Search Results (131)

Objective: We present the first clinical experience with the BIOGRAPH One next-generation PET/MRI system scanner, evaluating its performance for body and brain imaging in patients across multiple tracers. Methods: A total of 59 patients were scanned on the BIOGRAPH One PET/MRI following standard clinical PET/CT (n = 52) or first-generation PET/MRI (Biograph mMR, n = 7). Scans comprised 30 total body (TB), whole body (WB), or regional scans with [¹⁸F]FDG, and 29 brain scans with either [¹⁸F]FDG (n = 5), [¹⁸F]FE-PE2I (n = 10), [¹⁸F]FET (n = 4), or [⁶⁸Ga]Ga-DOTATOC (n = 10). The PET image quality was visually assessed using a 5-point Likert scale (1 = very good to 5 = very bad) and compared with clinical scans acquired on either a current-generation digital PET/CT or a first-generation PET/MRI system, including evaluation of diagnostic concordance. PET quantification and image noise was compared in brain and WB/TB [¹⁸F]FDG PET scans. Results: PET image quality was rated as good or very good in 93% of scans with a median [inter-quartile range] score of 1.5 [1.5;2]. In 99% of cases, image quality was judged equal to or better than the clinical reference scan (median score 3 [2.5;3]). Diagnostic concordance was observed in 99% of readings. Imaging metrics revealed the anticipated regional bias in brain imaging, while no significant bias was observed in body imaging. Image noise was comparable to that observed with digital PET/CT and demonstrated superiority over first-generation PET/MRI despite potential degradation related to isotope decay in BIOGRAPH One PET/MRI acquisitions scans performed at the end of the imaging workflow. Conclusions: Within the study limitations related to sequential imaging, the BIOGRAPH One PET/MRI scanner demonstrated improved PET sensitivity and workflow potential over its first-generation predecessor, which may allow for broader clinical and research applications. Full article

Background: Previous attempts to classify lymphoma subtypes based on metabolic features extracted from ¹⁸F-FDG PET imaging have been hindered by inconsistencies in imaging protocols, scanner types, and inter-institutional variability. To overcome these limitations, we propose a multimodal deep learning framework that integrates harmonized PET imaging features with structured clinical information. The proposed framework is designed to perform hierarchical classification of clinically meaningful lymphoma subtypes through two sequential binary classification tasks. Methods: We collected multi-center data comprising ¹⁸F-FDG PET images and structured clinical variables of patients with lymphoma. To mitigate domain shifts caused by different scanner manufacturers, we integrated a Scanner-Conditioned Normalization (SCN) module, which adaptively harmonizes feature distributions using manufacturer-specific parameters. Performance was validated using internal and external cohorts, with the statistical significance of performance gains assessed via DeLong’s test and bootstrap-based CI analysis. Results: The proposed model achieved an area under the curve (AUC) of 0.89 (internal) and 0.84 (external) for Hodgkin lymphoma versus non-Hodgkin lymphoma classification and 0.84 (internal) and 0.76 (external) for diffuse large B-cell lymphoma versus follicular lymphoma classification (p > 0.05). These results were obtained using a multimodal model that integrated anterior and lateral maximum intensity projection (MIP) images with clinical data. Conclusions: This study demonstrates the potential of a deep learning-based approach for lymphoma subtype classification using non-invasive ¹⁸F-FDG PET imaging combined with clinical data. While further validation in larger, more diverse cohorts is necessary to address the challenges of rare subtypes and biological heterogeneity, LymphoMAP serves as a meaningful step toward developing assistive tools for early clinical decision-making. These findings underscore the feasibility of using automated pipelines to support, rather than replace, conventional diagnostic workflows in personalized lymphoma management. Full article

Positron Emission Tomography (PET) is undergoing a profound transformation. Driven by the convergence of highly sensitive long-axial field-of-view (LAFOV) total-body PET systems and an expanding portfolio of targeted radiopharmaceuticals, PET is progressively evolving beyond its traditional role in oncologic diagnosis and staging. Ultra-sensitive scanners enable whole-body imaging with markedly reduced radiotracer doses, rapid acquisition times, and true dynamic multiparametric imaging across all organs simultaneously. In parallel, molecularly targeted radioligands support tumour phenotyping, theranostic applications, and personalized dosimetry. Together, these advances position PET as a systemic imaging platform capable of interrogating whole-body tumour biology, guiding precision therapies, and potentially enabling early detection or surveillance strategies in selected high-risk populations. This narrative review summarizes the technological foundations of total-body PET, reviews current clinical and translational applications, discusses opportunities and limitations for early detection and surveillance, and outlines a research and implementation roadmap to responsibly translate this paradigm into clinical oncology. Full article

