The Positron Emission Tomography (PET)/Magnetic Resonance Imaging (MRI) scanner combines two diagnostic imaging modalities, providing information on anatomy and physiology. Beneficial diagnosis areas are epilepsy ([[Formula: see text]F]Fluorodeoxyglucose (FDG)) and cancer recurrence ([[Formula: see text]C]methionine (MET)), where subject motion during PET acquisition reduces image quality, potentially compromising diagnostic accuracy. This project aimed to evaluate the impact of PET data-driven motion correction (ddMC) of these clinical PET radiotracers and to assess, for the first time, whether the automatic motion categorization reflects motion levels impacting the image quality. Eighty-nine PET scans (66 [[Formula: see text]C]MET, 23 [[Formula: see text]F]FDG) were reconstructed with ddMC and without motion correction (noMC) using the research software lmDuetto toolbox (GE Healthcare, Chicago, IL, USA), and were automatically categorized into motion groups. MRI images were segmented, and the regions of interest (ROIs) transferred to the PET space. The effect of ddMC was analyzed by relative signal differences between ddMC and noMC. Motion estimation and categorization were evaluated by normalized cross correlation (XC) over time and the proposed cumulative displacement-time histogram (cDTH). Overall, ddMC increased signal values within cortical ROIs compared to noMC. In the high motion category, median relative mean signal differences were 0.61% (0.41-0.80%) for [[Formula: see text]F]FDG and 0.70% (0.61-0.79%) for [[Formula: see text]C]MET. The XC improved ([[Formula: see text]F]FDG: 0.80 to 0.97, [[Formula: see text]C]MET: 0.85 to 0.98). Low and medium motion groups had lesser impact, indicating motion correction is most relevant for high motion. The XC and cDTH identified subjects whose motion classification should be revised. In conclusion, the results confirm previous findings with ddMC using [[Formula: see text]F]FDG and demonstrate its suitability for lower-accumulating [[Formula: see text]C]MET. The automatic motion categorization needs re-evaluation to better reflect motion affecting PET image quality.
Head motion during dynamic positron emission tomography (dPET) compromises pharmacokinetic modeling, especially in prolonged acquisitions where motion artifacts invalidate quantitative analysis. Although motion correction methods are well-established for static PET, their application in dynamic imaging remains limited. This study evaluates the efficacy of a data-driven dynamic head motion correction (dHMC) algorithm in preserving the reliability of kinetic parameter, using dynamic 68 Ga-PSMA-11 PET of intracranial tumors as a challenging model due to the tracer’s inability to cross the blood–brain barrier, unlike the diffusely distributed 18F-FDG used in prior studies. Sixteen patients with suspected glioma based on preoperative imaging underwent 40-min dynamic 68 Ga-PSMA-11 PET head scans. Head motion was categorized into minor and high-motion groups based on maximum displacement (> 3 mm) or time-weighted mean displacement (> 2 mm) within the tumor region. The performance of dHMC was evaluated in tumor and reference organs (parotid and lacrimal glands) based on fitting metrics, including the coefficient of determination (R2) and Akaike information criterion (AIC), and quantitative kinetic parameters. Time-activity curves (TACs) and key kinetic parameters (k2 and k3) were generated using an irreversible two-tissue compartment model (2T3k). dHMC robustly corrected motion-induced inaccuracies across motion levels, enabling analysis in 43.75% of previously non-analyzable cases. The algorithm yielded three major improvements without introducing bias: First, it significantly enhanced pharmacokinetic modeling fidelity, evidenced by smoother TACs, along with increased R2 values of 8.35%, 4.54%, and 30.9% in tumor, parotid, and lacrimal glands, respectively, and reduced AIC values by 6.95%, 5.33%, and 13.32%. Second, it improved the accuracy and homogeneity of k2 and k3 estimates in reference organs, indicated by significantly reduced data dispersion. Third, it brought the quantitative parameters of the parotid gland closer to established reference standards. All improvements were most pronounced in high-motion cases. The dHMC algorithm enhances fitting performance, parametric accuracy, and inter-frame consistency in dynamic 68 Ga-PSMA-11 PET, validating its broad applicability across varying motion scenarios and underscoring the essential role of motion correction in maintaining quantitative accuracy in dPET. The online version contains supplementary material available at 10.1186/s40658-026-00843-x.
