Positron Emission Tomography Imaging Research Articles

Towards the Stable Chelation of Radioantimony(V) for Targeted Auger Theranostics.

Antimony-119 (119Sb) is one of the most attractive Auger-electron emitters identified to date, but it remains practically unexplored for targeted radiotherapy because no chelators have been identified to stably bind this metalloid in vivo. In a departure from current studies focused on chelator development for Sb(III), we explore the chelation chemistry of Sb(V) using the tris-catecholate ligand TREN-CAM. Through a combination of radiolabeling, spectroscopic, solid-state, and computational studies, the radiochemistry and structural chemistry of TREN-CAM with 1XX/natSb(V) were established. The resulting [1XXSb]Sb-TREN-CAM complex remained intact for several days in human serum, signifying high stability under biological conditions. Finally, the first in vivo single photon emission computed tomography and positron emission tomography imaging studies were carried out using 117Sb, the diagnostic analogue of 119Sb. These studies revealed marked differences in the uptake/distribution of activity in mice administered unchelated [117Sb]Sb(OH)6- versus [117Sb]Sb-TREN-CAM, suggesting that 117Sb is largely retained by TREN-CAMover the time course of the study. Collectively, these findings demonstrate the most physiologically stable complex of no-carrier-added 1XXSb yet reported, offering new promise for the clinical implementation of radioantimony in nuclear medicine. Our results also establish the feasibility of 117Sb as an elementally matched partner to 119Sb for theranostic applications.

Cognitive variation reflects amyloid, tau, and neurodegenerative biomarkers in Alzheimer's disease.

In Alzheimer's disease (AD), the accumulation of neuropathological markers such as amyloid-β plaques, neurofibrillary tangles, and cortical neurodegeneration occurs over many years before overt manifestation of cognitive impairment. There is thus a need for neuropsychological markers that are indicative of pathological changes in the early stages of the disease. Intra-individual cognitive variability (IICV), defined as the variation of an individual's performance across cognitive domains, is a promising neuropsychological marker measuring heterogeneous changes in cognition that may reflect these early pathological changes. In this study, we comprehensively evaluated the global and regional associations of IICV with positron emission tomography (PET) and magnetic resonance imaging (MRI) measures of AD biomarkers in cognitively normal (CN) and mild cognitive impairment (MCI) participants. We found that higher IICV was robustly associated with increased Aβ, increased tau, decreased brain glucose metabolism, and reduced cortical thickness. Higher IICV was also associated with tau (OR = 2.53, P < .001) and fluorodeoxyglucose (OR = 1.34, P < .001) positivity but not Aβ positivity (OR = 1.15, P = .107). In regional analyses, IICV showed widespread associations with AD biomarkers, with the strongest Aβ and tau effects in the frontal and temporal regions, respectively. The strongest regional cortical thickness effects were found in the entorhinal and parahippocampal cortices. Our findings suggest that IICV may be a useful neuropsychological marker for increased Aβ, and especially increased tau and neurodegeneration that are reflective of emerging AD pathology in individuals without dementia.

Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks

Objective. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs).Approach. Instead of reading out all 64 signals from an 8 × 8 silicon photomultiplier (SiPM) array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants. To recover the original signal amplitudes, an ANN is used to demultiplex the multiplexed signals. Specifically, the ANN processes the sampled waveforms of these four multiplexed signals and estimates the energy information of the original 8 × 8 SiPM channels. In this study, two DOI estimation strategies were explored for a continuous DOI (cDOI) PET detector utilizing triangular teeth-shaped reflectors: a 'single-step estimation' method directly estimating DOI from multiplexed signals, and a 'two-stage cascade estimation' method that first demultiplexes the signals and then estimates DOI. The performances of proposed strategies were validated using data irradiated at five steps (2, 6, 10, 14, and 18 mm).Results. The signal amplitude of row/column summed signals, which were recovered using the proposed ANN-based de-multiplexing, showed strong correlation with ground truth (e.g.R2= 0.98 for 125 MHz digitizer sampling rate). Moreover, both the single-step and two-stage estimation methods achieved high accuracy in DOI estimation, with an average DOI resolution of 4.86 and 5.11 mm respectively.Significance. This novel signal multiplexing technique significantly reduces the number of required readout channels, making cDOI PET more cost-effective.

Quantitative and Visual Benefits of Data-Driven Motion Correction on Oncologic PET/CT: AProspective Cross-sectional Study.

