Normal values for 18F-FDG uptake in organs and tissues measured by dynamic whole body multiparametric FDG PET in 126 patients

Ultrashort Oncologic Whole-Body [18F]FDG Patlak Imaging Using LAFOV PET

Methods to shorten [18F]FDG Patlak PET imaging procedures ranging from 65-90 to 20-30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [18F]FDG Patlak imaging at 55-65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30-60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30-60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (Ki ) (mean difference ranging from -0.0022 to 0.0018 mL/[min × g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30-60 min after injection versus 55-65 min after injection, with 2.4% deviation in tumor Ki (median difference, -0.0018 mL/[min × g]; range, -0.0047 to 0.0036 mL/[min × g]). For different maximum crystal ring difference settings using the scan time interval of 55-65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min × g]; range, -0.0004 to 0.0013 mL/[min × g]) in tumor Ki was found. Conclusion: Ultrashort whole-body [18F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

Image Quality of Cardiac Silicon Photomultiplier PET/CT Using an Infant Phantom of Extremely Low Birth Weight

The lack of pediatrics-specific equipment for nuclear medicine imaging has resulted in insufficient diagnostic information for newborns, especially low-birth-weight infants. Although PET offers high spatial resolution and low radiation exposure, its use in newborns is limited. This study investigated the feasibility of cardiac PET imaging using the latest silicon photomultiplier (SiPM) PET technology in infants of extremely low birth weight (ELBW) using a phantom model. Methods: The study used a phantom model representing a 500-g ELBW infant with brain, cardiac, liver, and lung tissues. The cardiac tissue included a 3-mm-thick defect mimicking myocardial infarction. Organ tracer concentrations were calculated assuming 18F-FDG myocardial viability scans and 18F-flurpiridaz myocardial perfusion scans and were added to the phantom organs. Imaging was performed using an SiPM PET/CT scanner with a 5-min acquisition. The data acquired in list mode were reconstructed using 3-dimensional ordered-subsets expectation maximization with varying iterations. Image evaluation was based on the depiction of the myocardial defect compared with normal myocardial accumulation. Results: Increasing the number of iterations improved the contrast of the myocardial defect for both tracers, with 18F-flurpiridaz showing higher contrast than 18F-FDG. However, even at 50 iterations, both tracers overestimated the defect accumulation. A bull's-eye image can display the flow metabolism mismatch using images from both tracers. Conclusion: SiPM PET enabled cardiac PET imaging in a 500-g ELBW phantom with a 1-g heart. However, there were limitations in adequately depicting these defects. Considering the image quality and defect contrast,18F-flurpiridaz appears more desirable than 18F-FDG if only one of the two can be used.

Advances in PET Imaging of Large Vessel Vasculitis: An Update and Future Trends

Systemic vasculitides are autoimmune diseases characterized by inflammation of blood vessels. They are categorized based on the size of the preferentially affected blood vessels: large-, medium-, and small-vessel vasculitides. The main forms of large-vessel vasculitis include giant cell arteritis (GCA) and Takayasu arteritis (TAK). Depending on the location of the affected vessels, various imaging modalities can be employed for diagnosis of large vessel vasculitis: ultrasonography (US), magnetic resonance angiography (MRA), computed tomography angiography (CTA), and [18F]-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT). These imaging tools offer complementary information about vascular changes occurring in vasculitis. Recent advances in PET imaging in large vessel vasculitis include the introduction of digital long axial field-of-view PET/CT, dedicated acquisition, quantitative methodologies, and the availability of novel radiopharmaceuticals. This review aims to provide an update on the current status of PET imaging in large vessel vasculitis and to share the latest developments on imaging vasculitides.

Glucose metabolic rate from four-dimensional [18F]FDG PET/CT to differentiate sarcoid lesions from malignant lesions

OBJECTIVES

On 18F-Fludeoxyglucose (FDG) PET/CT, active sarcoid lesions are often difficult to differentiate from malignant lesions. We investigated the potential of the glucose metabolic rate (MRglc, mg/min/100 mL), a new quantification of glucose metabolic kinetics derived from direct reconstruction based on linear Patlak analysis, to distinguish between sarcoidosis and malignant lesions.