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The aim of this study was to evaluate the efficacy of neuroendocrine neoplasms (NENs) patients treated with peptide receptor radionuclide therapy (PRRT), and to investigate the relationships among the absorbed dose of the tumor target lesions, standardized uptake value (SUV), and the therapeutic efficacy of the patients. A total of 22 NENs patients who received [177Lu]Lu-DOTA-TATE treatment and underwent consecutive single-photon emission computed tomography/computed tomography (SPECT/CT) imaging after drug administration were included in this study. After the SPECT/CT imaging, the volume of interest (VOI) of the target organs and the target lesions were delineated using the Hermes Internal Radiation Dosimetry (HIRD) software, and their absorbed doses were calculated. The SUV of the target lesions was measured on the reconstructed SPECT/CT fusion images. We conducted studies on the correlations between the absorbed dose, SUV, and the therapeutic efficacy of the patients respectively. After the patients received [177Lu]Lu-DOTA-TATE treatment in cycles 1 to 4, the absorbed doses of the tumor target lesions were 21.0, 16.0, 11.4, and 8.8 Gy respectively. The results of the correlations between the absorbed dose, SUV, and the therapeutic efficacy of the patients showed that both the absorbed dose and SUV had an "S"-shaped curve relationship with the tumor partial response (PR) rate. In addition, for the 1st to 4th PRRT treatments, the absorbed dose and SUV were linearly correlated with R2 of 0.7, 0.8, 0.8, and 0.9, respectively. Through the calculation and analysis of the radionuclide therapy dosimetry based on the SPECT/CT images throughout the PRRT treatment of the patients, the absorbed dose and SUV of the patients can provide important dose guidance for radionuclide therapy, which is helpful to improve the treatment efficacy of the patients. A Study Comparing Treatment With Lutetium[177Lu] Oxodotreotide Injection to Octreotide LAR in Patients With GEP-NETs, NCT05459844. Registered 5 July 2022, https://clinicaltrials.gov/study/NCT05459844?cond=NCT05459844&rank=1 .
This study aimed to evaluate the image quality-dose trade-off in pregnant patients imaged with long-axial field-of-view [18F]FDG PET/CT and to identify the most predictive body composition metric for image quality to develop a pregnancy-tailored dosage model. Patients imaged with [18F]FDG PET/CT according to local pregnancy protocols were included in this study. Using raw PET data, images of various degrees of image quality were reconstructed. Acceptable image quality was identified using signal-to-noise ratio (SNR) in the liver and Likert scores. The minimum required scan statistics was modelled based on SNR and patient body composition. F-tests were used to find the best-fitting model parameter out of weight, weight-to-height-ratio, body-mass-index, and body surface area (BSA). Foetal dose was estimated with PET conversion factors and size-specific CT dose index values. Eleven patients were included in image quality analysis and dosage model optimization. SNR strongly correlated with Likert scores (R² = 0.80), with 10.72 SNR indicating acceptable image quality. BSA best predicted image quality (R² = 0.85), outperforming weight (R² = 0.78), weight-to-height ratio (R² = 0.63), and body mass index (R² = 0.38). The proposed dosage model reduces activity by 41-96% compared to current local pregnancy and adult protocols, resulting in estimated foetal radiation doses of 0.066 mGy (PET) and 0.31 mGy (CT). BSA accurately predicted [18F]FDG PET/CT image quality in pregnant patients. The proposed dosage regimen allows significant dose reduction and can be used as a foundation for the development of pregnancy dosage protocols.
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Reducing acquisition time in PET/CT imaging can degrade image quality and may compromise both diagnostic reliability and the robustness of radiomic features. This study investigates, in a large clinical cohort, whether AI-based denoising can preserve image quality and maintain the accuracy of quantitative and radiomic parameters in [18F]FDG and [68Ga]Ga-PSMA-11 PET/CT scans. We reconstructed three sets of images: 100% acquisition time (R100), 75% (R75), and 50% (R50), with their respective denoised versions using SubtlePET® (S75 and S50). On a NEMA phantom, we analysed six contrasts (12:1-2:1) using [18F]FDG, assessing contrast, background noise, and radiomic features. In a cohort of 282 patients injected with [18F]FDG and [68Ga]Ga-PSMA-11, five nuclear medicine physicians performed a qualitative evaluation of image quality and confidence in the presence of hypermetabolism in 634 lesions. 109 radiomic features from 105 lesions were compared between the original and denoised reconstructions. The phantom study showed no difference in sphere contrast, a reduction in background noise variability, and excellent preservation of radiomic features. In the clinical population, S75 images showed improvements across all criteria evaluated, except for diagnostic confidence, which remained higher with R75 (p = 0.555 when compared to R100) for [18F]FDG. For [68Ga]Ga-PSMA-11, only S50 images showed a significant degradation in liver image quality. A decrease in SUVmax was observed in denoised images (- 7.73% for [18F]FDG; - 11.46% for [68Ga]Ga-PSMA-11, p < 0.0001). The radiomic analysis demonstrated excellent correlation, with a concordance correlation coefficient (CCC) > 0.8 for 90% of radiomic features. SubtlePET® improves the image quality of PET acquisitions performed with reduced acquisition times using [18F]FDG and [68Ga]Ga-PSMA. However, clinician confidence remains limited and, while denoised acquisitions preserve most radiomic features, others are altered, potentially limiting model transposability.
