Positron Emission Tomography Imaging Research Articles

Deep learning reconstruction with uncertainty estimation for [formula omitted] photon interaction in fast scintillator detectors

This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate the 2-dimensional gamma photon interaction coordinates in a fast lead tungstate (PbWO4) monolithic scintillator detector. We introduce a custom loss function to estimate the inherent uncertainties associated with the reconstruction process and to incorporate the physical constraints of the detector.This unique combination allows for more robust and reliable position estimations and the obtained results demonstrate the effectiveness of the proposed approach and highlights the significant benefits of the uncertainties estimation. We discuss its potential impact on improving PET imaging quality and show how the results can be used to improve the exploitation of the model, to bring benefits to the application and how to evaluate the validity of the given prediction and the associated uncertainties. Importantly, our proposed methodology extends beyond this specific use case, as it can be generalized to other applications beyond PET imaging.

Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization.

In this study, we compared the repeatability and reproducibility of radiomic features obtained from positron emission tomography (PET) images according to the reconstruction algorithm used-advanced reconstruction algorithms, such as HYPER iterative (IT), HYPER deep learning reconstruction (DLR), and HYPER deep progressive reconstruction (DPR), or traditional Ordered Subset Expectation Maximization (OSEM)-to understand the potential variations and implications of using advanced reconstruction techniques in PET-based radiomics. We used a heterogeneous phantom with acrylic spherical beads (4- or 8-mm diameter) filled with 18F. PET images were acquired and reconstructed using OSEM, IT, DLR, and DPR. Original and wavelet radiomic features were calculated using SlicerRadiomics. Radiomic feature repeatability was assessed using the Coefficient of Variance (COV) and intraclass correlation coefficient (ICC), and inter-acquisition time reproducibility was assessed using the concordance correlation coefficient (CCC). For the 4- and 8-mm diameter beads phantom, the proportion of radiomic features with a COV < 10% was equivocal or higher for the advanced reconstruction algorithm than for OSEM. ICC indicated that advanced methods generally outperformed OSEM in repeatability, except for the original features of the 8-mm beads phantom. In the inter-acquisition time reproducibility analysis, the combinations of 3 and 5min exhibited the highest reproducibility in both phantoms, with IT and DPR showing the highest proportion of radiomic features with CCC > 0.8. Advanced reconstruction methods provided enhanced stability of radiomic features compared with OSEM, suggesting their potential for optimal image reconstruction in PET-based radiomics, offering potential benefits in clinical diagnostics and prognostics.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

Molecular Imaging of Alzheimer's Disease-Related Sigma-1 Receptor in the Brain via a Novel Ru-Mediated Aromatic 18F-deoxyfluorination Probe.

Sigma-1 receptor (σ1R) is an intracellular protein implicated in a spectrum of neurodegenerative conditions, notably Alzheimer's disease (AD). Positron emission tomography (PET) imaging of brain σ1R could provide a powerful tool for better understanding the underlying pathomechanism of σ1R in AD. In this study, we successfully developed a 18F-labeled σ1R radiotracer [18F]CNY-05 via an innovative ruthenium (Ru)-mediated 18F-deoxyfluorination method. [18F]CNY-05 exhibited preferable brain uptake, high specific binding, and slightly reversible pharmacokinetics within the PET scanning time window. PET imaging of [18F]CNY-05 in nonhuman primates (NHP) indicated brain permeability, metabolic stability, and safety. Moreover, autoradiography and PET studies of [18F]CNY-05 in the AD mouse model found a significantly decreased brain uptake compared to that in wild-type mice. Collectively, we have provided a novel 18F-radiolabeled σ1R PET probe, which enables visualizing brain σ1R in health and neurological diseases.