Conventional positron emission tomography (PET) respiratory gating utilizes a fraction of acquired PET counts (i.e., optimal gate [OG]), whereas elastic motion correction with deblurring (EMCD) utilizes all PET counts to reconstruct motion-corrected images without increasing image noise. Our aim was to assess the quantitative and visual impacts of EMCD-based motion correction on FDG-PET and DOTATATE-PET images relative to OG and ungated (UG) images. This prospective, single-center study enrolled adults undergoing FDG or DOTATATE oncologic PET/CT between June 2020 and October 2022. Subjects underwent a standard-of-care (SOC) PET acquisition while wearing a respiratory gating belt. UG, belt-gating-derived optimal gate (BG-OG), EMCD utilizing belt gating (BG-EMCD), and EMCD utilizing data-driven gating (DDG-EMCD) images were reconstructed. Tracer-avid lesions in the lower chest or upper abdomen were segmented. Quantitative metrics were extracted. Two independent, blinded readers assessed image quality via a 4-point scale and counted lesions on each reconstruction. Differences between reconstructions were assessed via the Wilcoxon signed-rank test (alpha, 0.05). This study enrolled 78 subjects; 36 subjects (mean age, 64.8 years; 20 males) with 136 tracer-avid lesions were analyzed. Data provided are medians across all tracers. Lesion SUV-max was significantly higher (P<0.001) on motion-corrected images (BG-OG: 10.77, BG-EMCD: 10.75, DDG-EMCD: 10.74) than UG images (9.00). Lesion contrast-to-noise ratios (CNRs) were significantly lower (P<0.001) for BG-OG (6.31) and UG images (7.89) than BG-EMCD (9.14) and DDG-EMCD (8.89) images. FDG and DOTATATE subgroup analyses produced similar results. Among motion-corrected images, both readers preferred EMCD-based images to BG-OG images for all tracers (P<0.001) and both subgroups. EMCD-based images occasionally demonstrated more tracer-avid lesions than BG-OG or UG images. EMCD-based motion correction beneficially impacts lesion quantitation (including higher SUVs and CNRs) and overall image quality. When employed on capable scanners, EMCD can improve the quality of oncologic PET imaging.

Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.

Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction. Nevertheless, a successful clinical application requires a thorough evaluation of uncertainty to ensure informed clinical judgment. We propose NPB-LDPET, a DL-based non-parametric Bayesian framework for LD PET reconstruction and uncertainty assessment. Our framework utilizes an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to sample from the underlying posterior distribution. We employed the Ultra-low-dose PET Challenge dataset to assess our framework's performance relative to the Monte Carlo dropout benchmark. We evaluated global reconstruction accuracy utilizing SSIM, PSNR, and NRMSE, local lesion conspicuity using mean absolute error (MAE) and local contrast, and the clinical relevance of uncertainty maps employing correlation between the uncertainty measures and the dose reduction factor (DRF). Our NPB-LDPET reconstruction method exhibits a significantly superior global reconstruction accuracy for various DRFs (paired t-test, , N=10,631). Moreover, we demonstrate a 21% reduction in MAE (573.54 vs. 723.70, paired t-test, , N=28) and an 8.3% improvement in local lesion contrast (2.077 vs. 1.916, paired t-test, , N=28). Furthermore, our framework exhibits a stronger correlation between the predicted uncertainty 95th percentile score and the DRF ( vs. , N=10,631). The proposed framework has the potential to improve clinical decision-making for LD PET imaging by providing a more accurate and informative reconstruction while reducing radiation exposure.

Diagnostic Value of Gastrin-Releasing Peptide Receptor-Targeted PET Imaging in Oncology: A Systematic Review.