Glutamate carboxypeptidase II (GCPII), also known as prostate-specific membrane antigen (PSMA) is overexpressed in 90-100% of prostate cancer cells. The radiopharmaceutical [18F]PSMA-1007, recognised as a PET tracer for prostate cancer imaging, is based on PSMA inhibitor [Glu-CO-Lys(2Nal-Amb-Glu-Glu-PyTMA)] bound to the radioisotope Fluorine-18. [18F]Fluoride was obtained via the 18O(p,n)18F reaction using a cyclotron for medical use, while synthesis of [18F]PSMA-1007 was performed with two different platforms: FASTlab2 and NEPTIS® Perform. Both modules enabled synthesis through nucleophilic substitution reaction and subsequent purification in solid phase extraction (SPE). Quality control process was validated according to the current specific monograph (3116) of the European Pharmacopoeia (Ph. Eur.) before clinical use. Twenty syntheses of [18F]PSMA-1007 for each module were performed in order to evaluate and compare radiochemical purity (96.58% ± 1.25 with FASTlab2 vs 95.86% ± 0.79 with NEPTIS® Perform) and decay-corrected radiochemical yield (43.7% ± 3 with FASTlab2 vs 28.5% ± 3.1 with NEPTIS® Perform). Both platforms produced [18F]PSMA-1007 that consistently met all pharmacopoeial quality control standards. However, the FASTlab2 system demonstrated a statistically significant higher decay-corrected radiochemical yield (43.7% ± 3% vs. 28.5% ± 3.1%, p-value < 0.001 after statistical testing). While this yield difference does not impact radiochemical purity or product safety, it may represent a relevant advantage in terms of production efficiency and available activity for clinical use, which may influence the choice of synthesizer.
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Quantitative 177Lu-SPECT allows for patient specific dosimetry, but due to the limited spatial resolution absorbed doses (AD) can be underestimated. Implementation of the Lucy-Richardson deconvolution (LRD) algorithm for spill-over correction in PET has been investigated. Therefore, the aim of this study was to extend the potential application of LRD to 177Lu-SPECT based tumor dosimetry. The NEMA IEC Body Phantom (foreground-to-background ratio 8:1, 237:30 kBq/mL) was measured according to the local imaging and reconstruction protocol. The two main parameters of LRD, sigma and number of iterations, were determined in two steps. First, a matched filter resolution analysis was conducted on the ground truth activity distribution as segmented from the NEMA IEC Body Phantom data to define the sigma of a 3D Gaussian point-spread-function, which describes the system's spatial resolution. Secondly, using this sigma, a suitable number of LRD iterations was determined by comparing sphere recovery coefficients (RC) and signal-to-noise ratios. The selected parameters were then applied to the reconstructed SPECT series (24, 48, and 72 h post-injection) of 20 patients who received either [177Lu]Lu-DOTA-TATE (n = 10) or [177Lu]Lu-PSMA-I&T (y = 10) treatment, in order to evaluate its impact on AD estimates. Lesion AD from the original reconstruction (OR) and OR + LRD were estimated using MIM SurePlan™ MRT. The AD from OR, OR + LRD, and OR + RC (phantom-based recovery correction based on volume) were compared. A sigma of 6.0 mm and four iterations resulted in an average improvement of 18.9 ± 4.7% and 17.4 ± 7.6% in the sphere recovery coefficients and the signal to noise ratio, respectively. In total, 98 lesions were evaluated ([177Lu]Lu-DOTA-TATE: n = 42) ([177Lu]Lu-PSMA-I&T: y = 56). For OR + LRD and OR + RC an average increase of 22 ± 12% and 57 ± 36% of tumor AD was found. OR + LRD increased AD compared to OR, independent of administered radiopharmaceutical and lesion location. This study suggests that implementing LRD may be a promising option for image-based spill-over correction in 177Lu-SPECT based dosimetry. Further studies are necessary to investigate the effect of different PVC methods, such as LRD or phantom-based correction factors, on overall uncertainty of lesion ADs.
