Methodology for quantitative rapid multi-tracer PET tumor characterizations

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET

BACKGROUND

Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

METHODS

Clinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference.

RESULTS

CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results.

CONCLUSIONS

CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.

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.

First exploration of the on-treatment changes in tumor and organ uptake of a radiolabeled anti PD-L1 antibody during chemoradiotherapy in patients with non-small cell lung cancer using whole body PET

BACKGROUND

In patients with locally advanced unresectable non-small cell lung cancer (NSCLC), durvalumab, an anti-programmed cell death ligand-1 (PD-L1) antibody, has shown improved overall survival when used as consolidation therapy following concurrent chemoradiotherapy (CRT). However, it is unclear whether CRT itself upregulates PD-L1 expression. Therefore, this study aimed to explore the changes in the uptake of the anti PD-L1 antibody [89Zr]Zr-durvalumab in tumors and healthy organs during CRT in patients with NSCLC.

METHODS

Patients with NSCLC scheduled to undergo CRT were scanned 7±1 days after administration of 37±1 MBq [89Zr]Zr-durvalumab at baseline, 1-week on-treatment and 1 week after finishing 6 weeks of CRT. First, [89Zr]Zr-durvalumab uptake was visually assessed in a low dose cohort with a mass dose of 2 mg durvalumab (0.13% of therapeutic dose) and subsequently, quantification was done in a high dose cohort with a mass dose of 22.5 mg durvalumab (1.5% of therapeutic dose). Tracer pharmacokinetics between injections were compared using venous blood samples drawn in the 22.5 mg cohort. Visual assessment included suspected lesion detectability. Positron emission tomography (PET) uptake in tumoral and healthy tissues was quantified using tumor to plasma ratio (TPR) and organ to plasma ratio, respectively.

RESULTS

In the 2 mg dose cohort, 88% of the 17 identified tumor lesions were positive at baseline, compared with 69% (9/13) for the 22.5 mg cohort. Although the absolute plasma concentrations between patients varied, the intrapatient variability was low. The ten quantitatively assessed lesions in the 22.5 mg cohort had a median TPR at baseline of 1.3 (IQR 0.7-1.5), on-treatment of 1.0 (IQR 0.7-1.4) and at the end of treatment of 0.7 (IQR 0.6-0.7). On-treatment, an increased uptake in bone marrow was seen in three out of five patients together with a decreased uptake in the spleen in four out of five patients.

CONCLUSIONS

This study successfully imaged patients with NSCLC with [89Zr]Zr-durvalumab PET before and during CRT. Our data did not show any increase in [89Zr]Zr-durvalumab uptake in the tumor 1-week on-treatment and at the end of treatment. The changes observed in bone marrow and spleen may be due to an CRT-induced effect on immune cells.

TRIAL REGISTRATION NUMBER

EudraCT number: 2019-004284-51.

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

Review of biomarkers for response to immunotherapy in HNSCC microenvironment

Head and neck squamous cell carcinoma are one of the most common types of cancer worldwide. Although a variety of treatment methods such as surgery, radiotherapy, chemotherapy, and targeted therapy are widely used in diagnosing and treating HNSCC, the survival prognosis of patients has not been significantly improved in the past decades. As an emerging treatment approach, immunotherapy has shown exciting therapeutic effects in R/M HNSCC. However, the current screening methods are still insufficient, and there is a significant need for reliable predictive biomarkers for personalized clinical management and new therapeutic strategies. This review summarized the application of immunotherapy in HNSCC, comprehensively analyzed the existing bioinformatic studies on immunotherapy in HNSCC, evaluated the current methods of tumor immune heterogeneity and immunotherapy, and aimed to screen molecular markers with potential predictive significance. Among them, PD-1 has obvious predictive relevance as the target of existing immune drugs. Clonal TMB is a potential biomarker for HNSCC immunotherapy. The other molecules, including IFN-γ, CXCL, CTLA-4, MTAP, SFR4/CPXM1/COL5A1, TILs, CAFs, exosomes, and peripheral blood indicators, may have suggestive significance for tumor immune microenvironment and prognosis of immunotherapy.

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.

