cps/(kBq/mL) was obtained for single and coincidence events respectively. An energy resolution of 35% full-width-half-maximum (FWHM) at 511 keV and a minimum detectable activity of 0.30 ± 0.08 kBq/mL in single mode were obtained. The in vivo AIFs obtained with the detector show an excellent Pearson's correlation (r = 0.996, p < 0.0001) with the ones obtained from well counter analysis of discrete blood samples. Moreover, in vitro experiments demonstrate the capability of the detector to apply the gamma spectroscopic analysis on a mixture of 68Ga and 18F and separate the individual signal emitted from each one.

CONCLUSIONS

Characterization and in vivo evaluation under realistic experimental conditions showed that the detector proposed in this work offers excellent sensibility and stability. The device also showed to successfully separate individual signals emitted from a mixture of radioisotopes. Therefore, the blood sample detector presented in this study allows fully automatic AIFs measurements during single- and multi-tracer PET studies.

Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging

Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which can improve the clinical accuracy of disease diagnosis and can also serve as a research tool for scientists. Most current research on dual-tracer PET reconstruction is based on mixed images pre-reconstructed by algorithms, which restricts the further improvement of the precision of reconstruction. In this study, we present a hybrid loss-guided deep learning based framework for dual-tracer PET imaging using sinogram data, which can achieve reconstruction by naturally unifying two functions: the reconstruction of the mixed images and the separation for individual tracers. Combined with volumetric dual-tracer images, we adopted a three-dimensional (3D) convolutional neural network (CNN) to learn full features, including spatial information and temporal information simultaneously. In addition, an auxiliary loss layer was introduced to guide the reconstruction of the dual tracers. We used Monte Carlo simulations with data augmentation to generate sufficient datasets for training and testing. The results were analyzed by the bias and variance both spatially (different regions of interest) and temporally (different frames). The analysis verified the feasibility of the 3D CNN framework for dual-tracer reconstruction. Furthermore, we compared the reconstruction results with a deep belief network (DBN), which is another deep learning based technique for the separation of dual-tracer images based on time-activity curves (TACs). The comparison results provide insights into the superior features and performance of the 3D CNN. Furthermore, we tested the [¹¹C]FMZ-[¹¹C]DTBZ images with three total-counts levels ([Formula: see text], [Formula: see text], [Formula: see text]), which indicate different noise ratios. The analysis results demonstrate that our method can successfully recover the respective distribution of lower total counts with nearly the same accuracy as that of the higher total counts in the total counts range we applied, which also also indicates the proposed 3D CNN framework is more robust to noise compared with DBN.

Multiplexed imaging for diagnosis and therapy

Complex molecular and metabolic phenotypes depict cancers as a constellation of different diseases with common themes. Precision imaging of such phenotypes requires flexible and tunable modalities capable of identifying phenotypic fingerprints by using a restricted number of parameters while ensuring sensitivity to dynamic biological regulation. Common phenotypes can be detected by in vivo imaging technologies, and effectively define the emerging standards for disease classification and patient stratification in radiology. However, for the imaging data to accurately represent a complex fingerprint, the individual imaging parameters need to be measured and analysed in relation to their wider spatial and molecular context. In this respect, targeted palettes of molecular imaging probes facilitate the detection of heterogeneity in oncogene-driven alterations and their response to treatment, and lead to the expansion of rational-design elements for the combination of imaging experiments. In this Review, we evaluate criteria for conducting multiplexed imaging, and discuss its opportunities for improving patient diagnosis and the monitoring of therapy.

Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems

Positron emission tomography (PET) imaging is based on detecting two time-coincident high-energy photons from the emission of a positron-emitting radioisotope. The physics of the emission, and the detection of the coincident photons, give PET imaging unique capabilities for both very high sensitivity and accurate estimation of the in vivo concentration of the radiotracer. PET imaging has been widely adopted as an important clinical modality for oncological, cardiovascular, and neurological applications. PET imaging has also become an important tool in preclinical studies, particularly for investigating murine models of disease and other small-animal models. However, there are several challenges to using PET imaging systems. These include the fundamental trade-offs between resolution and noise, the quantitative accuracy of the measurements, and integration with X-ray computed tomography and magnetic resonance imaging. In this article, we review how researchers and industry are addressing these challenges.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Hypoxia imaging in gliomas with 18F-fluoromisonidazole PET: toward clinical translation

There is significant interest in the development of improved image-guided therapy for neuro-oncology applications. Glioblastomas (GBM) in particular present a considerable challenge because of their pervasive nature, propensity for recurrence, and resistance to conventional therapies. MRI is routinely used as a guide for planning treatment strategies. However, this imaging modality is not able to provide images that clearly delineate tumor boundaries and affords only indirect information about key tumor pathophysiology. With the emergence of PET imaging with new oncology radiotracers, mapping of tumor infiltration and other important molecular events such as hypoxia is now feasible within the clinical setting. In particular, the importance of imaging hypoxia levels within the tumoral microenvironment is gathering interest, as hypoxia is known to play a central role in glioma pathogenesis and resistance to treatment. One of the hypoxia radiotracers known for its clinical utility is (18)F-fluoromisodazole ((18)F-FMISO). In this review, we highlight the typical causes of treatment failure in gliomas that may be linked to hypoxia and outline current methods for the detection of hypoxia. We also provide an overview of the growing body of studies focusing on the clinical translation of (18)F-FMISO PET imaging, strengthening the argument for the use of (18)F-FMISO hypoxia imaging to help optimize and guide treatment strategies for patients with glioblastoma.