Protein restriction during pregnancy alters Cdkn1c silencing, dopamine circuitry and offspring behaviour without changing expression of key neuronal marker genes

We tracked the consequences of in utero protein restriction in mice throughout their development and life course using a luciferase-based allelic reporter of imprinted Cdkn1c. Exposure to gestational low-protein diet (LPD) results in the inappropriate expression of paternally inherited Cdkn1c in the brains of embryonic and juvenile mice. These animals were characterised by a developmental delay in motor skills, and by behavioural alterations indicative of reduced anxiety. Exposure to LPD in utero resulted in significantly more tyrosine hydroxylase positive (dopaminergic) neurons in the midbrain of adult offspring as compared to age-matched, control-diet equivalents. Positron emission tomography (PET) imaging revealed an increase in striatal dopamine synthesis capacity in LPD-exposed offspring, where elevated levels of dopamine correlated with an enhanced sensitivity to cocaine. These data highlight a profound sensitivity of the developing epigenome to gestational protein restriction. Our data also suggest that loss of Cdkn1c imprinting and p57KIP2 upregulation alters the cellular composition of the developing midbrain, compromises dopamine circuitry, and thereby provokes behavioural abnormalities in early postnatal life. Molecular analyses showed that despite this phenotype, exposure to LPD solely during pregnancy did not significantly change the expression of key neuronal- or dopamine-associated marker genes in adult offspring.

Are central and systemic inflammation associated with fatigue in cerebral small vessel disease?

Fatigue is a common symptom in cerebral small vessel disease (SVD), but its pathogenesis is poorly understood. It has been suggested that inflammation may play a role. We determined whether central (neuro) inflammation and peripheral inflammation were associated with fatigue in SVD. Notably, 36 patients with moderate-to-severe SVD underwent neuropsychometric testing, combined positron emission tomography and magnetic resonance imaging (PET-MRI) scan, and blood draw for the analysis of inflammatory blood biomarkers. Microglial signal was taken as a proxy for neuroinflammation, assessed with radioligand 11C-PK11195. Of these, 30 subjects had full PET datasets for analysis. We assessed global 11C-PK11195 binding and hotspots of 11C-PK11195 binding in the normal-appearing white matter, lesioned tissue, and combined total white matter. Peripheral inflammation was assessed with serum C-reactive protein (CRP) and using the Olink cardiovascular III proteomic panel comprising 92 biomarkers of cardiovascular inflammation and endothelial activation. Fatigue was assessed using the fatigue severity scale (FSS), the visual analog fatigue scale, and a subscale of the Geriatric Depression Scale. Mean (SD) age was 68.7 (11.2) years, and 63.9% were male. Of these, 55.6% showed fatigue on the FSS. Fatigued participants had higher disability scores (p = 0.02), higher total GDS scores (p = 0.02), and more commonly reported a history of depression (p = 0.04). 11C-PK11195 ligand binding in the white matter was not associated with any measure of fatigue. Serum CRP was significantly associated with average fatigue score on FSS (ρ = 0.48, p = 0.004); this association persisted when controlling for age, sex, disability score, and depression (β = 0.49, 95% CI (0.17, 2.26), p = 0.03). Blood biomarkers from the Olink panel showed no association with fatigue. In symptomatic SVD patients, neuroinflammation, assessed with microglial marker 11C-PK11195, was not associated with fatigue. We found some evidence for a role of systematic inflammation, evidenced by an association between fatigue severity and raised CRP, but further studies are required to understand this relationship and inform whether it could be therapeutically modified to reduce fatigue severity. Data for this study are available from the corresponding author upon reasonable request.

Deep learned triple-tracer multiplexed PET myocardial image separation

IntroductionIn multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling method (MTCM) requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.MethodsIn this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. Dynamic triple-tracer noisy MLEM reconstruction was used as the network input and dynamic single-tracer noisy MLEM reconstructions were used as the training labels.ResultsA simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([18F]FDG+82Rb+[94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and ROI levels.DiscussionAs compared to the MTCM separation, the proposed method uses spatiotemporal information for separation, which enhances the separation performance at both the voxel and ROI levels. The simulation study also indicates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies.

Legumain-Triggered Macrocyclization of Radiofluorinated Tracer for Enhanced PET Imaging.