Impact of residual subtraction on myocardial blood flow and reserve estimates from rapid dynamic PET protocols

BACKGROUND

13N-ammonia and 18F-flurpiridaz require longer delays between rest and stress studies to allow for decay, lowering clinical throughput. In this study, we investigated the impact of residual subtraction on MBF and MFR estimates, as well as its effects on diagnostic accuracy.

METHODS

We retrospectively analyzed 63 patients who underwent a dynamic ammonia rest/stress study and 231 patients from the flurpiridaz 301 trial. Residual subtraction was performed by subtracting the mean pre-injection activity in each sampled region from that region's time activity curve. Corrected and uncorrected MBF and MFR were analyzed. Diagnostic accuracy was compared to quantitative coronary angiograms (QCA) for the flurpiridaz population.

RESULTS

With delays between injections above 3 half-lives, and a doubled stress dose, residual activity did not meaningfully increase ammonia MBF (< 5%). For shorter injection delays, stress MBF was overestimated by 13.6% ± 5.0% (P < .001). Residual activity had a large effect on flurpiridaz stress MBF, overestimating it by 37.9% ± 23.2% (P < .001). Comparison to QCA showed a significant improvement in AUC with residual subtraction (from 0.748 to 0.831, P = .001). MFR yielded similar results.

CONCLUSIONS

Accounting for residual activity has a marked impact on stress MBF and MFR and improves diagnostic accuracy relative to QCA.

Simultaneous PET/MRI: The future gold standard for characterizing motor neuron disease-A clinico-radiological and neuroscientific perspective

Neuroimaging assessment of motor neuron disease has turned into a cornerstone of its clinical workup. Amyotrophic lateral sclerosis (ALS), as a paradigmatic motor neuron disease, has been extensively studied by advanced neuroimaging methods, including molecular imaging by MRI and PET, furthering finer and more specific details of the cascade of ALS neurodegeneration and symptoms, facilitated by multicentric studies implementing novel methodologies. With an increase in multimodal neuroimaging data on ALS and an exponential improvement in neuroimaging technology, the need for harmonization of protocols and integration of their respective findings into a consistent model becomes mandatory. Integration of multimodal data into a model of a continuing cascade of functional loss also calls for the best attempt to correlate the different molecular imaging measurements as performed at the shortest inter-modality time intervals possible. As outlined in this perspective article, simultaneous PET/MRI, nowadays available at many neuroimaging research sites, offers the perspective of a one-stop shop for reproducible imaging biomarkers on neuronal damage and has the potential to become the new gold standard for characterizing motor neuron disease from the clinico-radiological and neuroscientific perspectives.

Temporal information guided dynamic dual-tracer PET signal separation network

PURPOSE

The difficulty of dynamic dual-tracer positron emission tomography (PET) technology is to separate the complete single-tracer information from mixed dual-tracer. Traditional methods cannot separate single injection single-scan dynamic dual-tracer PET images. In this paper, we propose a deep learning framework based on gated recurrent unit (GRU) network and evaluate its performance with simulation experiments and realistic monkey data.

METHODS

The proposed single-scan dynamic dual-tracer PET image separation network consists of three parts, including encoder, separation and decoder module. Encoder part is to map the mixed time activity curves (TACs) from the low-dimensional space to the high-dimensional space to get mixed weight vector matrix. Separation part is to capture the temporal information of mixed weight vector matrix using bi-directional GRU (bi-GRU) layer to obtain the single-tracer masks, and the decoding part remaps the high-dimensional single-tracer weight vector matrix to the low-dimensional space to obtain two separated single tracers.

RESULTS

In the simulation experiments under different tracers, phantoms, noise levels, arterial input function (AIF) and k-parameter with Gaussian random, compared to the stacked auto encoder (SAE) network and traditional background subtraction method, GRU-based network has better performance with low bias and mean squared error (MSE). In the realistic study, the image results of GRU network have higher mean structural similarity (MSSIM), and peak signal to noise ratio (PSNR).

CONCLUSIONS

This study demonstrates the feasibility of temporal information guided neural network in single-injection single-scan dynamic dual-tracer PET images separation. The GRU-based network uses TAC temporal information without AIFs to make the separation results more robust and accurate, which significantly outperforms state-of-the-art method qualitatively and quantitatively. This article is protected by copyright. All rights reserved.

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.