Background: This study examines how national innovation performance shapes the diffusion of advanced diagnostic technologies across European healthcare systems. Strengthening technological capacity through innovation is increasingly essential for resilient and efficient health services. The analysis quantifies the influence of innovation capacity on the availability of medical imaging technologies in 26 EU Member States between 2018 and 2024. Methods: A balanced panel dataset was assembled from Eurostat, the European Innovation Scoreboard, and World Bank indicators. Dynamic relationships between innovation performance and the adoption of CT, MRI, gamma cameras, and PET scanners were estimated using a two-step approach combining General-to-Specific (GETS) outlier detection with Robust Least Squares regression to address heterogeneity and specification uncertainty. Results: Higher innovation scores significantly increase the diffusion of R&D-intensive technologies such as MRI and PET, while CT availability shows limited responsiveness due to market maturity. Public health expenditure supports frontier technologies when strategically targeted, whereas GDP growth has no significant effect. Population size consistently enhances technological capacity through scale and system-integration effects. Conclusions: The findings show that innovation ecosystems, rather than economic growth alone, drive the modernization of diagnostic infrastructure in the EU. Integrating innovation metrics into health-technology assessments offers a more accurate basis for designing innovation-oriented investment policies in European healthcare. Full article

Background: The early and accurate identification of Alzheimer’s disease (AD) is complicated by a number of factors, such as the diversity of imaging modalities, variability in scanners across multiple sites, and the long-term progression of neurodegeneration. Such modest gains and the range of diagnostic scenarios suggest that robust multimodal applications, which incorporate both structural, molecular, and longitudinal measurements, are required if realistic benefits are to be seen in actual clinical settings. Methods: We introduce a multimodal self-supervised learning (SSL) approach, which learns feature representations of MRI and PET jointly using the cross-modal alignment, longitudinal temporal consistency, and domain-invariant embedding optimization. The approach integrates contrastive learning, scanner harmonization strategies, and missing modality-aware fusion for handling real-world cohort diversity. Six widely used datasets were evaluated, which are made publicly available: ADNI, OASIS-3, AIBL, BioFINDER, TADPOLE, and MIRIAD. Results: The model performed in a state-of-the-art way on all benchmark tasks. On ADNI, it obtained a BACC of 93.0% and an AUC of 0.96 for the binary classification task (AD vs. CN), surpassing recent baselines such as DiaMond’25, SMoCo, and AnatCL with statistically significant performance gain. Strong cross-cohort generalizability was reported (78.0% BACC on OASIS-3 and 77.5% BACC on AIBL). For TADPOLE, for longitudinal prognosis (i.e., MCI → AD conversion), the model yielded an AUC of 0.85 and a C-index of 0.82, which shows better ascendency over previous SSL-based methods. High test–retest consistency was observed on MIRIAD (ICC = 0.91), indicating that instability in volume measurement due to atrophy progression was minimal. Conclusions: The proposed multimodal SSL framework offers effective transferable and domain-robust biomarkers for the early diagnosis of AD and prediction of MCI-to-AD progression. It has strong cross-dataset generalization. Full article