Enhancing the accumulation and retention of small-molecule probes in tumors is an important way to achieve accurate cancer diagnosis and therapy. Enzyme-stimulated macrocyclization of small molecules possesses great potential for enhanced positron emission tomography (PET) imaging of tumors. Herein, we reported an 18F-labeled radiotracer [18F]AlF-RSM for legumain detection in vivo. The tracer was prepared by a one-step aluminum-fluoride-restrained complexing agent ([18F]AlF-RESCA) method with high radiochemical yield (RCY) (88.35 ± 3.93%) and radiochemical purity (RCP) (>95%). More notably, the tracer can be transformed into a hydrophobic macrocyclic molecule under the joint action of legumain and reductant. Simultaneously, the tracer could target legumain-positive tumors and enhance accumulation and retention in tumors, resulting in the amplification of PET imaging signals. The enhancement of radioactivity enables PET imaging of legumain activity with high specificity. We envision that, by combining this highly efficient 18F-labeled strategy with our intramolecular macrocyclization reaction, a range of radiofluorinated tracers can be designed for tumor PET imaging and early cancer diagnosis in the future.

Two Novel [68Ga]Ga-Labeled Radiotracers Based on Metabolically Stable [Sar11]RM26 Antagonistic Peptide for Diagnostic Positron Emission Tomography Imaging of GRPR-Positive Prostate Cancer

Two Novel [⁶⁸Ga]Ga-Labeled Radiotracers Based on Metabolically Stable [Sar¹¹]RM26 Antagonistic Peptide for Diagnostic Positron Emission Tomography Imaging of GRPR-Positive Prostate Cancer

In vivo MRI and PET imaging in a translational ILD mouse model expressing non-resolving fibrosis and bronchiectasis-like pathology after repeated systemic exposure to bleomycin

Drug-induced interstitial lung disease (ILD) is crucial to detect early to achieve the best treatment outcome. Optimally, non-invasive imaging biomarkers can be used for early detection of disease progression and treatment follow-up. Therefore, reliable in vivo models are warranted in new imaging biomarker development to accelerate better-targeted treatment options. Single-dose bleomycin models have, for a long time, served as a reference model in fibrosis and lung injury research. Here, we aimed to use a clinically more relevant animal model by systemic exposure to bleomycin and assessing disease progression over time by combined magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging.MethodsC57BL/6 mice received bleomycin (i.p. 35iU/kg) or saline as control twice per week for 4 weeks. Mice were monitored until 2 weeks after cessation of bleomycin administration (w4 + 1 and w4 + 2), referred to as the resting period. MRI scans were performed in weeks 3 and 4 and during the resting weeks. [18F]FDG-PET was performed at the last week of dosing (w4) and 2 weeks after the last dosing (w4 + 2). Lung tissue sections were stained with Masson’s trichrome and evaluated by modified Ashcroft scoring. Lung volume and lesion volumes were assessed using MRI, as well as 3D mapping of the central airways.Results and discussionBleomycin-challenged mice showed increased lung weights (p &amp;lt; 0.05), while total lung volume was unchanged (w4 and onward). Histology analysis demonstrated fibrotic lesions emanating from the distal parts of the lung. Fibrosis progression was visualized by MRI with significantly increased high signal in bleomycin-exposed lungs compared to controls (p &amp;lt; 0.05). In addition, a significant increase in central airway diameter (p &amp;lt; 0.01) was displayed in bleomycin-exposed animals compared to controls and further continued to dilate as the disease progressed, comparing the bleomycin groups over time (p &amp;lt; 0.05–0.001). Lung [18F]FDG uptake was significantly elevated in bleomycin-exposed mice compared to controls (p &amp;lt; 0.05).ConclusionNon-invasive imaging displayed progressing lesions in the lungs of bleomycin-exposed mice, using two distinct MRI sequences and [18F]FDG-PET. With observed fibrosis progression emanating from distal lung areas, dilation of the central airways was evident. Taken together, this chronic bleomycin-exposure model is translationally more relevant for studying lung injury in ILD and particularly in the context of DIILD.

Bibliometric Review of Optimization and Image Processing of Positron Emission Tomography (PET) Imaging System between 1981-2022.