Gastrin-releasing peptide receptor (GRPR), overexpressed in various cancers, is a promising target for positron emission tomography (PET). This systematic review investigated the diagnostic value of GRPR-targeted PET imaging in oncology. A systematic search was conducted on major medical databases until May 23, 2024. Keywords were modified to include clinical original studies on GRPR-targeted PET in cancer patients. Out of 1624 searched studies initially, 107 were eligible for the full-text review. Overall, data from 38 studies met inclusion criteria, investigating GRPR-targeting radiotracers in breast cancer, prostate cancer, gastrointestinal stromal tumours (GIST) and gliomas (including optic pathway glioma and glioblastoma multiforme). In breast cancer, GRPR-targeted PET effectively detected primary tumours and metastases, particularly in estrogen receptor (ER)-positive patients, and predicted treatment response. In prostate cancer, high sensitivity (up to 88%) and specificity (up to 90%) for detecting primary tumours were observed, providing added value when combined with magnetic resonance imaging (MRI). In biochemical recurrence, sites of prostate cancer were identified even at PSA levels below 0.5ng/dL. Compared with PSMA PET, GRPR-targeted PET showed comparable or superior detection rates. Considering GIST, GRPR-targeted PET imaging proved to be a valuable diagnostic tool, particularly when [18F] FDG PET results were inconclusive. Regarding gliomas, GRPR-targeted PET achieved a 100% detection rate (MRI reference), aiding localization, preoperative planning, and differentiation between recurrence and malignant transformation. GRPR-targeted PET shows promise in improving cancer diagnostics, particularly in ER-positive breast cancer, prostate cancer, and gliomas, and may enhance clinical decision-making.

Baseline Imaging Derived Factors of Response Following [225Ac]Ac-J591 Therapy in Metastatic Castration-Resistant Prostate Cancer: A Lesion Level Analysis.

Actinium-225 labeled prostate-specific membrane antigen (PSMA) targeted radionuclide therapy has emerged as a potential treatment option in the management of men with metastatic castrate-resistant prostate cancer (mCRPC). This study investigated molecular imaging-derived parameters and compared imaging response of lesions categorized by tumor site. Men with mCRPC treated with [225Ac]Ac-J591 from 2017 to 2022 at our center on two prospective trials (NCT03276572 and NCT04506567) with pre- and post-treatment [68Ga]Ga-PSMA-11 Positron Emission Tomography (PET) imaging studies available were included. SUVpeak of the 3 most- and 3 least-avid lesions of the tumor sites were manually assessed. The median change of the SUVpeak from pre- to post-treatment per tumor site was evaluated using the paired Wilcox test. An objective response (OR) in the follow-up image was defined as complete or partial response using PET Response Criteria in Solid Tumors (PERCIST) 1.0. A total of 46 cases met the criteria for image review; most of them (n = 25, 54.3%) had more than one tumor site category. In total, 445 PSMA PET-positive lesions were assessed: 220 osseous, 163 nodal, 41 visceral, and 21 prostatic lesions. After treatment with [225Ac]Ac-J591, absolute SUVpeak values per tumor site declined significantly (p < 0.05) except for prostatic lesions (p = 1). The PERCIST-OR rate for osseous, nodal, visceral, and prostatic lesions was 53%, 28%, 56%, and 38%, respectively. [225Ac]Ac-J591 is an active treatment in men with mCRPC. Tumor distribution patterns may influence treatment response and potentially prognosis. Our findings warrant further validation in a larger cohort but may be considered in treatment planning and trial design.

P1116 Imaging of CCR9 in the small bowel of patients with Crohn’s disease: effects of the CCR9-depleting monoclonal antibody AZD7798

Abstract Background Management of patients with small bowel Crohn’s disease (SBCD) remains challenging due to limited efficacy from current therapeutic options. AZD7798 is a first-in-class monoclonal antibody for CD that depletes CCR9+ lymphocytes, with potential for targeted treatment of SBCD given the critical role that CCR9 plays in lymphocyte homing to the small bowel. Monitoring CCR9+ cell depletion and repletion in the blood is straightforward, but difficult in the small bowel due to accessibility for biopsy. Here we use positron emission tomography (PET) with a novel CCR9-specific PET tracer, [11C]vercirnon, to assess the ability of AZD7798 to deplete CCR9+ cells in the intestines of healthy volunteers and patients with SBCD. Methods We performed an open-label phase 1b study to evaluate the effects of single and repeat intravenous doses of AZD7798 on intestinal [11C]vercirnon uptake in healthy volunteers and patients with SBCD (NCT06053424). Additional endpoints were safety and pharmacodynamics in blood (CCR9 cell depletion). PET imaging using [11C]vercirnon was performed prior to AZD7798 dosing and at 2 follow-up timepoints, approximately 2 and 6 weeks post dosing. Results A total of 6 healthy (aged 25-64 years, 4 female) and 6 SBCD participants (aged 24-59 years, 4 female) completed the study. There were no major adverse tolerability or safety findings following single or repeat doses of AZD7798. Mild cytokine release syndrome (CTCAE Grade 1) was observed after first doses, but not after second doses in the repeat dose cohort. There were no serious adverse events or treatment discontinuations due to adverse events. In 4 out of 6 SBCD participants, PET imaging showed a quantitative reduction in [11C]vercirnon uptake of up to 21.6% (median 11.5%) in the intestines at 6 weeks following single (n = 3 out of 4) and repeat (n = 1 out of 2) IV doses of AZD7798 (Figure 1). Rapid, profound and sustained CCR9+ cell depletion was observed in blood in all healthy and SBCD participants (Figure 2). Conclusion The use of a CCR9-specific PET tracer has enabled repeatable, non-invasive quantification of CCR9 expression in the gut. Preliminary evidence of intestinal CCR9+ cell depletion in patients with SBCD following single and repeat doses of AZD7798 was provided. The study demonstrates the value of molecular imaging for understanding treatment effect in the target tissue in vivo. An ongoing PET imaging study will assess the effects of AZD7798 on CCR9+ cell depletion/repletion in the gut following multiple doses, alongside Phase 2 studies evaluating the clinical efficacy of AZD7798 in moderate-to-severe Crohn’s disease.