MATERIALS AND METHODS

A total of 100 patients with cardiac sarcoidosis (CS) and 67 patients with cancer who underwent four-dimensional FDG PET/CT were enrolled. The lesions with a standardized uptake value (SUV) ≥ 2.7 on the standard scan were included as active lesions in the analysis. SUV and MRglc were derived using data acquired between 30 min and 50 min on four-dimensional FDG PET/CT. The mean value in the volume of interest (size 1.5 cm3) was measured. The diagnostic performance of sarcoidosis using MRglc and SUV was evaluated using receiver-operating-characteristic (ROC) analysis.

RESULTS

A total of 90 sarcoidosis lesions from 44 CS patients (18 males, 63.4 ± 12.2 years) and 87 malignant lesions from 57 cancer-bearing patients (32 males, 65 ± 14 years) were analyzed. SUV and MRglc for sarcoid lesions were significantly lower than those for malignant lesions (SUV, 4.98 ± 2.00 vs 6.21 ± 2.14; MRglc, 2.52 ± 1.39 vs 3.68 ± 1.61; p < 0.01). ROC analysis indicated that the ability to discriminate sarcoid patients from those with malignancy yielded areas under the curves of 0.703 and 0.754, with sensitivities of 64% and 77% and specificities of 75% and 72% for SUV 5.025 and MRglc 2.855, respectively.

CONCLUSION

MRglc was significantly lower in sarcoid lesions than malignant lesions, and improved sarcoid lesions identification over SUV alone.

CLINICAL RELEVANCE STATEMENT

MRglc improves sarcoid lymph node identification over SUV alone and is expected to shorten the examination time by eliminating delayed scans.

KEY POINTS

Active sarcoid lesions are sometimes associated with FDG accumulation and should be differentiated from malignant lesions. SUV and metabolic rate of glucose (MRglc) strongly positively correlated, and MRglc could differentiate sarcoid and malignant lesions. MRglc allows for accurate evaluation and staging of malignant lesions.

Mapping 18F-FDG Kinetics Together with Patient-Specific Bootstrap Assessment of Uncertainties: An Illustration with Data from a PET/CT Scanner with a Long Axial Field of View

The purpose of this study was to examine a nonparametric approach to mapping kinetic parameters and their uncertainties with data from the emerging generation of dynamic whole-body PET/CT scanners. Methods: Dynamic PET 18F-FDG data from a set of 24 cancer patients studied on a long-axial-field-of-view PET/CT scanner were considered. Kinetics were mapped using a nonparametric residue mapping (NPRM) technique. Uncertainties were evaluated using an image-based bootstrapping methodology. Kinetics and bootstrap-derived uncertainties are reported for voxels, maximum-intensity projections, and volumes of interest (VOIs) corresponding to several key organs and lesions. Comparisons between NPRM and standard 2-compartment (2C) modeling of VOI kinetics are carefully examined. Results: NPRM-generated kinetic maps were of good quality and well aligned with vascular and metabolic 18F-FDG patterns, reasonable for the range of VOIs considered. On a single 3.2-GHz processor, the specification of the bootstrapping model took 140 min; individual bootstrap replicates required 80 min each. VOI time-course data were much more accurately represented, particularly in the early time course, by NPRM than by 2C modeling constructs, and improvements in fit were statistically highly significant. Although 18F-FDG flux values evaluated by NPRM and 2C modeling were generally similar, significant deviations between vascular blood and distribution volume estimates were found. The bootstrap enables the assessment of quite complex summaries of mapped kinetics. This is illustrated with maximum-intensity maps of kinetics and their uncertainties. Conclusion: NPRM kinetics combined with image-domain bootstrapping is practical with large whole-body dynamic 18F-FDG datasets. The information provided by bootstrapping could support more sophisticated uses of PET biomarkers used in clinical decision-making for the individual patient.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Increased lesion detectability in patients with locally advanced breast cancer-A pilot study using dynamic whole-body [18F]FDG PET/CT

BACKGROUND

Accurate diagnosis of axillary lymph node (ALN) metastases is essential for prognosis and treatment planning in breast cancer. Evaluation of ALN is done by ultrasound, which is limited by inter-operator variability, and by sentinel lymph node biopsy and/or ALN dissection, none of which are without risks and/or long-term complications. It is known that conventional 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) has limited sensitivity for ALN metastases. However, a recently developed dynamic whole-body (D-WB) [18F]FDG PET/CT scanning protocol, allowing for imaging of tissue [18F]FDG metabolic rate (MRFDG), has been shown to have the potential to increase lesion detectability. The study purpose was to examine detectability of malignant lesions in D-WB [18F]FDG PET/CT compared to conventional [18F]FDG PET/CT.

