90Y post-radioembolization clinical assessment with whole-body Biograph Vision Quadra PET/CT: image quality, tumor, liver and lung dosimetry.

PURPOSE: Evaluation of 90Y liver radioembolization post-treatment clinical data using a whole-body Biograph Vision Quadra PET/CT to investigate the potential of protocol optimization in terms of scan time and dosimetry. METHODS: 17 patients with hepatocellular carcinoma with median (IQR) injected activity 2393 (1348-3298) MBq were included. Pre-treatment dosimetry plan was based on 99mTc-MAA SPECT/CT with Simplicit90Y™ and post-treatment validation with Quadra using Simplicit90Y™ and HERMIA independently. Regarding the image analysis, mean and peak SNR, the coefficient of variation (COV) and lesion-to-background ratio (LBR) were evaluated. For the post-treatment dosimetry validation, the mean tumor, whole liver and lung absorbed dose evaluation was performed using Simplicit90Y and HERMES. Images were reconstructed with 20-, 15-, 10-, 5- and 1- min sinograms with 2, 4, 6 and 8 iterations. Wilcoxon signed rank test was used to show statistical significance (p < 0.05). RESULTS: There was no difference of statistical significance between 20- and 5- min reconstructed times for the peak SNR, COV and LBR. In addition, there was no difference of statistical significance between 20- and 1- min reconstructed times for all dosimetry metrics. Lung dosimetry showed consistently lower values than the expected. Tumor absorbed dose based on Simplicit90Y™ was similar to the expected while HERMES consistently underestimated significantly the measured tumor absorbed dose. Finally, there was no difference of statistical significance between expected and measured tumor, whole liver and lung dose for all reconstruction times. CONCLUSION: In this study we evaluated, in terms of image quality and dosimetry, whole-body PET clinical images of patients after having been treated with 90Y microspheres radioembolization for liver cancer. Compared to the 20-min standard scan, the simulated 5-min reconstructed images provided equal image peak SNR and noise behavior, while performing also similarly for post-treatment dosimetry of tumor, whole liver and lung absorbed doses.

Quantification performance of silicon photomultiplier-based PET for small 18F-, 68Ga- and 124I-avid lesions in the context of radionuclide therapy planning.

PURPOSE: The aim of this study was to investigate conditions for reliable quantification of sub-centimeter lesions with low18F,68Ga, and124I uptake using a silicon photomultiplier-based PET/CT system. METHODS: A small tumor phantom was investigated under challenging but clinically realistic conditions resembling prostate and thyroid cancer lymph node metastases (6 spheres with 3.7-9.7 mm in diameter, 9 different activity concentrations ranging from about 0.25-25 kBq/mL, and a signal-to-background ratio of 20). Radionuclides with different positron branching ratios and prompt gamma coincidence contributions were investigated. Maximum-, contour-, and oversize-based partial volume effect (PVE) correction approaches were applied. Detection and quantification performance were estimated, considering a ±30 % deviation between imaged-derived and true activity concentrations as acceptable. A standard and a prolonged acquisition time and two image reconstruction algorithms (time-of-flight with/without point spread function modelling) were analyzed. Clinical data were evaluated to assess agreement of PVE-correction approaches indicating lesion quantification validity. RESULTS: The smallest 3.7-mm sphere was not visible. If the lesions were clearly observed, quantification was, except for a few cases, acceptable using contour- or oversized-based PVE-corrections. Quantification accuracy did not substantially differ between 18F, 68Ga, and 124I. No systematic differences between the analyzed reconstruction algorithms or shorter and larger acquisition times were observed. In the clinical evaluation of 20 lesions, an excellent statistical agreement between oversize- and contour-based PVE-corrections was observed. CONCLUSIONS: At the lower end of size (<10 mm) and activity concentration ranges of lymph-node metastases, quantification with reasonable accuracy is possible for 18F, 68Ga, and 124I, possibly allowing pre-therapeutic lesion dosimetry and individualized radionuclide therapy planning.

Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

INTRODUCTION: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS: SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Reduction of emission time for [68Ga]Ga-PSMA PET/CT using the digital biograph vision: a phantom study.