18F]BCPP-EF positron emission tomography of rat ovaries for evaluation of mitochondrial function.

The ovary is an important female organ not only for pregnancy but also for the regulation of life activities via hormone release. Ovarian function is measured by blood hormone levels, but the hormone level reflects only the ovarian reserve and no other essential ovarian functions, such as nurturing and expelling follicles. Ovarian fibrosis is related to essential ovarian function; however, the existing methods for evaluating fibrosis are invasive. Ovarian fibrosis has been reported to be associated with mitochondrial function. We hypothesized that positron emission tomography (PET) imaging of mitochondria could be a new, non-invasive method for evaluating essential ovarian function. In this study, we investigated the age-related changes in ovarian fibrosis using the mitochondrial complex-I (MC-I) PET probe, 2-tert-butyl-4-chloro-5-{6-[2-(2-18F-fluoroethoxy)-ethoxy]-pyridin-3-ylmethoxy}-2H-pyridazin-3-one ([18F]BCPP-EF). Aged rats, whose ovary function decline, exhibited a higher uptake of [18F]BCPP-EF in the ovary than young rats, and this high uptake in aged rats was suppressed by mitoquinone, a superoxide scavenger. Increased [18F]BCPP-EF uptake in the ovary was associated with ovarian fibrosis, but not with AMH level which reflects the ovarian reserve. Furthermore, the measurement of MC protein levels showed that the protein levels of MC-I increased with age, whereas those of MC-V decreased. This study demonstrated that [18F]BCPP-EF can detect age-related changes in essential ovarian function evaluated by ovarian fibrosis. Therefore, [18F]BCPP-EF-PET is a useful non-invasive method for evaluating essential ovarian functions and will contribute to basic research on ovarian aging as well as drug discovery targeting ovarian dysfunction.

Deficiency of orexin receptor type 1 in dopaminergic neurons increases novelty-induced locomotion and exploration

Orexin signaling in the ventral tegmental area and substantia nigra promotes locomotion and reward processing, but it is not clear whether dopaminergic neurons directly mediate these effects. We show that dopaminergic neurons in these areas mainly express orexin receptor subtype 1 (Ox1R). In contrast, only a minor population in the medial ventral tegmental area express orexin receptor subtype 2 (Ox2R). To analyze the functional role of Ox1R signaling in dopaminergic neurons, we deleted Ox1R specifically in dopamine transporter-expressing neurons of mice and investigated the functional consequences. Deletion of Ox1R increased locomotor activity and exploration during exposure to novel environments or when intracerebroventricularely injected with orexin A. Spontaneous activity in home cages, anxiety, reward processing, and energy metabolism did not change. Positron emission tomography imaging revealed that Ox1R signaling in dopaminergic neurons affected distinct neural circuits depending on the stimulation mode. In line with an increase of neural activity in the lateral paragigantocellular nucleus (LPGi) of Ox1RΔDAT mice, we found that dopaminergic projections innervate the LPGi in regions where the inhibitory dopamine receptor subtype D2 but not the excitatory D1 subtype resides. These data suggest a crucial regulatory role of Ox1R signaling in dopaminergic neurons in novelty-induced locomotion and exploration.