This article presents a comprehensive overview of the current and emerging roles of cryogenics and superconductivity in medical diagnostics, imaging, and therapy. Beginning with the historical foundations of both fields and their technological maturation, this review emphasizes how cryogenic engineering and superconducting materials have become indispensable to modern medical systems. Cryogenic technologies are highlighted in applications such as cryosurgery, cryotherapy, cryostimulation, and cryopreservation, all of which rely on controlled exposure to extremely low temperatures for therapeutic or biological preservation purposes. This article outlines the operating principles of cryomedical devices, the refrigerants and cooling methods used, and the technological barriers. This paper reviews the latest applications of superconductivity phenomena in medicine and identifies those that could be used in the future. These include cryogenic therapy, radiotherapy (cyclotrons, particle accelerators, synchrotron radiation generation, isotope production, and proton and ion beam delivery), magnetic resonance imaging (MRI), nuclear magnetic resonance spectroscopy (NMR), positron emission tomography (PET), and ultra-sensitive magnetic signal transducers based on SQUIDs for detecting ultra-low bio-signals emitted by human body organs. CT, MRI/NMR, and PET features are compared using the operation principle, specific applications, safety, contraindications for patients, examination time, and additional valued peculiarities. This article outlines the prospects for the development of superconducting and cryogenic materials and technologies in medical applications. Advances in diagnostic imaging are reviewed, with particular attention on the progression from conventional MRI scanners to ultra-high-field (UHF) systems exceeding 7–10.5 T, culminating in the 11.7 T Iseult whole-body MRI magnet. Another important application area described in this article includes biofunctionalized magnetic nanoparticles and superconducting quantum interference devices (SQUIDs), which enable the ultrasensitive detection of biomagnetic fields and targeted cancer diagnostics. Finally, this article identifies future directions of development in superconducting and cryogenic technologies for medicine. Full article

Positron emission tomography/computed tomography (PET/CT) scanners equipped with silicon photomultiplier detectors offer superior sensitivity and count-rate performance. The aim of this study was to evaluate the feasibility and impact of shortening the acquisition duration in brain tumor ¹¹C-methionine PET using a silicon photomultiplier PET/CT system, and to assess how point spread function (PSF) correction influences quantitative values. In the phantom study, a brain tumor phantom was scanned using the Biograph Vision silicon photomultiplier-based PET/CT system. Data were acquired for 10, 5, 3, and 1 min, and the images were reconstructed with and without PSF correction. In the clinical study, 20 patients who underwent ¹¹C-methionine PET were retrospectively analyzed. PET data were acquired over 10 min and subsequently reconstructed for 10, 5, and 3 min. We evaluated quantitative parameters including the maximum standardized uptake value (SUVmax), and their relative errors under shortened acquisition durations were analyzed. In the phantom study, the SUVmax increased with shorter acquisition durations; however, this increase was less pronounced with PSF correction. In the clinical study, relative errors of SUVmax for the 5 and 3 min acquisitions with PSF correction were 2.9 ± 3.8% and 5.2 ± 5.4%, respectively. They were smaller than those without PSF correction (5.5 ± 5.1% and 12.7 ± 8.5%), indicating superior quantitative stability with shortened acquisition duration. The combination of the Biograph Vision system and PSF correction enabled the acquisition of high-quality PET images with shortened scan times. Full article

