Frontiers in nuclear medicine (Lausanne, Switzerland)最新文献

Population pharmacokinetic (PopPK) has emerged as a robust framework for characterizing inter-individual variability in the absorbed dose estimates in radiopharmaceutical therapy (RPT). By enabling the analysis of biokinetic data from heterogeneous patient populations, PopPK allows individualized absorbed dose estimates while simultaneously leveraging population-level information. This review presents and evaluates the current applications of PopPK, such as nonlinear mixed-effects modeling (NLMEM) and Bayesian fitting methods in RPT, emphasizing its advantages over traditional individual-based modeling approaches. We summarize key studies that have implemented PopPK for modeling radiopharmaceutical biokinetics, with a focus on time-integrated activity (TIA) estimation, including single-time-point (STP) dosimetry, uncertainty analysis, as well as pharmacodynamic (PD) analysis. The flexibility of PopPK in handling sparse and irregularly sampled data makes it particularly relevant for clinical scenarios where comprehensive imaging schedules are impractical. However, despite its potential, the widespread adoption of PopPK in RPT remains limited due to challenges such as computational complexity and the need for specialized expertise. This review discusses critical aspects of PopPK implementation while emphasizing the importance of interdisciplinary collaboration in translating PopPK methodologies into clinical practice. Future directions include integrating PopPK into adaptive dosimetry frameworks and applying it in STP dosimetry and PD modeling to optimize treatment personalization. By providing a comprehensive overview of PopPK applications in RPT, this review aims to facilitate the integration of advanced modeling techniques into routine clinical workflows, ultimately supporting the development of accurate and precise RPTs.

We investigated subset-based optimization methods for positron emission tomography (PET) image reconstruction incorporating a regularizing prior. PET reconstruction methods that use a prior, such as the relative difference prior (RDP), are of particular relevance because they are widely used in clinical practice and have been shown to outperform conventional early-stopped and post-smoothed ordered subset expectation maximization. Our study evaluated these methods using both simulated data and real brain PET scans from the 2024 PET Rapid Image Reconstruction Challenge (PETRIC), where the main objective was to achieve RDP-regularized reconstructions as fast as possible, making it an ideal benchmark. Our key finding is that incorporating the effect of the prior into the preconditioner is crucial for ensuring fast and stable convergence. In extensive simulation experiments, we compared several stochastic algorithms-including stochastic gradient descent (SGD), stochastic averaged gradient amelioré (SAGA), and stochastic variance reduced gradient (SVRG)-under various algorithmic design choices and evaluated their performance for varying count levels and regularization strengths. The results showed that SVRG and SAGA outperformed SGD, with SVRG demonstrating a slight overall advantage. The insights gained from these simulations directly contributed to the design of our submitted algorithms, which formed the basis of the winning contribution to the PETRIC 2024 challenge.

Platinum group metals (PGMs) consist of six transition metals: platinum (Pt), palladium (Pd), rhodium (Rh), osmium (Os), iridium (Ir), and ruthenium (Ru). PGMs have been used notably in industrial, electronic, and medical applications. For example, Ir-192 is often utilized in industry to detect structural defects in metal and assess pipeline integrity. Pd-104 is irradiated to produce Pd-103 seeds, used for prostate cancer treatment. Other isotopes of elements in this group can be sourced to facilitate critical applications, discussed in this review. Due to their unique chemical and nuclear properties, these metals may be promising candidates for various nuclear medicine applications, including diagnostic imaging via Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT) and Targeted Radionuclide Therapy (TRT). This review will explore PGMs in nuclear medicine, focusing on their production routes, nuclear characteristics, and suitability for past and future development of radiopharmaceuticals. We will highlight methods for radiochemical separation and purification of each radionuclide, discussing potential challenges and emphasizing the need for further research to ensure sustainability. As the demand for advanced nuclear medicine techniques continues to grow, PGMs may play a significant role in addressing current challenges in the field. We will discuss several radionuclides of interest to nuclear medicine including ¹⁹¹Pt, ^193mPt, ^195mPt, ¹⁰³Pd, ¹⁰⁹Pd, ^103mRh, ¹⁰⁵Rh, ¹⁹¹Os, ¹⁹²Ir, ⁹⁷Ru, and ¹⁰³Ru.

Background: In this study, we investigate the impact of MR-derived attenuation maps and limited detector resolution on the quantification of positron emission tomography (PET) activity uptake in the spinal cord during PET/MRI. This was performed by simulating [ ${}^{18}$ F]FDG PET data in the neck and thorax and then modifying the attenuation map to remove bone features. We then compared Ordered Subset Expectation Maximisation-reconstructed images to those with full attenuation correction. This simulation was performed at two detector resolutions of 2.1 and 4.4 mm. Acquisitions from a clinical study were then used to assess the ability of point spread function (PSF) modelling and time-of-flight (TOF) corrections, as implemented on the SIGNA PET/MR scanner (GE HealthCare), to correct for these quantification errors. For comparison, mean uptake was measured in regions of interest at each vertebral position along the spinal cord.