Deficiency of orexin receptor type 1 in dopaminergic neurons increases novelty-induced locomotion and exploration

Orexin signaling in the ventral tegmental area and substantia nigra promotes locomotion and reward processing, but it is not clear whether dopaminergic neurons directly mediate these effects. We show that dopaminergic neurons in these areas mainly express orexin receptor subtype 1 (Ox1R). In contrast, only a minor population in the medial ventral tegmental area express orexin receptor subtype 2 (Ox2R). To analyze the functional role of Ox1R signaling in dopaminergic neurons, we deleted Ox1R specifically in dopamine transporter-expressing neurons of mice and investigated the functional consequences. Deletion of Ox1R increased locomotor activity and exploration during exposure to novel environments or when intracerebroventricularely injected with orexin A. Spontaneous activity in home cages, anxiety, reward processing, and energy metabolism did not change. Positron emission tomography imaging revealed that Ox1R signaling in dopaminergic neurons affected distinct neural circuits depending on the stimulation mode. In line with an increase of neural activity in the lateral paragigantocellular nucleus (LPGi) of Ox1RΔDAT mice, we found that dopaminergic projections innervate the LPGi in regions where the inhibitory dopamine receptor subtype D2 but not the excitatory D1 subtype resides. These data suggest a crucial regulatory role of Ox1R signaling in dopaminergic neurons in novelty-induced locomotion and exploration.

Quantitative accuracy of preclinical in ovo PET/MRI: influence of attenuation and quantification methods

AimThe combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) provides an innovation leap in the use of fertilized chicken eggs (in ovo model) in preclinical imaging as PET/MRI enables the investigation of the chick embryonal organ-specific distribution of PET-tracers. However, hybrid PET/MRI inheres technical challenges in quantitative in ovo PET such as attenuation correction (AC) for the object as well as for additional hardware parts present in the PET field-of-view, which potentially contribute to quantification biases in the PET images if not accounted for. This study aimed to investigate the influence of the different sources of attenuation on in ovo PET/MRI and assess the accuracy of MR-based AC for in ovo experiments.MethodAn in-house made chicken egg phantom was used to investigate the magnitude of self-attenuation and the influence of the MRI hardware on the PET signal. The phantom was placed in a preclinical PET/MRI system and PET acquisitions were performed without, and after subsequently adding the different hardware parts to the setup. Reconstructions were performed without any AC for the different setups and with subsequently incorporating the hardware parts into the AC. In addition, in ovo imaging was performed using [18F]FDG and [68Ga]Ga-Pentixafor, and PET data was reconstructed with the different AC combinations. Quantitative accuracy was assessed for the phantom and the in ovo measurements.ResultsIn general, not accounting for the self-attenuation of the egg and the hardware parts caused an underestimation of the PET signal of around 49% within the egg. Accounting for all sources of attenuation allowed a proper quantification with global offsets of 2% from the true activity. Quantification based on % injected dose per cc (%ID/cc) was similar for the in ovo measurements, regardless of whether hardware parts were included in AC or not, when the injected activity was extracted from the PET images. However, substantial quantification biases were found when the self-attenuation of the egg was not taken into account.ConclusionSelf-attenuation of the egg and PET signal attenuation within the hardware parts of the MRI substantially influence quantitative accuracy in in ovo measurements. However, when compensating for the self-attenuation of the egg by a respective AC, a reliable quantification using %ID/cc can be performed even if not accounting for the attenuation of the hardware parts.

PET Imaging of a Transgenic Tau Rat Model SHR24 with [18F]AV1451.

Positron Emission Tomography (PET) scans with radioligands targeting tau neurofibrillary tangles (NFT) have accelerated our understanding of the role of misfolded tau in neurodegeneration. While intended for human research, applying these radioligands to small animals establishes a vital translational link. Transgenic animal models of dementia, such as the tau rat SHR24, play a crucial role in enhancing our understanding of these disorders. This study aims to evaluate the utility of SHR24 rat model for PET studies. Dynamic PET scans were conducted in male SHR24 rats and their wild-type SHR (SHRwt) littermates using [18F]AV1451. Rapid blood sampling and metabolite analysis were performed to acquire input curves. Time activity curves were obtained from various brain regions over 60min. Blood-based, 2-Tissue Compartment Model (2-TCM) and Logan graphical analysis were used to obtain kinetic modelling parameters. The ability of reference tissue models to predict the binding potential (BPND) were assessed. Autoradiography studies were performed to corroborate the scan data. Total distribution volume (VT) was the best predicted parameter which revealed significantly higher uptake of [18F]AV1451 in the cortex (5.8 ± 1.1 vs 4.6 ± 0.7, P < 0.05) of SHR24 rats compared to SHRwt rats. Binding potential obtained from 2-TCM was variable, however BPND from reference tissue models detected significantly higher binding in cortex (0.28 ± 0.07 vs 0.20 ± 0.04, P < 0.01 by SRTM) and brainstem (0.14 ± 0.04 vs 0.08 ± 0.02, P < 0.01, by SRTM). With the ability to detect binding of established radioligand [18F]AV1451 in these rats, we have demonstrated the utility of this model for assessing aggregated tau neurobiology by PET, with reference tissue models providing potential for longitudinal studies.