PET Use in Cancer Diagnosis, Treatment, and Prognosis

Tumorigenesis is a multistep process marked by variations in numerous metabolic pathways that affect cellular architectures and functions. Cancer cells reprogram their energy metabolism to enable several basic molecular functions, including membrane biosynthesis, receptor regulations, bioenergetics, and redox stress. In recent years, cancer diagnosis and treatment strategies have targeted these specific metabolic changes and the tumor's interactions with its microenvironment. Positron emission tomography (PET) captures all molecular alterations leading to abnormal function and cancer progression. As a result, the development of PET radiotracers increasingly focuses on irregular biological pathways or cells that overexpress receptors that have the potential to function as biomarkers for early diagnosis and treatment measurements as well as research. This chapter reviews both established and evolving PET radiotracers used to image tumor biology. We have also included a few advantages and disadvantages of the routinely used PET radiotracers in cancer imaging.

Total-Body PET Kinetic Modeling and Potential Opportunities Using Deep Learning

The uEXPLORER total-body PET/CT system provides a very high level of detection sensitivity and simultaneous coverage of the entire body for dynamic imaging for quantification of tracer kinetics. This article describes the fundamentals and potential benefits of total-body kinetic modeling and parametric imaging focusing on the noninvasive derivation of blood input function, multiparametric imaging, and high-temporal resolution kinetic modeling. Along with its attractive properties, total-body kinetic modeling also brings significant challenges, such as the large scale of total-body dynamic PET data, the need for organ and tissue appropriate input functions and kinetic models, and total-body motion correction. These challenges, and the opportunities using deep learning, are discussed.

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.

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.

Hypoxia dynamics on FMISO-PET in combination with PD-1/PD-L1 expression has an impact on the clinical outcome of patients with Head-and-neck Squamous Cell Carcinoma undergoing Chemoradiation

Tumor-associated hypoxia influences the radiation response of head-and-neck cancer (HNSCC) patients, and a lack of early hypoxia resolution during treatment considerably deteriorates outcomes. As the detrimental effects of hypoxia are partly related to the induction of an immunosuppressive microenvironment, we investigated the interaction between tumor hypoxia dynamics and the PD-1/PD-L1 axis in HNSCC patients undergoing chemoradiation and its relevance for patient outcomes in a prospective trial.

Methods: 49 patients treated with definitive chemoradiation for locally advanced HNSCC were enrolled in this trial and received longitudinal hypoxia PET imaging using fluorine-18 misonidazole ([¹⁸F]FMISO) at weeks 0, 2 and 5 during treatment. Pre-therapeutic tumor biopsies were immunohistochemically analyzed regarding the PD-1/PD-L1 expression both on immune cells and on tumor cells, and potential correlations between the PD-1/PD-L1 axis and tumor hypoxia dynamics during chemoradiation were assessed using Spearman's rank correlations. Hypoxia dynamics during treatment were quantified by subtracting the standardized uptake value (SUV) index at baseline from the SUV values at weeks 2 or 5, whereby SUV index was defined as ratio of maximum tumor [¹⁸F]FMISO SUV to mean SUV in the contralateral sternocleidomastoid muscle (i.e. tumor-to-muscle ratio). The impact of the PD-1/PD-L1 expression alone and in combination with persistent tumor hypoxia on locoregional control (LRC), progression-free survival (PFS) and overall survival (OS) was examined using log-rank tests and Cox proportional hazards models.

Results: Neither PD-L1 nor PD-1 expression levels on tumor-infiltrating immune cells influenced LRC (HR = 0.734; p = 0.480 for PD-L1, HR = 0.991; p = 0.989 for PD-1), PFS (HR = 0.813; p = 0.597 for PD-L1, HR = 0.796; p = 0.713 for PD-1) or OS (HR = 0.698; p = 0.405 for PD-L1, HR = 0.315; p = 0.265 for PD-1). However, patients with no hypoxia resolution between weeks 0 and 2 and PD-L1 expression on tumor cells, quantified by a tumor proportional score (TPS) of at least 1%, showed significantly worse LRC (HR = 3.374, p = 0.022) and a trend towards reduced PFS (HR = 2.752, p = 0.052). In the multivariate Cox regression analysis, the combination of absent tumor hypoxia resolution and high tumoral PD-L1 expression remained a significant prognosticator for impaired LRC (HR = 3.374, p = 0.022). On the other side, tumoral PD-L1 expression did not compromise the outcomes of patients whose tumor-associated hypoxia declined between week 0 and 2 during chemoradiation (LRC: HR = 1.186, p = 0.772, PFS: HR = 0.846, p = 0.766).