PET scan stands as a valuable diagnostic tool in nuclear medicine, enabling the observation of metabolic and physiological changes at a molecular level. However, PET scans have a number of drawbacks, such as poor spatial resolution, noisy images, scattered radiation, artifacts, and radiation exposure. These challenges demonstrate the need for optimization in image processing techniques. Our objective is to identify the evolving trends and impacts of publication in this field, as well as the most productive and influential countries, institutions, authors, themes, and articles. A bibliometric study was conducted using a comprehensive query string such as "positron emission tomography" AND "image processing" AND optimization to retrieve 1,783 publications from 1981 to 2022 found in the Scopus database related to this field of study. The findings revealed that the most influential country, institution, and authors are from the USA, and the most prevalent theme is TOF PET image reconstruction. The increasing trend in publication in the field of optimization of image processing in PET scans would address the challenges in PET scan by reducing radiation exposure, faster scanning speed, as well as enhancing lesion identification.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278±0.122%, PSNR of 33.783±0.824dB, SSIM of 0.964±0.009, NCC of 0.968±0.011, HRS of 4.543, and SUV Error of 0.255±0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973±0.066%, PSNR of 36.172±0.801dB, SSIM of 0.984±0.004, NCC of 0.990±0.005, HRS of 4.428, and SUV Error of 0.151±0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

Clinical [18F]FSPG Positron Emission Tomography Imaging Reveals Heterogeneity in Tumor-Associated System xc- Activity.

(4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid ([18F]FSPG) positron emission tomography/computed tomography (PET/CT) provides a readout of system xc- transport activity and has been used for cancer detection in clinical studies of different cancer types. As system xc- provides the rate-limiting precursor for glutathione biosynthesis, an abundant antioxidant, [18F]FSPG imaging may additionally provide important prognostic information. Here, we performed an analysis of [18F]FSPG radiotracer distribution between primary tumors, metastases, and normal organs from cancer patients. We further assessed the heterogeneity of [18F]FSPG retention between cancer types, and between and within individuals. This retrospective analysis of prospectively collected data compared [18F]FSPG PET/CT in subjects with head and neck squamous cell cancer (HNSCC, n = 5) and non-small-cell lung cancer (NSCLC, n = 10), scanned at different institutions. Using semi-automated regions of interest drawn around tumors and metastases, the maximum standardized uptake value (SUVmax), SUVmean, SUV standard deviation and SUVpeak were measured. [18F]FSPG time-activity curves (TACs) for normal organs, primary tumors and metastases were subsequently compared to 18F-2-fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT at 60 min post injection (p.i.). The mean administered activity of [18F]FSPG was 309.3 ± 9.1 MBq in subjects with NSCLC and 285.1 ± 11.3 MBq in those with HNSCC. The biodistribution of [18F]FSPG in both cohorts showed similar TACs in healthy organs from cancer patients. There was no statistically significant overall difference in the average SUVmax of tumor lesions at 60 min p.i. for NSCLC (8.1 ± 7.1) compared to HNSCC (6.0 ± 4.1; p = 0.29) for [18F]FSPG. However, there was heterogeneous retention between and within cancer types; the SUVmax at 60 min p.i. ranged from 1.4 to 23.7 in NSCLC and 3.1-12.1 in HNSCC. [18F]FSPG PET/CT imaging from both NSCLC and HNSCC cohorts showed the same normal-tissue biodistribution, but marked tumor heterogeneity across subjects and between lesions. Despite rapid elimination through the urinary tract and low normal-background tissue retention, the diagnostic potential of [18F]FSPG was limited by variability in tumor retention. As [18F]FSPG retention is mediated by the tumor's antioxidant capacity and response to oxidative stress, this heterogeneity may provide important insights into an individual tumor's response or resistance to therapy.

3-[18F]Fluoro-para-hydroxyphenethylguanidine (3-[18F]pHPG) PET-A Novel Imaging Modality for Paraganglioma.