RESULTS

This study prospectively included ten women with locally advanced breast cancer who were referred for an [18F]FDG PET/CT as part of their diagnostic work-up. They all underwent D-WB [18F]FDG PET/CT, consisting of a 6 min single bed dynamic scan over the chest region started at the time of tracer injection, a 64 min dynamic WB PET scan consisting of 16 continuous bed motion passes, and finally a contrast-enhanced CT scan, with generation of MRFDG parametric images. Lesion visibility was assessed by tumor-to-background and contrast-to-noise ratios using volumes of interest isocontouring tumors with a set limit of 50% of SUVmax and background volumes placed in the vicinity of tumors. Lesion visibility was best in the MRFDG images, with target-to-background values 2.28 (95% CI: 2.04-2.54) times higher than target-to-background values in SUV images, and contrast-to-noise values 1.23 (95% CI: 1.12-1.35) times higher than contrast-to-noise values in SUV images. Furthermore, five imaging experts visually assessed the images and three additional suspicious lesions were found in the MRFDG images compared to SUV images; one suspicious ALN, one suspicious parasternal lymph node, and one suspicious lesion located in the pelvic bone.

CONCLUSIONS

D-WB [18F]FDG PET/CT with MRFDG images show potential for improved lesion detectability compared to conventional SUV images in locally advanced breast cancer. Further validation in larger cohorts is needed.

CLINICAL TRIAL REGISTRATION

The trial is registered in clinicaltrials.gov, NCT05110443, https://www.

CLINICALTRIALS

gov/study/NCT05110443?term=NCT05110443&rank=1 .

Characteristics of whole-body dynamic 18F-FDG PET/CT Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters

Background

Dynamic course of flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak muti-parametric imaging spatial distribution in the targeted tissues may reveal highly useful clinical information about the tissue's metabolic properties. The characteristics of the Patlak multi-parametric imaging in lung cancer and the influence of different delineation methods on quantitative parameters may provide reference for the clinical application of this new technology.

Methods

A total of 27 patients with pathologically diagnosed lung cancer underwent whole-body dynamic 18F-FDG PET/CT examination before treatment. Parametric images of metabolic rate of FDG (MRFDG) and Patlak intercept (or distribution volume; DV) were generated using Patlak reconstruction. The values of primary lung cancer lesions, target-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were investigated using contour delineation and boundary delineation. Statistical analysis was performed to analyze the relationship between multi-parametric images and clinicopathological features, and to compare the effects of contour delineation and boundary delineation on quantitative parameters.

Results

MRFDG images showed higher TBR and CNR than did standardized uptake value (SUV) images. There were significant differences in MRFDG-max, MRFDG-mean, and MRFDG-peak among groups with different tumor diameters and pathology types (P<0.05). Moreover, the metabolic parameters of MRFDG were higher in patients with tumor diameters ≥3 cm and squamous carcinoma. The differences of the maximum and peak values of MRFDG and DV were not statistically significant in the different outlining method subgroups (all P>0.05). However, the difference of the mean values of MRFDG and DV were statistically significant in the different outline method groupings (all P<0.05).

Conclusions

Dynamic 18F-FDG PET/CT Patlak multi-parametric imaging can obtain quantitative values for lung cancer with high TBR and CNR. Moreover, the multi-parameters are various from different pathology types to tumor size. Different delineation methods have a greater influence on the mean value of quantitative parameters.

The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions

OBJECTIVES

Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner.

MATERIALS & METHODS

In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson's correlation and Kruskal-Wallis tests were used to compare parameters.

RESULTS

The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P < 0.01), but this is not the case for the delayed images (P > 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index.

CONCLUSION

The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future.

CLINICAL RELEVANCE

Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

Single time point quantitation of cerebral glucose metabolism by FDG-PET without arterial sampling

BACKGROUND

Until recently, quantitation of the net influx of 2-[18F]fluorodeoxyglucose (FDG) to brain (Ki) and the cerebrometabolic rate for glucose (CMRglc) required serial arterial blood sampling in conjunction with dynamic positron emission tomography (PET) recordings. Recent technical innovations enable the identification of an image-derived input function (IDIF) from vascular structures, but are frequently still encumbered by the need for interrupted sequences or prolonged recordings that are seldom available outside of a research setting. In this study, we tested simplified methods for quantitation of FDG-Ki by linear graphic analysis relative to the descending aorta IDIF in oncology patients examined using a Biograph Vision 600 PET/CT with continuous bed motion (Aarhus) or using a recently installed Biograph Vision Quadra long-axial field-of-view (FOV) scanner (Bern).