Conclusion: In this exploratory analysis, we showed for the first time that patients with both persistent tumor-associated hypoxia during treatment and PD-L1 expression on tumor cells exhibited a worse outcome, while the tumor cells' PD-L1 expression did not influence the outcomes of patients with early tumor hypoxia resolution. While the results have to be validated in an independent cohort, these findings form a foundation to investigate the combination of hypoxic modification and immune checkpoint inhibitors for the unfavorable subgroup, moving forward towards personalized radiation oncology treatment.

Current Applications for Nuclear Medicine Imaging in Pulmonary Disease

Purpose of Review

The main goal of the article is to familiarize the reader with commonly and uncommonly used nuclear medicine procedures that can significantly contribute to improved patient care. The article presents examples of specific modality utilization in the chest including assessment of lung ventilation and perfusion, imaging options for broad range of infectious and inflammatory processes, and selected aspects of oncologic imaging. In addition, rapidly developing new techniques utilizing molecular imaging are discussed.

Recent Findings

The article describes nuclear medicine imaging modalities including gamma camera, SPECT, PET, and hybrid imaging (SPECT/CT, PET/CT, and PET/MR) in the context of established and emerging clinical applications. Areas of potential future development in nuclear medicine are discussed with emphasis on molecular imaging and implementation of new targeted tracers used in diagnostics and therapeutics (theranostics).

Summary

Nuclear medicine and molecular imaging provide many unique and novel options for the diagnosis and treatment of pulmonary diseases. This article reviews current applications for nuclear medicine and molecular imaging and selected future applications for radiopharmaceuticals and targeted molecular imaging techniques.

A novel tracer for in vivo optical imaging of fatty acid metabolism in the heart and brown adipose tissue

Multiplexed imaging is essential for the evaluation of substrate utilization in metabolically active organs, such as the heart and brown adipose tissue (BAT), where substrate preference changes in pathophysiologic states. Optical imaging provides a useful platform because of its low cost, high throughput and intrinsic ability to perform composite readouts. However, the paucity of probes available for in vivo use has limited optical methods to image substrate metabolism. Here, we present a novel near-infrared (NIR) free fatty acid (FFA) tracer suitable for in vivo imaging of deep tissues such as the heart. Using click chemistry, Alexa Fluor 647 DIBO Alkyne was conjugated to palmitic acid. Mice injected with 0.05 nmol/g bodyweight of the conjugate (AlexaFFA) were subjected to conditions known to increase FFA uptake in the heart (fasting) and BAT [cold exposure and injection with the β₃ adrenergic agonist CL 316, 243(CL)]. Organs were subsequently imaged both ex vivo and in vivo to quantify AlexaFFA uptake. The blood kinetics of AlexaFFA followed a two-compartment model with an initial fast compartment half-life of 0.14 h and a subsequent slow compartment half-life of 5.2 h, consistent with reversible protein binding. Ex vivo fluorescence imaging after overnight cold exposure and fasting produced a significant increase in AlexaFFA uptake in the heart (58 ± 12%) and BAT (278 ± 19%) compared to warm/fed animals. In vivo imaging of the heart and BAT after exposure to CL and fasting showed a significant increase in AlexaFFA uptake in the heart (48 ± 20%) and BAT (40 ± 10%) compared to saline-injected/fed mice. We present a novel near-infrared FFA tracer, AlexaFFA, that is suitable for in vivo quantification of FFA metabolism and can be applied in the context of a low cost, high throughput, and multiplexed optical imaging platform.

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.