BACKGROUND: The aim of this phantom study was to optimize the [68Ga]Ga-PSMA PET/CT examination in terms of scan time duration and image reconstruction parameters, in combination with PSF and TOF modelling, in a digital Biograph Vision PET/CT scanner. METHODS: Three types of phantoms were used: 1) soft-tissue tumor phantom consisting of six spheres mounted in a torso phantom; 2) bone-lung tumor phantom; 3) resolution phantom. Phantom inserts were filled with activity concentrations (ACs) that were derived from clinical data. Phantom data were acquired in list-mode at one bed position. Images with emission data ranging from 30 to 210 s in 30-s increments were reconstructed from a reference image acquired with 3.5-min emission. Iterative image reconstruction (OSEM), point-spread-function (PSF) and time-of-flight (TOF) options were applied using different iterations, Gaussian filters, and voxel sizes. The criteria for image quality was lesion detectability and lesion quantification, evaluated as contrast-to-noise ratio (CNR) and maximum AC (peak AC), respectively. A threshold value of CNR above 6 and percentage maximum AC (peak AC) deviation range of ±20% of the reference image were considered acceptable. The proposed single-bed scan time reduction was projected to a whole-body examination (patient validation scan) using the continuous-bed-motion mode. RESULTS: Sphere and background ACs of 20 kBq/mL and 1 kBq/mL were selected, respectively. The optimized single-bed scan time was approximately 60 s using OSEM-TOF or OSEM-TOF+PSF (four iterations, 4.0-mm Gaussian filter and almost isotropic voxel size of 3.0-mm side length), resulting in a PET spatial resolution of 6.3 mm for OSEM-TOF and 5.5 mm for OSEM-TOF+PSF. In the patient validation, the maximum percentage difference in lesion quantification between standard and optimized protocol (whole-body scan time of 15 vs. 5 min) was below 19%. CONCLUSIONS: A reduction of single-bed and whole-body scan time for [68Ga]Ga-PSMA PET/CT compared to current recommended clinical acquisition protocols is postulated. Clinical studies are warranted to validate the applicability of this protocol.

A Multicenter Study on Observed Discrepancies Between Vendor-Stated and PET-Measured 90Y Activities for Both Glass and Resin Microsphere Devices.

Dosimetry-guided treatment planning in selective internal radiation therapy relies on accurate and reproducible measurement of administered activity. This 4-center, 5-PET-device study compared the manufacturer-declared 90Y activity in vials with quantitative 90Y PET/CT assessment of the same vials. We compared 90Y PET-measured activity (APET) for 56 90Y-labeled glass and 18 90Y-labeled resin microsphere vials with the calibrated activity specified by the manufacturer (AM). Additionally, the same analysis was performed for 4 90Y-chloride vials. The mean APET/AM ratio was 0.79 ± 0.04 (range, 0.71-0.89) for glass microspheres and 1.15 ± 0.06 (range, 1.05-1.25) for resin microspheres. The mean APET/AM ratio for 90Y-chloride vials was 1.00 ± 0.04 (range, 0.96-1.06). Thus, we found an average difference of 46% between glass and resin microsphere activity calibrations, whereas close agreement was found for chloride solutions. We expect that the reported discrepancies will promote further investigations to establish reliable and accurate patient dosimetry and dose-effect assessments.

Phantom-based evaluation of yttrium-90 datasets using biograph vision quadra.