Direct Parametric Image Reconstruction in Reduced Parameter Space for Rapid Multi-Tracer PET Imaging

The separation of multiple PET tracers within an overlapping scan based on intrinsic differences of tracer pharmacokinetics is challenging, due to limited signal-to-noise ratio (SNR) of PET measurements and high complexity of fitting models. In this study, we developed a direct parametric image reconstruction (DPIR) method for estimating kinetic parameters and recovering single tracer information from rapid multi-tracer PET measurements. This is achieved by integrating a multi-tracer model in a reduced parameter space (RPS) into dynamic image reconstruction. This new RPS model is reformulated from an existing multi-tracer model and contains fewer parameters for kinetic fitting. Ordered-subsets expectation-maximization (OSEM) was employed to approximate log-likelihood function with respect to kinetic parameters. To incorporate the multi-tracer model, an iterative weighted nonlinear least square (WNLS) method was employed. The proposed multi-tracer DPIR (MT-DPIR) algorithm was evaluated on dual-tracer PET simulations ([18F]FDG and [11C]MET) as well as on preclinical PET measurements ([18F]FLT and [18F]FDG). The performance of the proposed algorithm was compared to the indirect parameter estimation method with the original dual-tracer model. The respective contributions of the RPS technique and the DPIR method to the performance of the new algorithm were analyzed in detail. For the preclinical evaluation, the tracer separation results were compared with single [18F]FDG scans of the same subjects measured two days before the dual-tracer scan. The results of the simulation and preclinical studies demonstrate that the proposed MT-DPIR method can improve the separation of multiple tracers for PET image quantification and kinetic parameter estimations.

Dual acquisition of (18)F-FMISO and (18)F-FDOPA

Metabolic imaging using positron emission tomography (PET) has found increasing clinical use for the management of infiltrating tumours such as glioma. However, the heterogeneous biological nature of tumours and intrinsic treatment resistance in some regions means that knowledge of multiple biological factors is needed for effective treatment planning. For example, the use of (18)F-FDOPA to identify infiltrative tumour and (18)F-FMISO for localizing hypoxic regions. Performing multiple PET acquisitions is impractical in many clinical settings, but previous studies suggest multiplexed PET imaging could be viable. The fidelity of the two signals is affected by the injection interval, scan timing and injected dose. The contribution of this work is to propose a framework to explicitly trade-off signal fidelity with logistical constraints when designing the imaging protocol. The particular case of estimating (18)F-FMISO from a single frame prior to injection of (18)F-FDOPA is considered. Theoretical experiments using simulations for typical biological scenarios in humans demonstrate that results comparable to a pair of single-tracer acquisitions can be obtained provided protocol timings are carefully selected. These results were validated using a pre-clinical data set that was synthetically multiplexed. The results indicate that the dual acquisition of (18)F-FMISO and (18)F-FDOPA could be feasible in the clinical setting. The proposed framework could also be used to design protocols for other tracers.

(18)F-alfatide II and (18)F-FDG dual-tracer dynamic PET for parametric, early prediction of tumor response to therapy

A single dynamic PET acquisition using multiple tracers administered closely in time could provide valuable complementary information about a tumor's status under quasiconstant conditions. This study aimed to investigate the utility of dual-tracer dynamic PET imaging with (18)F-alfatide II ((18)F-AlF-NOTA-E[PEG4-c(RGDfk)]2) and (18)F-FDG for parametric monitoring of tumor responses to therapy.

METHODS

We administered doxorubicin to one group of athymic nude mice with U87MG tumors and paclitaxel protein-bound particles to another group of mice with MDA-MB-435 tumors. To monitor therapeutic responses, we performed dual-tracer dynamic imaging, in sessions that lasted 90 min, starting with injection via the tail vein catheters with (18)F-alfatide II, followed 40 min later by (18)F-FDG. To achieve signal separation of the 2 tracers, we fit a 3-compartment reversible model to the time-activity curve of (18)F-alfatide II for the 40 min before (18)F-FDG injection and then extrapolated to 90 min. The (18)F-FDG tumor time-activity curve was isolated from the 90-min dual-tracer tumor time-activity curve by subtracting the fitted (18)F-alfatide II tumor time-activity curve. With separated tumor time-activity curves, the (18)F-alfatide II binding potential (Bp = k3/k4) and volume of distribution (VD) and (18)F-FDG influx rate ((K1 × k3)/(k2 + k3)) based on the Patlak method were calculated to validate the signal recovery in a comparison with 60-min single-tracer imaging and to monitor therapeutic response.