Correlation between positron emission tomography and Cerenkov luminescence imaging in vivo and ex vivo using 64Cu-labeled antibodies in a neuroblastoma mouse model

Antibody-based therapies gain momentum in clinical therapy, thus the need for accurate imaging modalities with respect to target identification and therapy monitoring are of increasing relevance. Cerenkov luminescence imaging (CLI) are a novel method detecting charged particles emitted during radioactive decay with optical imaging. Here, we compare Position Emission Tomography (PET) with CLI in a multimodal imaging study aiming at the fast and efficient screening of monoclonal antibodies (mAb) designated for targeting of the neuroblastoma-characteristic epitope disialoganglioside GD2. Neuroblastoma-bearing SHO mice were injected with a ⁶⁴Cu-labeled GD2-specific mAb. The tumor uptake was imaged 3 h, 24 h and 48 h after tracer injection with both, PET and CLI, and was compared to the accumulation in GD2-negative control tumors (human embryonic kidney, HEK-293). In addition to an in vivo PET/CLI-correlation over time, we also demonstrate linear correlations of CLI- and γ-counter-based biodistribution analysis. CLI with its comparably short acquisition time can thus be used as an attractive one-stop-shop modality for the longitudinal monitoring of antibody-based tumor targeting and ex vivo biodistribution.

These findings suggest CLI as a reliable alternative for PET and biodistribution studies with respect to fast and high-throughput screenings in subcutaneous tumors traced with radiolabeled antibodies. However, in contrast to PET, CLI is not limited to positron-emitting isotopes and can therefore also be used for the visualization of mAb labeled with therapeutic isotopes like electron emitters.

Monitoring early response to chemoradiotherapy with 18F-FMISO dynamic PET in head and neck cancer

PURPOSE

There is growing recognition that biologic features of the tumor microenvironment affect the response to cancer therapies and the outcome of cancer patients. In head and neck cancer (HNC) one such feature is hypoxia. We investigated the utility of ¹⁸F-fluoromisonidazole (FMISO) dynamic positron emission tomography (dPET) for monitoring the early microenvironmental response to chemoradiotherapy in HNC.

EXPERIMENTAL DESIGN

Seventy-two HNC patients underwent FMISO dPET scans in a customized immobilization mask (0-30 min dynamic acquisition, followed by 10 min static acquisitions starting at ∼95 min and ∼160 min post-injection) at baseline and early into treatment where patients have already received one cycle of chemotherapy and anywhere from five to ten fractions of 2 Gy per fraction radiation therapy. Voxelwise pharmacokinetic modeling was conducted using an irreversible one-plasma two-tissue compartment model to calculate surrogate biomarkers of tumor hypoxia (k ₃ and Tumor-to-Blood Ratio (TBR)), perfusion (K ₁ ) and FMISO distribution volume (DV). Additionally, Tumor-to-Muscle Ratios (TMR) were derived by visual inspection by an experienced nuclear medicine physician, with TMR > 1.2 defining hypoxia.

RESULTS

One hundred and thirty-five lesions in total were analyzed. TBR, k ₃ and DV decreased on early response scans, while no significant change was observed for K ₁ . The k ₃ -TBR correlation decreased substantially from baseline scans (Pearson's r = 0.72 and 0.76 for mean intratumor and pooled voxelwise values, respectively) to early response scans (Pearson's r = 0.39 and 0.40, respectively). Both concordant and discordant examples of changes in intratumor k ₃ and TBR were identified; the latter partially mediated by the change in DV. In 13 normoxic patients according to visual analysis (all having lesions with TMR = 1.2), subvolumes were identified where k ₃ indicated the presence of hypoxia.

CONCLUSION

Pharmacokinetic modeling of FMISO dynamic PET reveals a more detailed characterization of the tumor microenvironment and assessment of response to chemoradiotherapy in HNC patients than a single static image does. In a clinical trial where absence of hypoxia in primary tumor and lymph nodes would lead to de-escalation of therapy, the observed disagreement between visual analysis and pharmacokinetic modeling results would have affected patient management in <20% cases. While simple static PET imaging is easily implemented for clinical trials, the clinical applicability of pharmacokinetic modeling remains to be investigated.

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.