Diffusion transformer model with compact prior for low-dose PET reconstruction.

Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes. In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion models with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and alternating direction method of multipliers (ADMM) enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks. Experimental results demonstrate the effectiveness of DTM in enhancing image quality and preserving critical clinical information for low-dose PET scans. Our approach not only reduces radiation exposure risks but also provides a more reliable PET imaging tool for early disease detection and patient management.

Compartmental modeling for blood flow quantification from dynamic O-water PET images of humans: a systematic review.

Dynamic positron emission tomography (PET) can be used to non-invasively estimate the blood flow of different organs via compartmental modeling. Out of different PET tracers, water labeled with the radioactive O isotope of oxygen (half-life of 2.04min) is freely diffusable, and therefore, very well-suited for blood flow quantification. While the earlier O-water PET research has primarily focused on cerebral or myocardial blood flow quantification, the recent emergence of total-body PET scanners has enabled greater application possibilities for both PET imaging in general and also O-water PET based blood flow quantification in particular. However, to validate new methods, it is necessary to compare them to earlier research. To help in this process, we systematically review 53 articles quantifying blood flow via compartmental modeling. We introduce the articles organized within subcategories of cerebral, myocardial, renal, pulmonary, pancreatic, hepatic, muscle, and tumor blood flow and summarize their results so that they can easily be evaluated in terms of population characteristics of the patients such as age or sex ratio and their potential diagnoses. We compare how both the compartment model used and the potential corrections for arterial blood volume, non-perfusable tissue, spill-over from the heart cavities, and time delay caused while the tracer travels between different areas of interest are generally implemented in the articles. We also analyze the differences in the data pre-processing techniques. According to our results, the estimates of cerebral and tumor blood flow vary considerably more between the articles than those of myocardial blood flow. This might be caused by differences in the model approaches or the study populations. We also note that the choice of the unit for these estimates is quite inconsistent as certain researchers seem to prefer mL/min/g over mL/min/mL even if no weight or density parameter is present in the modeling. We encourage more research on sex- and age-based differences in blood flow estimates and organ-specific blood flow quantification studies for kidneys, lungs, liver, and other important organs besides brain and heart.

Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.

In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images. The aim of our proof-of-concept study is to investigate whether machine learning-based tumor diagnosis is also possible using CT images. We investigate the differentiability of histologically confirmed low-grade and high-grade gliomas. Three conventional machine learning algorithms and a neural net are tested. In addition, we analyze which of the common imaging methods (MRI or CT) appears to be best suited for the diagnostic question under investigation when machine learning algorithms are used. For this purpose, we compare our results based on CT images with numerous studies based on MRI scans. Our best-performing model includes six features and is obtained using univariate analysis for feature preselection and a Naive Bayes approach for model construction. Using independent test data, this model yields a mean AUC of 0.903, a mean accuracy of 0.839, a mean sensitivity of 0.807 and a mean specificity of 0.864. Our results demonstrate that low-grade and high-grade gliomas can be differentiated with high accuracy using machine learning algorithms, not only based on the usual MRI scans, but also based on CT images. In the future, such CT-image-based models can help to further accelerate brain tumor diagnostics and to reduce the number of necessary biopsies.