PURPOSE: The image quality characteristics of two NEMA phantoms with yttrium-90 (90Y) were evaluated on a long axial field-of-view (AFOV) PET/CT. The purpose was to identify the optimized reconstruction setup for the imaging of patients with hepatocellular carcinoma after 90Y radioembolization. METHODS: Two NEMA phantoms were used, where one had a 1:10 sphere to background activity concentration ratio and the second had cold background. Reconstruction parameters used are as follows: iterations 2 to 8, Gaussian filter 2- to 6-mm full-width-at-half-maximum, reconstruction matrices 440 × 440 and 220 × 220, high sensitivity (HS), and ultra-high sensitivity (UHS) modes. 50-, 40-, 30-, 20-, 10-, and 5-min acquisitions were reconstructed. The measurements included recovery coefficients (RC), signal-to-noise ratio (SNR), background variability, and lung error which measures the residual error in the corrections. Patient data were reconstructed with 20-, 10-, 5-, and 1-min time frames and evaluated in terms of SNR. RESULTS: The RC for the hot phantom was 0.36, 0.45, 0.53, 0.63, 0.68, and 0.84 for the spheres with diameters of 10, 13, 17, 22, 28, and 37 mm, respectively, for UHS 2 iterations, a 220 × 220 matrix, and 50-min acquisition. The RC values did not differ with acquisition times down to 20 min. The SNR was the highest for 2 iterations, measured 11.7, 16.6, 17.6, 19.4, 21.9, and 27.7 while the background variability was the lowest (27.59, 27.08, 27.36, 26.44, 30.11, and 33.51%). The lung error was 18%. For the patient dataset, the SNR was 19%, 20%, 24%, and 31% higher for 2 iterations compared to 4 iterations for 20-, 10-, 5-, and 1-min time frames, respectively. CONCLUSIONS: This study evaluates the NEMA image quality of a long AFOV PET/CT scanner with 90Y. It provides high RC for the smallest sphere compared to other standard AFOV scanners at shorter scan times. The maximum patient SNR was for 2 iterations, 20 min, while 5 min delivers images with acceptable SNR.

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner.

BACKGROUND: Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling. METHODS: Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65, and 55-65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed. RESULTS: There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55-65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB. CONCLUSION: We demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Lesion Quantification Accuracy of Digital 90Y PET Imaging in the Context of Dosimetry in Systemic Fibroblast Activation Protein Inhibitor Radionuclide Therapy.

Therapy with 90Y-labeled fibroblast activation protein inhibitors (90Y-FAPIs) was recently introduced as a novel treatment concept for patients with solid tumors. Lesion and organ-at-risk dosimetry is part of assessing treatment efficacy and safety and requires reliable quantification of tissue uptake. As 90Y quantification is limited by the low internal positron-electron pair conversion rate, the increased effective sensitivity of digital silicon photomultiplier-based PET/CT systems might increase quantification accuracy and, consequently, allow for dosimetry in 90Y-FAPI therapy. The aim of this study was to explore the conditions for reliable lesion image quantification in 90Y-FAPI radionuclide therapy using a digital PET/CT system. Methods: Two tumor phantoms were filled with 90Y solution using different sphere activity concentrations and a constant signal-to-background ratio of 40. The minimum detectable activity concentration was determined, and its dependence on acquisition time (15 vs. 30 min per bed position) and smoothing levels (all-pass vs. 5-mm gaussian filter) was investigated. Quantification accuracy was evaluated at various activity concentrations to estimate the minimum quantifiable activity concentration using contour-based and oversized volume-of-interest-based quantification approaches. A ±20% deviation range between image-derived and true activity concentrations was regarded as acceptable. Tumor dosimetry for 3 patients treated with 90Y-FAPI is presented to project the phantom results to clinical scenarios. Results: For a lesion size of 40 mm and a clinical acquisition time of 15 min, both minimum detectable and minimum quantifiable activity concentrations were 0.12 MBq/mL. For lesion sizes of greater than or equal to 30 mm, accurate quantification was feasible for detectable lesions. Only for the smallest 10-mm sphere, the minimum detectable and minimum quantifiable activity concentrations differ substantially (0.43 vs. 1.97 MBq/mL). No notable differences between the 2 quantification approaches were observed. For the investigated tumors, absorbed dose estimates with reliable accuracy were achievable. Conclusion: For lesion sizes and activity concentrations that are expected to be observed in patients treated with 90Y-FAPI, quantification with reasonable accuracy is possible. Further dosimetry studies are needed to thoroughly investigate the efficacy and safety of 90Y-FAPI therapy.

Phantom-based acquisition time and image reconstruction parameter optimisation for oncologic FDG PET/CT examinations using a digital system.