Background: Dynamic whole-body (D-WB) FDG PET/CT is a novel technique that enables the direct reconstruction of multiparametric images representing the FDG metabolic uptake rate (MR_FDG) and “free” FDG (DV_FDG). Applying complementary parameters with distinct characteristics compared to static SUV images, the aims of this study are as follows: (1) to determine the threshold values of SUV, MR_FDG, and DV_FDG for malignant and benign lesions; (2) to compare the specificity of MR_FDG and DV_FDG images with static SUV_bw images; and (3) to assess whether any of the dynamic imaging parameters correlate more significantly with malignancy or non-malignancy in the examined lesions based on the measured values obtained from D-WB FDG PET/CT. Methods: The study was a retrospective analysis of D-WB PET/CT data from 43 patients (23 males and 20 females) included both in the context of primary staging as well as imaging performed due to suspicion of post-therapeutic relapse or recurrence. Standard scanning was performed using a multiparametric PET acquisition protocol on a Siemens Biograph Vision 600 PET/CT scanner. Pathological findings were manually delineated, and values for SUV_bw, MR_FDG, and DV_FDG were extracted. The findings were classified and statistically evaluated based on their was histological verification of a malignant or benign lesion. Multinomial and binomial logistic regression analyses were used to find parameters for data classification in different models, employing various combinations of the input data (SUV_bw, MR_FDG, DV_FDG). ROC curves were generated by changing the threshold p-value in the regression models to compare the models and determine the optimal thresholds. Results: Patlak PET parameters (MR_FDG and DV_FDG) combined with mean SUV_bw achieved the highest diagnostic accuracy of 0.82 (95% CI 0.75–0.89) for malignancy detection (F1-score = 0.90). Sensitivity reached 0.85 (95% CI 0.77–0.91) and specificity 0.93 (95% CI 0.87–0.98). Classification accuracy in tumors was 0.86 (95% CI 0.78–0.92) and in lymph nodes 0.81 (95% CI 0.73–0.88). Relative contribution analysis showed that DV_FDG accounted for up to 65% of the classification weight. ROC analysis demonstrated AUC values above 0.8 for all models, with optimal thresholds achieving sensitivities of around 0.85 and specificities up to 0.93. Thresholds for malignancy detection were, for mean values, SUV_bw > 5.8 g/mL, MR_FDG > 0.05 µmol/mL/min, DV_FDG > 68%, and, for maximal values, SUV_bw > 8.7 g/mL, MR_FDG > 0.11 µmol/mL/min, DV_FDG > 202%. Conclusions: The D-WB [¹⁸F]FDG PET/CT images in this study highlight the potential for improved differentiation between malignant and benign lesions compared to conventional SUV_bw imaging in patients with locally advanced head and neck cancers presenting with cervical lymphadenopathy and carcinoma of unknown primary origin (CUP). This observation may be particularly relevant in common diagnostic dilemmas, especially in distinguishing residual or recurrent tumors from post-radiotherapy changes. Further validation in larger cohorts with histopathological confirmation is warranted, as the small sample size in this study may limit the generalizability of the findings. Full article

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments. Full article

This paper presents the development of two mobile brain Positron Emission Tomography (PET) scanners under the PETITION project, designed for Intensive Care Units (ICUs) and Proton Beam Therapy (PBT) applications. The ICU scanner facilitates bedside imaging for critically ill patients, while the PBT scanner enables undisturbed proton beam irradiation during imaging. Key aspects of the hardware design, including modular detectors and electromagnetic interference considerations, are discussed along with preliminary performance evaluations. Operational testing, employing a ²²Na source and a hot-rod phantom, was conducted to determine the timing resolution (548 $\mathrm{p}$$\mathrm{s}$), energy resolution (11.4%) and a qualitative spatial resolution (around 2.2 $\mathrm{m}$$\mathrm{m}$). Our study presents findings on the ICU PET scanner’s electromagnetic emissions measured in a controlled EMC testing facility, where all the emissions tests performed comply with the standard EN 60601-1-2 (radiated emissions 15 dB below regulatory limits in the frequency range of 30 $\mathrm{M}$$\mathrm{Hz}$ to 1 G$\mathrm{Hz}$). Full article

Polymerization initiators are commonly used to lower the processing temperatures and accelerate the synthesis of plastic scintillators. However, these additives can reduce light output. Since plastic scintillator tiles, fibers, and bars are used in countless radiation detection instruments, from PET scanners to LHC calorimeters, any loss in light output immediately degrades the timing and energy resolution of the whole system. Understanding how the initiators alter scintillation performance is therefore important. In this study, five different plastic scintillator samples were produced with varying concentrations of two initiators, 2,2-Azobis(2-methylpropionitrile) (AIBN) and benzoyl peroxide (BPO), along with a reference sample containing no initiators. The relative light yield (RLY) was measured using four different gamma sources. Analyzing the Compton edges revealed that higher initiator concentrations consistently decrease the light output. This study shows that keeping the initiator concentration at 0.2% limits the reduction to 8%, whereas 0.5–1% loadings can lower the yield by 20–35%, providing realistic bounds on initiator levels for future plastic scintillator productions. Full article

Manual calibration of nuclear medicine scanners currently relies on handling phantoms containing radioactive sources, exposing personnel to high radiation doses and elevating cancer risk. We designed an automated detection framework for robotic inspection on the YOLOv8n foundation. It pairs a lightweight backbone with a shape-aware geometric attention module and an anchor-free head. Facing a small training set, we produced extra images with a GAN and then fine-tuned a pretrained network on these augmented data. Evaluations on a custom dataset consisting of PET/CT gantry and table images showed that the SAM-YOLOv8n model achieved a precision of 93.6% and a recall of 92.8%. These results demonstrate fast, accurate, real-time detection, offering a safer and more efficient alternative to manual calibration of nuclear medicine equipment. Full article

Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain images to estimate scanner-specific Gaussian smoothing filters, optimizing parameters via the structural similarity index (SSIM). Validation was performed using images from cognitively normal individuals and Alzheimer’s disease patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results demonstrated robust harmonization at moderate target resolutions (8 and 10 mm FWHM), with filter estimates consistently within 1.2 mm of phantom-derived ground truths. However, at higher resolutions (6 mm FWHM), discrepancies reached up to 3 mm, reflecting reduced accuracy. These deviations were particularly evident for high-resolution scanners like HRRT, likely due to elevated noise levels and smaller sample sizes. The presented harmonization method effectively reduces inter-scanner variability in retrospective FDG-PET studies, especially valuable when phantom scans are unavailable. Nonetheless, the current limitations at finer resolutions underline the necessity for methodological refinements to meet the demands of evolving high-resolution PET imaging technologies. Full article

Background: The recent introduction of whole-body positron emission tomography/ computed tomography (PET/CT) scanners and multi-bed, multi-time point acquisition technique enable calculating fluorodeoxyglucose (FDG) kinetics in the whole body. However, validating parametric, Patlak-derived data is difficult on phantoms. Methods: This prospective study investigated the effect of quantification methods mean, max, and peak on the metabolic rate (MR-FDG) and distribution volume (DV-FDG) quantification, as well as the diagnostic accuracy of parametric Patlak FDG-PET scans in diagnosing lung lesions and lymph node metastases, using histopathology and follow-up as reference standards. Dynamic whole-body FDG PET was acquired for 80 minutes in 34 patients with indeterminate lung lesions and kinetic parameters extracted from lung lesions and representative mediastinal and hilar lymph nodes. Results: All quantification methods—mean, max, and peak—demonstrated high diagnostic accuracy (AUC: MR-FDG: 0.987–0.991 and 0.893–0.905; DV-FDG: 0.948–0.975 and 0.812–0.825) for differentiating benign from malignant lymph nodes and lung lesions. Differences in the magnitude of MR-FDG (−4.76–14.09) and DV-FDG (−10.64–46.10%) were substantial across methods. Variability was more pronounced in lymph nodes (MR-FDG: 1.37–3.48) than in lung lesions (MR-FDG: 3.31–5.04). The variability was lowest between mean and max quantification, with percentage differences of 40.87 ± 5.69% for MR-FDG and 39.26 ± 7.68% for DV-FDG. Conclusions: The choice of method to measure MR-FDG and DV-FDG greatly influences the results, especially in smaller lesions with large and systematic differences. For lung lesions, a conversion factor between mean and max methods of 40% provides acceptable agreement, facilitating retrospective comparisons of measurements, e.g., in meta-analyses. Full article

Objectives: This study aimed to evaluate the consistency of Centiloid (CL) values calculated using three commercially available software platforms: BTXBrain (v1.1.2), MIM (v7.3.7), and SCALE PET (v2.0.1). Methods: A total of 239 patients who underwent amyloid PET/CT with either F-18 flutemetamol (FMM) or F-18 florbetaben (FBB) were retrospectively analyzed. CL values were calculated using BTXBrain, MIM, and SCALE PET. Linear regression, Passing–Bablok regression, and Bland–Altman analysis were performed to assess the agreement between CL values. Subgroup analyses were conducted for each radiotracer. CL values were compared according to visual interpretation status. Results: Strong correlations were observed between CL values derived from the three software platforms (R² > 0.95). However, Passing–Bablok regression revealed significant proportional bias, with CL values from BTXBrain being lower than others, and CL values from SCALE PET being higher than others as CL values increased. Bland–Altman plots visualized the proportional bias, particularly between BTXBrain and SCALE PET. Subgroup analyses by radiotracer showed similar results. CL values in visually positive scans were significantly higher than those in visually negative scans across all platforms. Conclusions: The three commercial software programs demonstrated high consistency in CL quantification. However, a notable systematic bias was observed. Further evaluation of various scanner effects and CL calculation methods is warranted to improve the consistency and reproducibility of CL quantification in clinical practice. Full article