Photon scatter significantly degrades Single Photon Emission Computed Tomography (SPECT) image quality, with scattered photons accounting for 30-40% of detected counts within standard energy windows. While conventional scatter correction methods face limitations including noise amplification and computational demands, wavelet transforms offer promising capabilities for sinogram-domain correction. However, comprehensive parameter optimization remains unexplored. We evaluated 96 mother wavelets across seven families, implementing three decomposition levels and five thresholding strategies in a Monte Carlo simulation framework. Scatter-contaminated sinograms were processed using discrete wavelet transforms and reconstructed via filtered backprojection. Quantitative assessment employed Universal Image Quality Index (UIQI) with varying block sizes (3 × 3, 25 × 25, 128 × 128) and Root Mean Square Error (RMSE). Three nuclear medicine physicians performed blinded qualitative assessment of the processed images. Among 94 viable wavelets (excluding outliers db45 and rbio3.1), global optimization identified Rigrsure and Heursure thresholding at decomposition level 1 as optimal for maximizing UIQI (0.559 ± 0.002), while per-slice optimization favored Minimaxi thresholding at level 2. Strong positive correlation existed between UIQI (25 × 25) and UIQI (128 × 128) (r = 0.887, p < 0.01), with both metrics inversely related to RMSE error (r≈ - 0.73, p < 0.01). Despite UIQI optimization, RMSE-optimized images received significantly higher visual quality rankings from physicians (69% improvement, p < 0.001), revealing critical divergence between quantitative metrics and diagnostic utility. This study establishes wavelet-based scatter correction as a viable approach for SPECT image enhancement through systematic parameter mapping. The marked preference for RMSE-optimized images over UIQI-optimized ones underscores the necessity of aligning algorithmic optimization with clinical perception rather than technical metrics alone. These findings provide a foundation for standardizing wavelet implementation in SPECT scatter correction, directly connecting mathematical optimization to diagnostic relevance in nuclear medicine imaging.
Technetium-99 m-labeled macroaggregated albumin ([⁹⁹ᵐTc]Tc-MAA) is the standard agent for lung perfusion imaging in pulmonary embolism (PE). Limitations related to particle consistency, preparation procedures, and blood-derived origin have prompted the development of alternative non-blood-derived tracers. A narrative review of studies up to August 2025 was performed. Candidate radiopharmaceuticals were evaluated for pulmonary localization, physicochemical properties, quality control characteristics, radiopharmacy practicality, kit-based preparation, and preclinical or clinical validation. Biodegradable microspheres, synthetic colloids, starch-based microparticles, and small-molecule complexes demonstrated promising lung uptake. Most tracers, however, lacked standardized preparation, kit compatibility, or validation in PE-relevant models. Starch-based microparticles emerged as the most translationally promising, showing practical workflow and favorable biodistribution. No non-blood-derived ⁹⁹ᵐTc tracer currently matches [⁹⁹ᵐTc]Tc-MAA for routine lung perfusion imaging. Future development requires standardized, pharmacopeia-aligned tracers, head-to-head comparisons, and systematic evaluation in early-phase clinical trials.
A position paper released by the European Association of Nuclear Medicine emphasised the need for multidisciplinary engagement to establish dosimetry-based personalised treatment in Radionuclide therapy (RNT). The uncertainty analysis results often ignored in routine clinical practice should be incorporated into the dose calculations to improve the efficacy and accuracy of treatment. In this study, patients with haematological malignancies undergoing radioimmunotherapy were evaluated. Our study aimed to calculate the uncertainties associated with each parameter of the single time point (STP) dosimetry chain and compare the with multiple time points (MTP) in the bone marrow and liver results. 28 patients received an intravenous injection of 111In-besilesomab (0.17 ± 0.01GBq) for pre-therapeutic dosimetry and were subsequently treated with 90Y-besilesomab(2.43 ± 0.53GBq). A dosimetry analysis was performed on bone marrow (BM) and liver with MTP and STP. We investigated the uncertainty in population mean effective half-life, volume, recovery coefficient, counts, measured activity, fitting parameters, time-integrated-activity, S-factors, and absorbed dose (AD) for a group of patients. The mean absorbed dose per unit administered activity (DpA) to BM was 5.8 ± 1.7 mGy/MBq with MTP and 5.8 ± 1.6 mGy/MBq with STP, and to the liver was 2.9 ± 1.9 mGy/MBq with MTP and 3.1 ± 2.4 mGy/MBq with STP. The mean fractional uncertainty associated with total absorbed dose to BM was 13.18 ± 3.46% with MTP and 18.75 ± 3.22% with STP, and to liver was 5.77 ± 3.13% with MTP and 49.78 ± 25.36% with STP. A moderate positive relationship (R2 = 0.7) was noted between post-injection acquisition time and AD uncertainty with STP for BM, whereas a strong positive relationship (R2 = 1) was noted for the liver. The absorbed dose uncertainty in STP was significantly higher compared to the MTP. Incorporating the uncertainty analysis for STP dosimetry parameters in routine clinical practice is strongly recommended. The accuracy in the acquisition time, population-based half-life and fitting function for time activity curve is vital for minimising uncertainty in STP dosimetry, which is less time-consuming and easier to implement in clinical practice than MTP.