RESULTS

Correlation analysis of the coefficients of a tri-exponential decomposition of the IDIFs measured during 67 min revealed strong relationships among the total area under the curve (AUC), the terminal normalized arterial integral (theta(52-67 min)), and the terminal image-derived arterial FDG concentration (Ca(52-67 min)). These relationships enabled estimation of the missing AUC from late recordings of the IDIF, from which we then calculated FDG-Ki in brain by two-point linear graphic analysis using a population mean ordinate intercept and the single late frame. Furthermore, certain aspects of the IDIF data from Aarhus showed a marked age-dependence, which was not hitherto reported for the case of FDG pharmacokinetics.

CONCLUSIONS

The observed interrelationships between pharmacokinetic parameters in the IDIF measured during the PET recording support quantitation of FDG-Ki in brain using a single averaged frame from the interval 52-67 min post-injection, with minimal error relative to calculation from the complete dynamic sequences.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

From FDG and beyond: the evolving potential of nuclear medicine

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

PURPOSE

[18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients.

MATERIALS AND METHODS

Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients.

RESULTS

BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5.

CONCLUSIONS

The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Multiparametric dynamic whole-body PSMA PET/CT using [68Ga]Ga-PSMA-11 and [18F]PSMA-1007

BACKGROUND

Routine prostate-specific membrane antigen (PSMA) positron emission tomography (PET) performed for primary staging or restaging of prostate cancer patients is usually done as a single static image acquisition 60 min after tracer administration. In this study, we employ dynamic whole-body (D-WB) PET imaging to compare the pharmacokinetics of [⁶⁸Ga]Ga-PSMA-11 and [¹⁸F]PSMA-1007 in various tissues and lesions, and to assess whether Patlak parametric images are quantitative and improve lesion detection and image readability.

METHODS

Twenty male patients with prostate cancer were examined using a D-WB PSMA PET protocol. Ten patients were scanned with [⁶⁸Ga]Ga-PSMA-11 and ten with [¹⁸F]PSMA-1007. Kinetic analyses were made using time-activity curves (TACs) extracted from organs (liver, spleen, bone, and muscle) and lesions. For each patient, three images were produced: SUV + Patlak parametric images (K_i and DV). All images were reviewed visually to compare lesion detection, image readability was quantified using target-to-background ratios (TBR), and Ki and DV values were compared.

RESULTS

The two PSMA tracers exhibited markedly different pharmacokinetics in organs: reversible for [⁶⁸Ga]Ga-PSMA-11 and irreversible for [¹⁸F]PSMA-1007. For both tracers, lesions kinetics were best described by an irreversible model. All parametric images were of good visual quality using both radiotracers. In general, Ki images were characterized by reduced vascular signal and increased lesion TBR compared with SUV images. No additional malignant lesions were identified on the parametric images.

CONCLUSION

D-WB PET/CT is feasible for both PSMA tracers allowing for direct reconstruction of parametric Ki images. The use of multiparametric PSMA images increased TBR but did not lead to the detection of more lesions. For quantitative whole-body Ki imaging, [¹⁸F]PSMA-1007 should be preferred over [⁶⁸Ga]Ga-PSMA-11 due to its irreversible kinetics in organs and lesions.

Validation of the DoseCalcs Monte Carlo code for estimating the 18F S-values for ICRP adult and 15-year-old male and female phantoms

Internal radiation exposure using radiopharmaceuticals, as in nuclear medicine procedures, necessitates the estimation of the S-value to determine and improve the estimates of absorbed doses in at-risk organs and tissues. The S value is defined as the absorbed dose in the target organ per unit of nuclear transformation in the source organ. It is calculated using the specific absorbed fraction, which is an important quantity that connects the deposited energy in the target and emitting source organs. In this study, we applied DoseCalcs, a new Geant4-based tool, to estimate the S values of [Formula: see text]F using nuclear data from ICRP Publication 107. Geometrical data from ICRP Publications 110 and 143 were used to select four models representing male and female phantoms for adults and 15 years old to study the variability in the S-values arising from variations in anatomy and initial energy validations, because we used the [Formula: see text] mean energy instead of the full beta spectrum. The [Formula: see text]F-released photons and [Formula: see text] from 26 source organs were tracked using the Geant4 Livermore package. Accordingly, the S-values were calculated for 141 target organs. The results for the adult male and female phantoms were compared with the OpenDose reference data. These results agreed well with OpenDose, the average ratio for self-absorption S-values was 1.015, and the average ratios for the cross-irradiation were 1.2 and 1.22 for the AM and AF, respectively. This indicates the accuracy of DoseCalcs for subsequent use in estimating [Formula: see text]F S-values using voxelized geometries.