Results: Simulation results showed a decreasing pattern of uptake from the cervical to the thoracic spinal cord. When bone was not included in attenuation correction, the mean uptake decreased by 3%-10.4%. This difference in measured uptake was 6.4%-23.9% in images simulated at a detector resolution representative of a clinical PET/MRI scanner. At a detector resolution of 4.4 mm, a 32.2% decrease in uptake was measured compared to the 2.1 mm simulation. In patient data, introducing vertebral bone to the attenuation correction pseudo-CT led to a 1.8%-18.3% difference in ${\mathrm{SUV}}_{\mathrm{mean}}$ in the spinal cord. Applying PSF modelling did not lead to any statistically significant changes. TOF correction reduces the difference in ${\mathrm{SUV}}_{\mathrm{mean}}$ between data attenuation corrected with and without vertebral bone to 4.3%-7%. TOF Q.Clear images with beta = 100 showed the smallest difference between attenuation correction approaches at 0.6%-5.2%.

Conclusion: Ignoring bone during image reconstruction in PET/MRI reduces the activity measured during quantification of the spinal cord; however, the partial volume effect has a greater impact on reducing measured uptake in lower-resolution data. While time-of-flight correction goes somewhat resolves these quantification errors, further research is needed into partial volume correction.

Background: Animal models of nerve compression have revealed neuroinflammation not only at the entrapment site, but also remotely at the spinal cord. However, there is limited information on the presence of neuroinflammation in human compression neuropathies. The objectives of this study were to: (1) assess which tracer kinetic model most optimally quantified [¹¹C]DPA713 uptake in the spinal cord and neuroforamina in patients with painful cervical radiculopathy, (2) evaluate the performance of linearized methods (e.g., Logan) and simplified (e.g., standardized uptake value - SUV) methods, and (3) assess the test-retest reliability of these methods. Microglia activation associated with neuroinflammation was quantified using positron emission tomography (PET) with the radiotracer [¹¹C]DPA713, targeting the 18 kDa translocator protein (TSPO). The Akaike information criterion, visual inspection of the fits and number of outliers were used to select the optimal kinetic model. As unaffected tissue, the spinal cord and neuroforamina three cervical levels above the affected target tissue was used.

Results: The single tissue (1T2k) compartment model was the preferred model to describe [¹¹C]DPA713 kinetics at the spinal cord and neuroforamina. Higher levels of 1T2k V _T were observed in the affected neuroforamina and spinal cord compared with corresponding unaffected tissues. Logan V _T (≥0.73) showed high correlation with 1T2k V _T at both locations. Of the simplified methods, neuroforamina and spinal cord SUV normalized for the metabolite corrected plasma (TBR-PP) exhibited high correlations with 1T2k V _T (r ≥ 0.84). Test-retest reliability varied between fair to excellent.

Conclusions: These results indicate that a 1T2k model with metabolite corrected image derived input function can be used to describe the kinetics of [¹¹C]DPA713 in the spinal cord and neuroforamina in humans. 1T2k V _T or Logan V _T can be used as binding metric, while TBR-PP is the recommended choice among simplified models.

[This corrects the article DOI: 10.3389/fnume.2025.1632112.].

Neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms characterized by their overexpression of somatostatin receptors (SSTRs), which can be utilized for peptide receptor radionuclide therapy. This review provides a comprehensive update on the clinical trials of radiolabeled SSTR-targeting radiopharmaceuticals since 2020, with a focus on somatostatin receptor agonists and antagonists radiolabeled with ⁶⁸Ga, ¹⁸F, ^99mTc, ¹⁷⁷Lu, ¹⁶¹Tb, ²¹²Pb, ⁶⁷Cu, and ²²⁵Ac. Head-to-head clinical trials demonstrate that radiolabeled SSTR antagonists such as [⁶⁸Ga]Ga-DOTA-JR11 and [⁶⁸Ga]Ga-DOTA-LM3 offer improved lesion detection and tumor-to-background ratios (particularly in liver metastases) compared to radiolabeled agonists like [⁶⁸Ga]Ga-DOTA-TOC and [⁶⁴Cu]Cu-DOTA-TATE. Additionally, ¹⁸F-labeled agents offer logistical and dosimetric advantages over ⁶⁸Ga, due to ¹⁸F's longer half-life and cyclotron production, allowing for delayed imaging and increased availability to a wider range of patients. Emerging targeted alpha therapy agents, including [²²⁵Ac]Ac-DOTA-TATE, show promising results in treating disease resistant to conventional therapies due to the high linear energy transfer of alpha particles, which leads to improved localized cytotoxicity. Collectively, these developments support a shift toward more precise, receptor-specific theragnostics, emphasizing the need for further head-to-head clinical trials and integration of dosimetry-driven, personalized treatment planning in the management of NETs.