RESULTS

The transport and binding rate parameters K1-k3 of (18)F-alfatide II, calculated from the first 40 min of the dual-tracer dynamic scan, as well as Bp and VD correlated well with the parameters from the 60-min single-tracer scan (R(2) > 0.95). Compared with the results of single-tracer PET imaging, (18)F-FDG tumor uptake and influx were recovered well from dual-tracer imaging. On doxorubicin treatment, whereas no significant changes in static tracer uptake values of (18)F-alfatide II or (18)F-FDG were observed, both (18)F-alfatide II Bp and (18)F-FDG influx from kinetic analysis in tumors showed significant decreases. For therapy of MDA-MB-435 tumors with paclitaxel protein-bound particles, a significant decrease was observed only with (18)F-alfatide II Bp value from kinetic analysis but not (18)F-FDG influx.

CONCLUSION

The parameters fitted with compartmental modeling from the dual-tracer dynamic imaging are consistent with those from single-tracer imaging, substantiating the feasibility of this methodology. Even though no significant differences in tumor size were found until 5 d after doxorubicin treatment started, at day 3 there were already substantial differences in (18)F-alfatide II Bp and (18)F-FDG influx rate. Dual-tracer imaging can measure (18)F-alfatide II Bp value and (18)F-FDG influx simultaneously to evaluate tumor angiogenesis and metabolism. Such changes are known to precede anatomic changes, and thus parametric imaging may offer the promise of early prediction of therapy response.

Combined Injection of (18)F-Fluorodeoxyglucose and 3'-Deoxy-3'-[(18)F]fluorothymidine PET Achieves More Complete Identification of Viable Lung Cancer Cells in Mice and Patients than Individual Radiopharmaceutical: A Proof-of-Concept Study

PURPOSE

The objective is to validate the combination of 3'-deoxy-3'-[(18)F]fluorothymidine ((18)F-FLT) and (18)F-fluorodeoxyglucose ((18)F-FDG) as a "novel" positron emission tomography (PET) tracer for better visualization of cancer cell components in solid cancers than individual radiopharmaceutical.

METHODS

Nude mice with subcutaneous xenografts of human non-small cell lung cancer A549 and HTB177 cells and patients with lung cancer were included. In ex vivo study, intratumoral radioactivity of (18)F-FDG, (18)F-FLT, and the cocktail of (18)F-FDG and (18)F-FLT detected by autoradiography was compared with hypoxia (by pimonidazole) and proliferation (by bromodeoxyuridine) in tumor section. In in vivo study, first, (18)F-FDG PET and (18)F-FLT PET were conducted in the same subjects (mice and patients) 10 to 14 hours apart. Second, PET scan was also performed 1 hour after one tracer injection; subsequently, the other was administered and followed the second PET scan in the mouse. Finally, (18)F-FDG and (18)F-FLT cocktail PET scan was also performed in the mouse.

RESULTS

When injected individually, (18)F-FDG highly accumulated in hypoxic zones and high (18)F-FLT in proliferative cancer cells. In case of cocktail injection, high radioactivity correlated with hypoxic regions and highly proliferative and normoxic regions. PET detected that intratumoral distribution of (18)F-FDG and (18)F-FLT was generally mismatched in both rodents and patients. Combination of (18)F-FLT and (18)F-FDG appeared to map more cancer tissue than single-tracer PET.

CONCLUSIONS

Combination of (18)F-FDG and (18)F-FLT PET imaging would give a more accurate representation of total viable tumor tissue than either tracer alone and would be a powerful imaging strategy for cancer management.

Methodology for quantitative rapid multi-tracer PET tumor characterizations

Positron emission tomography (PET) can image a wide variety of functional and physiological parameters in vivo using different radiotracers. As more is learned about the molecular basis for disease and treatment, the potential value of molecular imaging for characterizing and monitoring disease status has increased. Characterizing multiple aspects of tumor physiology by imaging multiple PET tracers in a single patient provides additional complementary information, and there is a significant body of literature supporting the potential value of multi-tracer PET imaging in oncology. However, imaging multiple PET tracers in a single patient presents a number of challenges. A number of techniques are under development for rapidly imaging multiple PET tracers in a single scan, where signal-recovery processing algorithms are employed to recover various imaging endpoints for each tracer. Dynamic imaging is generally used with tracer injections staggered in time, and kinetic constraints are utilized to estimate each tracers' contribution to the multi-tracer imaging signal. This article summarizes past and ongoing work in multi-tracer PET tumor imaging, and then organizes and describes the main algorithmic approaches for achieving multi-tracer PET signal-recovery. While significant advances have been made, the complexity of the approach necessitates protocol design, optimization, and testing for each particular tracer combination and application. Rapid multi-tracer PET techniques have great potential for both research and clinical cancer imaging applications, and continued research in this area is warranted.