BACKGROUND: New-generation silicon-photomultiplier (SiPM)-based PET/CT systems exhibit an improved lesion detectability and image quality due to a higher detector sensitivity. Consequently, the acquisition time can be reduced while maintaining diagnostic quality. The aim of this study was to determine the lowest 18F-FDG PET acquisition time without loss of diagnostic information and to optimise image reconstruction parameters (image reconstruction algorithm, number of iterations, voxel size, Gaussian filter) by phantom imaging. Moreover, patient data are evaluated to confirm the phantom results. METHODS: Three phantoms were used: a soft-tissue tumour phantom, a bone-lung tumour phantom, and a resolution phantom. Phantom conditions (lesion sizes from 6.5 mm to 28.8 mm in diameter, lesion activity concentration of 15 kBq/mL, and signal-to-background ratio of 5:1) were derived from patient data. PET data were acquired on an SiPM-based Biograph Vision PET/CT system for 10 min in list-mode format and resampled into time frames from 30 to 300 s in 30-s increments to simulate different acquisition times. Different image reconstructions with varying iterations, voxel sizes, and Gaussian filters were probed. Contrast-to-noise-ratio (CNR), maximum, and peak signal were evaluated using the 10-min acquisition time image as reference. A threshold CNR value ≥ 5 and a maximum (peak) deviation of ± 20% were considered acceptable. 20 patient data sets were evaluated regarding lesion quantification as well as agreement and correlation between reduced and full acquisition time standard uptake values (assessed by Pearson correlation coefficient, intraclass correlation coefficient, Bland-Altman analyses, and Krippendorff's alpha). RESULTS: An acquisition time of 60 s per bed position yielded acceptable detectability and quantification results for clinically relevant phantom lesions ≥ 9.7 mm in diameter using OSEM-TOF or OSEM-TOF+PSF image reconstruction, a 4-mm Gaussian filter, and a 1.65 × 1.65 x 2.00-mm3 or 3.30 × 3.30 x 3.00-mm3 voxel size. Correlation and agreement of patient lesion quantification between full and reduced acquisition times were excellent. CONCLUSION: A threefold reduction in acquisition time is possible. Patients might benefit from more comfortable examinations or reduced radiation exposure, if instead of the acquisition time the applied activity is reduced.

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. METHODS: Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. RESULTS: No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. CONCLUSION: We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners.

Abbreviated scan protocols to capture 18F-FDG kinetics for long axial FOV PET scanners.

PURPOSE: Kinetic parameters from dynamic 18F-fluorodeoxyglucose (FDG) imaging offer complementary insights to the study of disease compared to static clinical imaging. However, dynamic imaging protocols are cumbersome due to the long acquisition time. Long axial field-of-view (LAFOV) PET scanners (> 70 cm) have two advantages for dynamic imaging over clinical PET scanners with a standard axial field-of-view (SAFOV; 16-30 cm). The large axial coverage enables multi-organ dynamic imaging in a single bed position, and the high sensitivity may enable clinically routine abbreviated dynamic imaging protocols. METHODS: In this work, we studied two abbreviated protocols using data from a 65-min dynamic 18F-FDG scan: (A) dynamic imaging immediately post-injection (p.i.) for variable durations, and (B) dynamic imaging immediately p.i. for variable durations plus a 1-h p.i. (5-min-long) datapoint. Nine cancer patients were imaged on the Biograph Vision Quadra (Siemens Healthineers). Time-activity curves over the lesions (N = 39) were fitted using the Patlak graphical analysis and a 2-tissue-compartment (2C, k4 = 0) model for variable scan durations (5-60 min). Kinetic parameters from the complete dataset served as the reference. Lesions from all cancers were grouped into low, medium, and high flux groups, and bias and precision of Ki (Patlak) and Ki, K1, k2, and k3 (2C) were calculated for each group. RESULTS: Using only early dynamic data with the 2C (or Patlak) model, accurate quantification of Ki required at least 50 (or 55) min of dynamic data for low flux lesions, at least 30 (or 40) min for medium flux lesions, and at least 15 (or 20) min for high flux lesions to achieve both 10% bias and precision. The addition of the final (5-min) datapoint allowed for accurate quantification of Ki with a bias and precision of 10% using only 10-15 min of early dynamic data for either model. CONCLUSION: Dynamic imaging for 10-15 min immediately p.i. followed by a 5-min scan at 1-h p.i can accurately and precisely quantify 18F-FDG on a long axial FOV scanner, potentially allowing for more widespread use of dynamic 18F-FDG imaging.