Radiocobalt-based theranostics has emerged as a promising platform in nuclear medicine that offers dual capabilities for both diagnostic imaging and targeted radionuclide therapy. ⁵⁵Co (t_1/2 = 17.53 h, β⁺ = 77%, E _γ  = 931.1 keV, I _γ  = 75%) and ^58mCo (t_1/2 = 9.10 h, IC = 100%) serve as an elementally matched pair for positron emission tomography and targeted Auger electron therapy, respectively, that enable a more personalized approach to cancer management, where imaging with ⁵⁵Co can help to guide and predict therapeutic outcomes for ^58mCo therapy. The unique coordination chemistry of cobalt allows for stable complexation with various chelators, enhancing in vivo stability and targeting efficacy when conjugated to biomolecules such as peptides, antibodies, and small molecules. Recent developments in radiolabeling techniques, chelator design, and preclinical evaluations have significantly improved the pharmacokinetic profiles and tumor specificity of radiocobalt-based radiopharmaceuticals. The aim of this mini review is to provide an overview of the recent advancements and applications of radiocobalt isotopes with a particular focus on the production, chelation chemistry, and in vivo targeting of ⁵⁵Co- and ^58mCo-labelled radiopharmaceuticals over the last 5 years. While challenges still exist in production scalability, dosimetry optimization, and clinical translations, the current trajectory suggests a growing role for radiocobalt-based theranostics in precision oncology.

Purpose: Long axial field-of-view (LAFOV) PET systems like the Siemens Biograph Vision Quadra offer unprecedented sensitivity and imaging capabilities, but compliance with EARL standards across all acquisition modes remains unexplored. This study aimed to identify reconstruction parameters meeting EARL 1 and 2 compliance for static and continuous bed motion (CBM) acquisitions in High Sensitivity (HS) and Ultra-High Sensitivity (UHS) modes on the Quadra. The research focused on optimising image quality while maintaining compliance with quantitative standards.

Methods: The International Electrotechnical Commission (IEC) body phantom was filled with ¹⁸F-FDG in a 10:1 sphere-to-background activity ratio and scanned at five positions across the field of view (FOV) using static and CBM acquisitions in HS and UHS modes. Reconstructions used standard clinical parameters, varied with Gaussian filters (1-7 mm) and matrix sizes (440, 220, 128). EARL compliance was assessed with the EARL tool to evaluate SUV recovery coefficients (RCSUVmean, RCSUVmax, RCSUVpeak). Patient images were reconstructed using standard and EARL-compliant parameters for comparison.

Results: Reconstruction parameters achieving EARL compliance were identified for all acquisition modes, with no differences between static and CBM reconstructions. Achieving EARL compliance required significant image quality reductions, especially for EARL 1, with greater degradation in UHS mode. Patient images reconstructed with EARL-compliant parameters appeared smoother and had reduced contrast compared to clinical reconstructions.

Conclusion: While EARL compliance ensures quantitative standardisation, it significantly reduces image quality, especially on advanced LAFOV PET systems. An updated "EARL 3" standard is needed to reflect the capabilities of modern systems.

Introduction: Ventilation-perfusion (V/Q) nuclear scintigraphy remains a vital diagnostic tool for assessing pulmonary embolism (PE) and other lung conditions. Interpretation of these images requires specific expertise which may benefit from recent advances in artificial intelligence (AI) to improve diagnostic accuracy and confidence in reporting. Our study aims to develop a multi-center dataset combining imaging and clinical reports to aid in creating AI models for PE diagnosis.

Methods: We established a comprehensive imaging registry encompassing patient-level V/Q image data along with relevant clinical reports, CTPA images, DVT ultrasound impressions, D-dimer lab tests, and thrombosis unit records. Data extraction was performed at two hospitals in Canada and at multiple sites in the United States, followed by a rigorous de-identification process. We utilized the V7 Darwin platform for crowdsourced annotation of V/Q images including segmentation of V/Q mismatched vascular defects. The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation.

Results: A query of The Ottawa Hospital's data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. Additional contributions included 600 studies from University Health Toronto, and 385 studies by private company Segmed Inc. resulting in a total of 3,122 V/Q planar and SPECT images. The majority of studies were acquired using Siemens, Philips, and GE scanners, adhering to standardized local imaging protocols. After annotating 1,500 studies from The Ottawa Hospital, the analysis identified 138 high-probability, 168 intermediate-probability, 266 low-probability, 244 very low-probability, and 669 normal, and 15 normal perfusion with reversed mismatched ventilation defect studies. In 1,500 patients were 3,511 segmented vascular perfusion defects.

Conclusion: The VQ4PEDB comprised 8 unique ventilation agents and 11 unique scanners. The VQ4PEDB database is unique in its depth and breadth in the domain of V/Q nuclear scintigraphy for PE, comprising clinical reports, imaging studies, and annotations. We share our experience in addressing challenges associated with data retrieval, de-identification, and annotation. VQ4PEDB will be a valuable resource to development and validate AI models for diagnosing PE and other pulmonary diseases.