Functional positron emission tomography (PET) imaging for the characterization of pheochromocytoma and paraganglioma (PCC/PGL) and for detection of metastases in malignant disease, offers valuable clinical insights that can significantly guide patient treatment. This work aimed to evaluate a novel PET radiotracer, 3-[18F]fluoro-para-hydroxyphenethylguanidine (3-[18F]pHPG), a norepinephrine analogue, for its ability to localize PCC/PGL. 3-[18F]pHPG PET/CT whole-body scans were performed on 16 patients (8 male:8 female; mean age 47.6 ± 17.6 years; range, 19-74 years) with pathologically confirmed or clinically diagnosed PCC/PGL. After intravenous administration of 304 to 475 MBq (8.2-12.8 mCi) of 3-[18F]pHPG, whole-body PET scans were performed at 90 minutes in all patients. 3-[18F]pHPG PET was interpreted for abnormal findings consistent with primary tumor or metastasis, and biodistribution in normal organs recorded. Standardized uptake value (SUV) measurements were obtained for target lesions and physiological organ distributions. 3-[18F]pHPG PET showed high radiotracer uptake and trapping in primary tumors, and metastatic tumor lesions that included bone, lymph nodes, and other solid organ sites. Physiological biodistribution was universally present in salivary glands (parotid, submandibular, sublingual), thyroid, heart, liver, adrenals, kidneys, and bladder. Comparison [68Ga]DOTATATE PET/CT was available in 10 patients and in all cases showed concordant distribution. Comparison [123I]meta-iodobenzylguanidine [123I]mIBG planar scintigraphy and SPECT/CT scans were available for 4 patients, with 3-[18F]pHPG showing a greater number of metastatic lesions. We found the kinetic profile of 3-[18F]pHPG PET affords high activity retention within benign and metastatic PCC/PGL. Therefore, 3-[18F]pHPG PET imaging provides a novel modality for functional imaging and staging of malignant paraganglioma with advantages of high lesion affinity, whole-body coregistered computed tomography, and rapid same-day imaging.

Abstract SY35-02: Spatial mapping of mitochondrial networks in lung cancer

Abstract Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited. Here we present an in vivo structural and functional profiling of mitochondrial networks and bioenergetic phenotypes in non-small cell lung cancer (NSCLC) using an integrated platform comprised of positron emission tomography (PET) imaging, respirometry and 3-dimensional scanning block-face electron microscopy (3D SBEM). Interestingly, the diverse bioenergetic phenotypes and metabolic dependencies we identified in NSCLC tumors align with distinct structural organization of mitochondrial networks present. Further, we discovered that mitochondrial networks are organized into distinct compartments within tumor cells. In tumors with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation we identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. In contrast, we discovered that in tumors with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated i) peri-nuclear localization of mitochondria, ii) structural remodeling of cristae and iii) mitochondrial respiratory capacity. Our findings suggest that in NSCLC, mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumors. Structure-and-function studies defining the relationship between mitochondrial architecture and metabolic dependencies may hold promise as strategies to profile metabolic liabilities unique to lung cancer subtypes. We anticipate that coupling PET imaging with 3D SBEM will have dynamic applications beyond that of lung cancer and enrich our understanding of how mitochondrial bioenergetics impact human disease. Citation Format: David B. Shackelford. Spatial mapping of mitochondrial networks in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY35-02.

Low-dose and standard-dose whole-body [18F]FDG-PET/CT imaging: implications for healthy controls and lung cancer patients

Aim: High-sensitivity hybrid positron emission tomography (PET) imaging using advanced whole-body (WB) or total-body PET/computed tomography (CT) systems permits reducing injected tracer activity while preserving diagnostic quality. Such approaches are promising for healthy control studies or exploring inter-organ communication in systemic diseases. This study assessed test/retest variations in the fluoro-2-deoxy-D-glucose (FDG) uptake in key organs from low-dose (LD) and standard-dose (STD) [18F]FDG-PET/CT imaging protocols in healthy controls and lung cancer patients.Methods: A total of 19 healthy controls (19–62 years, 46–104 kg, 10 M/9 F) and 7 lung cancer patients (47–77 years, 50–88 kg, 4 M/3 F) underwent [18F]FDG-PET/CT imaging. All subjects were first injected (“test,” LD) with 28 ± 2 MBq FDG and underwent a dynamic (0–67 min post-injection) WB imaging protocol with LD-CT. Then, 90 min post-LD injection, the subjects were repositioned and injected with 275 ± 16 MBq FDG (“retest,” STD). Second LD-CT and STD-CT scans were acquired for healthy controls and patients, respectively. Static images (55–67 min post-injection) were considered for subsequent analysis. The CT images were used to automatically segment the target volumes of interest. Standardized uptake values normalized to the body weight (SUVBW) were extracted for each volume of interest. The mean SUVBW were compared for both LD/STD conditions with paired t-tests. In patients, FDG-avid lesions were manually delineated on LD and STD static images. Effective dose levels were estimated from both the CT and PET acquisitions.Results: Organ-based mean SUVBW were similar between the LD and STD (mean %difference ≤5%) in both healthy controls and cancer patients, except in the heart. Intra-control test/retest variability was significant in the brain, heart, and skeletal muscle (p &amp;lt; 0.05). While 17 lesions were delineated on the STD images of the patients, only 10/17 lesions were identified on the LD images due to increased image noise. Lesion-based mean SUVBW were similar between LD and STD acquisitions (p = 0.49, %difference = 10%). In patients, the effective doses were (1.9 ± 0.2) mSv (LD-CT), (16.6 ± 5.4) mSv (STD-CT), (0.5 ± 0.1) mSv (LD-PET), and (4.6 ± 0.3) mSv (STD-PET).Conclusion: LD and STD [18F]FDG injections in healthy controls and lung cancer patients yielded comparable mean SUVBW, except in the heart. Dose levels may be reduced for [18F]FDG-PET imaging without a loss in mean SUVBW accuracy, promoting LD-PET/CT protocols for studying multi-organ metabolic patterns. In oncology patients, this approach may be hindered by a lower diagnostic quality in the presence of significant noise.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features.Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection andSupport Vector Machineclassifier (SVM)with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assessthe impactof ComBat harmonizationon the outcome. From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively.In terms ofpredicting recurrence in NSCLC, ourfindings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increasein sensitivity and specificity. Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Cross noise level PET denoising with continuous adversarial domain generalization