Current Standards for and Clinical Impact of Emergency Radiology in Major Trauma.

In industrialized countries, high energy trauma represents the leading cause of death and disability among people under 35 years of age. The two leading causes of mortality are neurological injuries and bleeding. Clinical evaluation is often unreliable in determining if, when and where injuries should be treated. Traditionally, surgery was the mainstay for assessment of injuries but advances in imaging techniques, particularly in computed tomography (CT), have contributed in progressively changing the classic clinical paradigm for major traumas, better defining the indications for surgery. Actually, the vast majority of traumas are now treated nonoperatively with a significant reduction in morbidity and mortality compared to the past. In this sense, another crucial point is the advent of interventional radiology (IR) in the treatment of vascular injuries after blunt trauma. IR enables the most effective nonoperative treatment of all vascular injuries. Indications for IR depend on the CT evidence of vascular injuries and, therefore, a robust CT protocol and the radiologist's expertise are crucial. Emergency and IR radiologists form an integral part of the trauma team and are crucial for tailored management of traumatic injuries.

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients.

PURPOSE: To investigate the kinetics of 18F-fluorodeoxyglucose (18F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF). METHODS: Twenty-four oncological patients underwent dynamic 18F-FDG scans lasting 65 min using a long  axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated. RESULTS: We report estimates of kinetic parameters and metabolic rate of glucose consumption (MRFDG) for different organs and tumor lesions. In some organs, there were significant differences between MRFDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MRFDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (Ki) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBRmean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method. CONCLUSION: We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body 18F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.

A CT-less approach to quantitative PET imaging using the LSO intrinsic radiation for long-axial FOV PET scanners.

PURPOSE: Long-axial field-of-view (FOV) positron emission tomography (PET) scanners have gained a lot of interest in the recent years. Such scanners provide increased sensitivity and enable unique imaging opportunities that were not previously feasible. Benefiting from the high sensitivity of a long-axial FOV PET scanner, we studied a computed tomography (CT)-less reconstruction algorithm for the Siemens Biograph Vision Quadra with an axial FOV of 106 cm. METHODS: In this work, the background radiation from radioisotope lutetium-176 in the scintillators was used to create an initial estimate of the attenuation maps. Then, joint activity and attenuation reconstruction algorithms were used to create an improved attenuation map of the object. The final attenuation maps were then used to reconstruct quantitative PET images, which were compared against CT-based PET images. The proposed method was evaluated on data from three patients who underwent a flurodeoxyglucouse PET scan. RESULTS: Segmentation of the PET images of the three studied patients showed an average quantitative error of 6.5%-8.3% across all studied organs when using attenuation maps from maximum likelihood estimation of attenuation and activity and 5.3%-6.6% when using attenuation maps from maximum likelihood estimation of activity and attenuation correction coefficients. CONCLUSIONS: Benefiting from the background radiation of lutetium-based scintillators, a quantitative CT-less PET imaging technique was evaluated in this work.

Silicon-photomultiplier-based PET/CT reduces the minimum detectable activity of iodine-124.