The Landscape of Clinical Trials Evaluating the Theranostic Role of PET Imaging in Oncology: Insights from an Analysis of ClinicalTrials.gov Database

In the war on cancer marked by personalized medicine, positron emission tomography (PET)-based theranostic strategy is playing an increasingly important role. Well-designed clinical trials are of great significance for validating the PET applications and ensuring evidence-based cancer care. This study aimed to provide a comprehensive landscape of the characteristics of PET clinical trials using the substantial resource of ClinicalTrials.gov database. We identified 25,599 oncology trials registered with ClinicalTrials.gov in the last ten-year period (October 2005-September 2015). They were systematically reviewed to validate classification into 519 PET trials and 25,080 other oncology trials used for comparison. We found that PET trials were predominantly phase 1-2 studies (86.2%) and were more likely to be single-arm (78.9% vs. 57.9%, P <0.001) using non-randomized assignment (90.1% vs. 66.7%, P <0.001) than other oncology trials. Furthermore, PET trials were small in scale, generally enrolling fewer than 100 participants (20.3% vs. 25.7% for other oncology trials, P = 0.014), which might be too small to detect a significant theranostic effect. The funding support from industry or National Institutes of Health shrunk over time (both decreased by about 5%), and PET trials were more likely to be conducted in only one region lacking international collaboration (97.0% vs. 89.3% for other oncology trials, P <0.001). These findings raise concerns that clinical trials evaluating PET imaging in oncology are not receiving the attention or efforts necessary to generate high-quality evidence. Advancing the clinical application of PET imaging will require a concerted effort to improve the quality of trials.

Physics of pure and non-pure positron emitters for PET: a review and a discussion

With the increased interest in new PET tracers, gene-targeted therapy, immunoPET, and theranostics, other radioisotopes will be increasingly used in clinical PET scanners, in addition to ¹⁸F. Some of the most interesting radioisotopes with prospective use in the new fields are not pure short-range β⁺ emitters but can be associated with gamma emissions in coincidence with the annihilation radiation (prompt gamma), gamma-gamma cascades, intense Bremsstrahlung radiation, high-energy positrons that may escape out of the patient skin, and high-energy gamma rays that result in some e⁺/e⁻ pair production. The high level of sophistication in data correction and excellent quantitative accuracy that has been reached for ¹⁸F in recent years can be questioned by these effects. In this work, we review the physics and the scientific literature and evaluate the effect of these additional phenomena on the PET data for each of a series of radioisotopes: ¹¹C, ¹³N, ¹⁵O, ¹⁸F, ⁶⁴Cu, ⁶⁸Ga, ⁷⁶Br, ⁸²Rb, ⁸⁶Y, ⁸⁹Zr, ⁹⁰Y, and ¹²⁴I. In particular, we discuss the present complications arising from the prompt gammas, and we review the scientific literature on prompt gamma correction. For some of the radioisotopes considered in this work, prompt gamma correction is definitely needed to assure acceptable image quality, and several approaches have been proposed in recent years. Bremsstrahlung photons and ¹⁷⁶Lu background were also evaluated.

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.

Artifact suppression in electron paramagnetic resonance imaging of (14)N- and (15)N-labeled nitroxyl radicals with asymmetric absorption spectra

This article describes an improved method for suppressing image artifacts in the visualization of (14)N- and (15)N-labeled nitroxyl radicals in a single image scan using electron paramagnetic resonance (EPR). The purpose of this work was to solve the problem of asymmetric EPR absorption spectra in spectral processing. A hybrid function of Gaussian and Lorentzian lineshapes was used to perform spectral line-fitting to successfully separate the two kinds of nitroxyl radicals. This approach can process the asymmetric EPR absorption spectra of the nitroxyl radicals being measured, and can suppress image artifacts due to spectral asymmetry. With this improved visualization method and a 750-MHz continuous-wave EPR imager, a temporal change in the distributions of a two-phase paraffin oil and water/glycerin solution system was visualized using lipophilic and hydrophilic nitroxyl radicals, i.e., 2-(14-carboxytetradecyl)-2-ethyl-4,4-dimethyl-3-oxazolidinyloxy (16-DOXYL stearic acid) and 4-hydroxyl-2,2,6,6-tetramethylpiperidine-d17-1-(15)N-1-oxyl (TEMPOL-d17-(15)N). The results of the two-phase separation experiment verified that reasonable artifact suppression could be achieved by the present method that deals with asymmetric absorption spectra in the EPR imaging of (14)N- and (15)N-labeled nitroxyl radicals.

Quantitative methods for molecular diagnostic and therapeutic imaging

This theme issue provides an overview on the basic quantitative methods, an in-depth discussion on the cutting-edge quantitative analysis approaches as well as their applications for both static and dynamic molecular diagnostic and therapeutic imaging.