Objective. Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique. Approach. We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%–22% (uniformly randomly selected), or 25% fractions of events from 97 18F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%–22% set. Main results. The proposed CD improves the denoising performance of our model trained in a 13%–22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain. Significance. To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains.

Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising

Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural network architecture without training dataset, whereas the general supervised approach requires massive low-and high-quality PET image pairs. To answer the increased need for PET imaging with DIP, it is indispensable to improve the performance of the underlying DIP itself. Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance. Our proposed pre-training model acquires transferable and generalizable visual representations from only unlabeled PET images by restoring various degraded PET images in a self-supervised approach. We evaluated the proposed method using clinical brain PET data with various radioactive tracers (18F-florbetapir, 11C-Pittsburgh compound-B, 18F-fluoro-2-deoxy-D-glucose, and 15O-CO2) acquired from different PET scanners. The proposed method using the self-supervised pre-training model achieved robust and state-of-the-art denoising performance while retaining spatial details and quantification accuracy compared to other unsupervised methods and pre-training model. These results highlight the potential that the proposed method is particularly effective against rare diseases and probes and helps reduce the scan time or the radiotracer dose without affecting the patients.

[18F]AlF-ND-bisFAPI PET imaging of fibroblast activation protein as a biomarker to monitor the progression of liver fibrosis.

Hepatic fibrosis is a progressive disease, which is reversible in the early stages. The current monitoring methods have notable limitations that pose a challenge to early detection. In this study, we evaluated the utility of [18F]AlF-ND-bisFAPI positron emission tomography imaging of fibroblast activation protein (FAP) to monitor the progression of liver fibrosis. Two mouse models of liver fibrosis were established by bile duct ligation and carbon tetrachloride administration, respectively. Positron emission tomography imaging was performed with the FAP-specific radiotracer [18F]AlF-ND-bisFAPI for the evaluation of rat HSCs and mouse models of fibrosis and combined with histopathology, immunohistochemical staining, and immunoblotting to elucidate the relationships among radioactivity uptake, FAP levels, and liver fibrosis progression. Furthermore, [18F]AlF-ND-bisFAPI autoradiography was performed to assess tracer binding in liver sections from patients with varying degrees of liver fibrosis. Cell experiments demonstrated that [18F]AlF-ND-bisFAPI uptake was specific in activated HSCs. Compared with control mice, [18F]AlF-ND-bisFAPI uptake in livers increased in the early stages of fibrosis and increased significantly further with disease progression. Immunohistochemistry and western blot analyses demonstrated that FAP expression increased with fibrosis severity. In accordance with the findings in animal models, ex vivo autoradiography on human fibrotic liver sections showed that radioactivity increased as fibrosis progressed from mild to severe. [18F]AlF-ND-bisFAPI positron emission tomography imaging is a promising noninvasive method for monitoring the progression of liver fibrosis.