The radioiodine isotope pair 124I/131I is used in a theranostic approach for patient-specific treatment of differentiated thyroid cancer. Lesion detectability is notably higher for 124I PET (positron emission tomography) than for 131I gamma camera imaging but can be limited for small and low uptake lesions. The recently introduced silicon-photomultiplier-based (SiPM-based) PET/CT (computed tomography) systems outperform previous-generation systems in detector sensitivity, coincidence time resolution, and spatial resolution. Hence, SiPM-based PET/CT shows an improved detectability, particularly for small lesions. In this study, we compare the size-dependant minimum detectable 124I activity (MDA) between the SiPM-based Biograph Vision and the previous-generation Biograph mCT PET/CT systems and we attempt to predict the response to 131I radioiodine therapy of lesions additionally identified on the SiPM-based system. A tumour phantom mimicking challenging conditions (derived from published patient data) was used; i.e., 6 small spheres (diameter of 3.7-9.7 mm), 9 low activity concentrations (0.25-25 kBq/mL), and a very low signal-to-background ratio (20:1). List-mode emission data (single-bed position) were divided into frames of 4, 8, 16, and 30 min. Images were reconstructed with ordinary Poisson ordered-subsets expectation maximization (OSEM), additional time-of-flight (OSEM-TOF) or TOF and point spread function modelling (OSEM-TOF+PSF). The signal-to-noise ratio and the MDA were determined. Absorbed dose estimations were performed to assess possible treatment response to high-activity 131I radioiodine therapy. The signal-to-noise ratio and the MDA were improved from the mCT to the Vision, from OSEM to OSEM-TOF and from OSEM-TOF to OSEM-TOF+PSF reconstructed images, and from shorter to longer emission times. The overall mean MDA ratio of the Vision to the mCT was 0.52 ± 0.18. The absorbed dose estimations indicate that lesions ≥ 6.5 mm with expected response to radioiodine therapy would be detectable on both systems at 4-min emission time. Additional smaller lesions of therapeutic relevance could be detected when using a SiPM-based PET system at clinically reasonable emission times. This study demonstrates that additional lesions with predicted response to 131I radioiodine therapy can be detected. Further clinical evaluation is warranted to evaluate if negative 124I PET scans on a SiPM-based system can be sufficient to preclude patients from blind radioiodine therapy.

Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging.

PURPOSE: The aim of this methods work is to explore the different behavior of radiomic features resulting by using or not the contrast medium in chest CT imaging of non-small cell lung cancer. METHODS: Chest CT scans, unenhanced and contrast-enhanced, of 17 patients were selected from images collected as part of the staging process. The major T1-T3 lesion was contoured through a semi-automatic approach. These lesions formed the lesion phantoms to study features behavior. The stability of 94 features of the 3D-Slicer package Radiomics was analyzed. Feature discrimination power was quantified by means of Gini's coefficient. Correlation between distance matrices was evaluated through Mantel statistic. Heatmap, cluster and silhouette plots were applied to find well-structured partitions of lesions. RESULTS: The Gini's coefficient evidenced a low discrimination power, <0.05, for four features and a large discrimination power, around 0.8, for five features. About 90% of features was affected by the contrast medium, masking tumor lesions variability; thirteen features only were found stable. On 8178 combinations of stable features, only one group of four features produced the same partition of lesions with the silhouette width greater than 0.51, both on unenhanced and contrast-enhanced images. CONCLUSIONS: Gini's coefficient highlighted the features discrimination power in both CT series. Many features were sensitive to the use of the contrast medium, masking the lesions intrinsic variability. Four stable features produced, on both series, the same partition of cancer lesions with reasonable structure; this may merit being objects of further validation studies and interpretative investigations.

Evaluation of [68Ga]Ga-PSMA PET/CT images acquired with a reduced scan time duration in prostate cancer patients using the digital biograph vision.

AIM: [68Ga]Ga-PSMA-11 PET/CT allows for a superior detection of prostate cancer tissue, especially in the context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration/administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand [68Ga]Ga-PSMA-11 PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance. METHODS: Twenty prostate cancer patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent [68Ga]Ga-PSMA-11 PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a mean scan time duration of 16.7 min (reference acquisition protocol) and 4.6 min (reduced acquisition protocol). Four iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak), and (c) image noise using the liver's activity distribution. RESULTS: Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13 to 21% (4 iterations) and from 10 to 15% (2 iterations) for PSF + TOF and TOF images. CONCLUSIONS: High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered [68Ga]Ga-PSMA-11 activities can be considered in metastatic patients, where missing small lesions would not impact patient management. Limitations include the small and heterogeneous sample size and the lack of follow-up.

Comparing lesion detection efficacy and image quality across different PET system generations to optimize the iodine-124 PET protocol for recurrent thyroid cancer.