Small nuclei identification with a hemispherical brain PET

BACKGROUND

To confirm the performance of the first hemispherical positron emission tomography (PET) for the brain (Vrain) that we developed to visualise the small nuclei in the deep brain area, we compared ¹⁸F-fluorodeoxyglucose (FDG) brain images with whole-body PET images.

METHODS

Ten healthy male volunteers (aged 22-45 years) underwent a representative clinical whole-body PET, followed by Vrain each for 10 min. These two scans were initiated 30 min and 45 min after FDG injection (4.1 ± 0.5 MBq/kg), respectively. First, we visually identified the small nuclei and then compared their standardised uptake values (SUVs) with the participants' age. Next, the SUVs of each brain region, which were determined by applying a volume-of-interest template for anatomically normalised PET images, were compared between the brain images with the Vrain and those with the whole-body PET images.

RESULTS

Small nuclei, such as the inferior colliculus, red nucleus, and substantia nigra, were more clearly visualised in Vrain than in whole-body PET. The anterior nucleus and dorsomedial nucleus in the thalamus and raphe nucleus in the brainstem were identified in Vrain but not in whole-body PET. The SUVs of the inferior colliculus and dentate gyrus in the cerebellum positively correlated with age (Spearman's correlation coefficient r = 0.811, p = 0.004; r = 0.738, p = 0.015, respectively). The SUVs of Vrain were slightly higher in the mesial temporal and medial parietal lobes than those in whole-body PET.

CONCLUSIONS

This was the first time that the raphe nuclei, anterior nuclei, and dorsomedial nuclei were successfully visualised using the first hemispherical brain PET. TRIAL REGISTRATION  : Japan Registry of Clinical Trials, jRCTs032210086, Registered 13 May 2021, https://jrct.niph.go.jp/latest-detail/jRCTs032210086 .

Clinical validation of a population-based input function for 20-min dynamic whole-body 18F-FDG multiparametric PET imaging

Purpose

Contemporary PET/CT scanners can use 70-min dynamic whole-body (D-WB) PET to generate more quantitative information about FDG uptake than just the SUV by generating parametric images of FDG metabolic rate (MR_FDG). The analysis requires the late (50–70 min) D-WB tissue data combined with the full (0–70 min) arterial input function (AIF). Our aim was to assess whether the use of a scaled population-based input function (sPBIF) obviates the need for the early D-WB PET acquisition and allows for a clinically feasible 20-min D-WB PET examination.

Methods

A PBIF was calculated based on AIFs from 20 patients that were D-WB PET scanned for 120 min with simultaneous arterial blood sampling. MR_FDG imaging using PBIF requires that the area under the curve (AUC) of the sPBIF is equal to the AUC of the individual patient’s input function because sPBIF AUC bias translates into MR_FDG bias. Special patient characteristics could affect the shape of their AIF. Thus, we validated the use of PBIF in 171 patients that were divided into 12 subgroups according to the following characteristics: diabetes, cardiac ejection fraction, blood pressure, weight, eGFR and age. For each patient, the PBIF was scaled to the aorta image-derived input function (IDIF) to calculate a sPBIF, and the AUC bias was calculated.

Results

We found excellent agreement between the AIF and IDIF at all times. For the clinical validation, the use of sPBIF led to an acceptable AUC bias of 1–5% in most subgroups except for patients with diabetes or patients with low eGFR, where the biases were marginally higher at 7%. Multiparametric MR_FDG images based on a short 20-min D-WB PET and sPBIF were visually indistinguishable from images produced by the full 70-min D-WB PET and individual IDIF.

Conclusions

A short 20-min D-WB PET examination using PBIF can be used for multiparametric imaging without compromising the image quality or precision of MR_FDG. The D-WB PET examination may therefore be used in clinical routine for a wide range of patients, potentially allowing for more precise quantification in e.g. treatment response imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00490-y.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging

Introduction

Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently.

Methods

In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject’s whole-body ¹⁸F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown.

Results

The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level.

Conclusion

The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.