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Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accurate kinetic modeling requires precise input function estimation, traditionally achieved through arterial blood sampling. However, arterial cannulation in small animals, such as mice, involves intricate, time-consuming, and terminal procedures, precluding longitudinal studies. This work proposes a non-invasive, fully convolutional deep learning-based approach (FC-DLIF) to predict input functions directly from PET imaging data, which may eliminate the need for arterial blood sampling in the context of dynamic small-animal PET imaging. The proposed FC-DLIF model consists of a spatial feature extractor that acts on the volumetric time frames of the dynamic PET imaging sequence, extracting spatial features. These are subsequently further processed in a temporal feature extractor that predicts the arterial input function. The proposed approach is trained and evaluated using images and arterial blood curves from [18F]FDG data using cross validation. Further, the model applicability is evaluated on imaging data and arterial blood curves collected using two additional radiotracers ([18F]FDOPA, and [68Ga]PSMA). The model was further evaluated on data truncated and shifted in time, to simulate shorter, and shifted, PET scans. The proposed FC-DLIF model reliably predicts the arterial input function with respect to mean squared error and correlation. Furthermore, the FC-DLIF model is able to predict the arterial input function even from truncated and shifted samples. The model fails to predict the AIF from samples collected using different radiotracers, as these are not represented in the training data. Our deep learning-based input function offers a non-invasive and reliable alternative to arterial blood sampling, proving robust and flexible to temporal shifts and different scan durations. The online version contains supplementary material available at 10.1186/s13550-026-01398-9.
Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to prospectively evaluate the clinical utility of the unified data-driven respiratory motion correction (uRMC) algorithm utilizing deep learning neural networks for diagnosing upper abdominal lesions. A total of 100 patients with suspected upper abdominal lesions who underwent 18F-FDG PET/CT were enrolled. Two senior physicians independently conducted subjective visual assessments and semi-quantitative analyses of the PET/CT images before and after applying uRMC. Subjective visual evaluation parameters included overall image quality, PET-CT misalignment, and lesion distortion. Additionally, physicians identified involved upper-abdominal lesions in both images. Semi-quantitative metrics recorded for each lesion included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), tumor-to-background ratio (TBR), and horizontal-to-vertical ratio (HV_ratio) before and after correction. Percentage changes in lesion SUVmax and MTV were calculated, and subgroup analyses were performed to assess the impact of lesion uptake, volume, and displacement on the performance of the uRMC algorithm. Compared to no motion correction (NMC) images, 78% (78/100) of patients demonstrated improved overall image quality after uRMC reconstruction, with 68.9% (155/225) of lesion showing improved PET-CT alignment and 64.0% (144/225) demonstrating reduced lesion distortion (all p < 0.001). The RM-corrected images exhibited a significantly higher SUVmax (9.07 [6.45, 11.79] vs.7.46 [5.69, 10.00], p < 0.001) and TBR (3.65 [2.54, 4.98] vs. 3.17 [2.40, 4.38], p < 0.001). The number of detected lesions increased from 171 (NMC) to 181 (uRMC) in 62 patients, with 10 additional suspicious lesions identified in 14.5% (9/62) of cases. Moreover, 7 lesions in 9.7% (6/62) of patients exhibited improved PET-CT alignment after uRMC correction. The uRMC algorithm also significantly reduced lesion MTV (1359.6 [690.8, 3837.6] mm3 vs.1710.5 [899.1, 4013.0] mm3, p < 0.01) and HV_ratio (0.99 [0.82, 1.09] vs. 1.16 [1.02, 1.44], p < 0.01). Subgroup-based analyses revealed that uRMC outperformed NMC in detecting low-uptake and small-volume lesions, with SUVmax improvements being more pronounced in lesions with larger displacement (17.8% vs. 9.8%, p < 0.001). Compared with conventional NMC reconstruction, the uRMC algorithm significantly enhances overall image quality, PET-CT alignment, and lesion delineation. Furthermore, it improves the detection of low-uptake and small-volume lesions in the upper abdomen, thereby increasing the accuracy and reliability of clinical diagnoses and supporting more informed therapeutic decision-making.
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