Modeling and Optimization of Distillation Column for Separation of 18O Isotope with Mathematical Model

Positron emission tomography (PET) is an advanced imaging tool for the diagnosis and staging of cancer tumors. This method is based on the detection of increased glycolytic activity in malignant cells, in which cell glucose is concentrated because of an increase in membrane glucose transporters, as well as an increase in some key enzymes, such as hexokinase, which are responsible for glucose phosphorylation. Therefore, for this type of imaging, drugs containing glucose are needed. On the other hand, with the expansion of the use of PET imaging devices, the need for drugs for this type of imaging method [fluorodeoxyglucose drug (FDG)] has also increased significantly. FDG is a drug tracer used in the medical imaging technique of PET. The production of FDG requires the production of 18F and, as a result, reaching 18O with a richness of more than 95%. There are various methods to produce oxygen with high richness. Among these methods, using a distillation column is a suitable method to produce oxygen, which has low efficiency and high production cost. Optimization of the distillation column can reduce the cost of producing high-rich oxygen. Numerical methods are one of the useful techniques for optimization. In this study, the distillation column has been computerized using mathematical models, and then by changing the number of inputs, including the height of the pipes, the temperature of the input of the distillation column has been optimized. Results show that the maximum separation of the desired isotope concentration in the distillation tower depends on the type of isotope desired and the condition of the device and is independent of the type of feed. Also, the input feed has no effect on the concentration distribution.

Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.

Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study investigated whether an explainable radiomics model modifies nuclear physicians' assessment of glioma aggressiveness at diagnosis. Patients underwent dynamic 6-[18F]fluoro-L-DOPA PET acquisition. With a 75%/25% split for training (n = 63) and test sets (n = 22), an ensemble ML model was trained using radiomics features extracted from static/dynamic parametric PET images to classify lesion aggressiveness. Three explainable ML methods-Local Interpretable Model-agnostic Explanations (LIME), Anchor, and SHapley Additive exPlanations (SHAP)-generated patient-specific explanations. Eighteen physicians from eight institutions evaluated the test samples. During the first phase, physicians analyzed the 22 cases exclusively through magnetic resonance and static/dynamic PET images, acquired within a maximum interval of 30 days. In the second phase, the same physicians reevaluated the same cases (n = 22), using all available data, including the radiomics model predictions and explanations. Eighty-five patients (54[39-62] years old, 41 women) were selected. In the second phase, physicians demonstrated a significant improvement in diagnostic accuracy compared to the first phase (0.775 [0.750-0.802] vs. 0.717 [0.694-0.737], p = 0.007). The explainable radiomics model augmented physician agreement, with a 22.72% increase in Fleiss's kappa, and significantly enhanced physician confidence (p < 0.001). Among all physicians, Anchor and SHAP showed efficacy in 75% and 72% of cases, respectively, outperforming LIME (p ≤ 0.001). Our results highlight the potential of an explainable radiomics model using amino acid PET scans as a diagnostic support to assist physicians in identifying glioma aggressiveness.

Synthetic [18F]FET-PET Generation and Hotspot Prediction Via Preoperative MRI Fusion of Glioma Lacking Radiographic High-Grade Characteristics

Abstract Background Limited amino acid availability for Positron Emission Tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic [18F]FET-PET and predicting high [18F]FET-uptake from magnetic resonance imaging (MRI). Methods We trained a deep learning (DL)-based model to segment tumors in MRI and extracted radiomic features using the pyradiomics package and classification with a Random Forest classifier. To generate [18F]FET-PET images, we employed a Generative Adversarial Network (GAN) framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. Results We included MR and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [18F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak Signal-to-Noise Ratio analysis indicated high signal fidelity with a peak at 40 dB, while Structural Similarity Index values showed structural congruence. Root Mean Square Error analysis demonstrated lower values below 5.6. Most Visual Information Fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. Conclusion For the first time, we demonstrate that predicting high [18F]FET-uptake and generating synthetic PET images from preoperative MRI in LGG and HGG is feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.

18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review

Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. Methods: A wide literature search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases was made to find relevant published articles on the role of [18F]FDG PET-based radiomics and ML for the assessment of gliomas and GBMs. Results: Eight studies were included in the systematic review. Signatures, including radiomics analysis and ML, generally demonstrated a possible diagnostic value to assess different characteristics of gliomas and GBMs, such as the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter, the isocitrate dehydrogenase (IDH) genotype, alpha thalassemia/mental retardation X-linked (ATRX) mutation status, proliferative activity, differential diagnosis with solitary brain metastases or primary central nervous system lymphoma, and prognosis of these patients. Conclusion: Despite some intrinsic limitations of radiomics and ML affecting the studies included in the review, some initial insights on the promising role of these technologies for the assessment of gliomas and GBMs are emerging. Validation of these preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.