BACKGROUND: In recurrent differentiated thyroid cancer patients, detectability in 124I PET is limited for lesions with low radioiodine uptake. We assess the improvements in lesion detectability and image quality between three generations of PET scanners with different detector technologies. The results are used to suggest an optimized protocol. METHODS: Datasets of 10 patients with low increasing thyroglobulin or thyroglobulin antibody levels after total thyroidectomy and radioiodine therapies were included. PET data were acquired and reconstructed on a Biograph mCT PET/CT (whole-body, 4-min acquisition time per bed position; OSEM, OSEM-TOF, OSEM-TOF+PSF), a non-TOF Biograph mMR PET/MR (neck region, 4 min and 20 min; OSEM), and a new generation Biograph Vision PET/CT (whole-body, 4 min; OSEM, OSEM-TOF, OSEM-TOF+PSF). The 20-min image on the mMR was used as reference to calculate the detection efficacy in the neck region. Image quality was rated on a 5-point scale. RESULTS: All detected lesions were in the neck region. Detection efficacy was 8/9 (Vision OSEM-TOF and OSEM-TOF+PSF), 4/9 (Vision OSEM), 3/9 (mMR OSEM and mCT OSEM-TOF+PSF), and 2/9 (mCT OSEM and OSEM-TOF). Median image quality was 4 (Vision OSEM-TOF and OSEM-TOF+PSF), 3 (Vision OSEM, mCT OSEM-TOF+PSF, and mMR OSEM 20-min), 2 (mCT OSEM-TOF), 1.5 (mCT OSEM), and 1 (mMR OSEM 4 min). CONCLUSION: At a clinical standard acquisition time of 4 min per bed position, the new generation Biograph Vision using a TOF-based image reconstruction demonstrated the highest detectability and image quality and should, if available, be preferably used for imaging of low-uptake lesions. A prolonged acquisition time for the mostly affected neck region can be useful.

Evaluation of 18F-FDG PET/CT images acquired with a reduced scan time duration in lymphoma patients using the digital biograph vision.

BACKGROUND: The superior accuracy and sensitivity of 18F-FDG-PET/CT in comparison to morphological imaging alone leads to an upstaging in up to 30% of lymphoma patients. Novel digital PET/CT scanners might enable to reduce administered tracer activity or scan time duration while maintaining diagnostic performance; this might allow for a higher patient throughput or a reduced radiation exposure, respectively. In particular, the radiation exposure reduction is of interest due to the often young age and high remission rate of lymphoma patients. METHODS: Twenty patients with (suspected) lymphoma (6 for initial staging, 12 after systemic treatment, 2 in suspicion of recurrence) sequentially underwent 18F-FDG-PET/CT examinations on a digital PET/CT (Siemens Biograph Vision) with a total scan time duration of 15 min (reference acquisition protocol) and 5 min (reduced acquisition protocol) using continuous-bed-motion. Both data sets were reconstructed using either standalone time of flight (TOF) or in combination with point spread function (PSF), each with 2 and 4 iterations. Lesion detectability by blinded assessment (separately for supra- and infradiaphragmal nodal lesions and for extranodal lesions), lesion image quantification, and image noise were used as metrics to assess diagnostic performance. Additionally, Deauville Score was compared for all patients after systemic treatment. RESULTS: All defined regions were correctly classified in the images acquired with reduced emission time, and therefore, no changes in staging were observed. Lesion quantification was acceptable, that is, mean absolute percentage deviation of maximum and peak standardized uptake values were 6.8 and 6.4% (derived from 30 lesions). A threefold reduction of scan time duration led to an increase in image noise from 7.1 to 11.0% (images reconstructed with 4 iterations) and from 4.7 to 7.2% (images reconstructed with 2 iterations). No deviations in Deauville Score were observed. CONCLUSION: These results suggest that scan time duration or administered tracer activity can be reduced threefold without compromising diagnostic performance. Especially a reduction of administered activity might allow for a lower radiation exposure and better health economics. Larger trials are warranted to confirm our results.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.

PURPOSE: To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning. METHODS: We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images. RESULTS: LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%. CONCLUSION: LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.