EJNMMI Physics最新文献

{"title":"Do cell culturing influence the radiosensitizing effect of gold nanoparticles: a Monte Carlo study.","authors":"J Antunes, T Pinheiro, I Marques, S Pires, M Filomena Botelho, J M Sampaio, A Belchior","doi":"10.1186/s40658-025-00746-3","DOIUrl":"https://doi.org/10.1186/s40658-025-00746-3","url":null,"abstract":"<p><strong>Background: </strong>Cell culture can be categorized into two major types: adherent and suspension. Both are used in a range of diverse research applications, exhibiting Pros and Cons, depending on what is being studied. In the field of Internal Emitters (IE), different morphological features such as nuclei size, cytoplasm ratio, and shape could influence its non-uniformity deposition and thus impact on the biological outcome. In this work we tested the hypothesis that cellular morphology differences, offered by adherent and suspension cultures, influence the radiosensitizing effect of gold nanoparticles (AuNPs).</p><p><strong>Methods: </strong>Using two PC3 cellular models, taken using confocal microscopy, we conducted Monte Carlo simulations to investigate the effects of different irradiation conditions on cellular Survival Fractions (SF). Our simulations focused on cells exposed to two distinct irradiation sources: ⁶⁰Co and 14 MeV protons, along both the longer and shorter axes of the cells to assess directional influences on cell survival. Additionally, we compared the SF of cells adherent to the culture flask with those in suspension, reflecting different experimental and potentially clinical scenarios.</p><p><strong>Results: </strong>In the absence of AuNPs, neither cell type nor irradiation direction significantly affected SF for the radiation types tested. However, with AuNPs present, SF demonstrated a strong dependence on irradiation direction and cell morphology.</p><p><strong>Conclusions: </strong>Our results indicate that the direction of irradiation plays a crucial role in determining the effectiveness of AuNPs in reducing SF. Furthermore, the results suggest that using cells in suspension will reduce the dependence of cell survival on the beam direction during irradiation, regardless of the radiation quality used.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"41"},"PeriodicalIF":3.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness of textural analysis features in quantitative ^{99 m}Tc and ¹⁷⁷Lu SPECT-CT phantom acquisitions.","authors":"Alastair J Gemmell, Colin M Brown, Surajit Ray, Alexander Small","doi":"10.1186/s40658-025-00749-0","DOIUrl":"https://doi.org/10.1186/s40658-025-00749-0","url":null,"abstract":"<p><strong>Background: </strong>Textural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. Recent EANM and SNMMI guidelines for radiomics provide advice on the potential use of phantoms to identify robust features (Hatt in EJNMMI, 2022). This study applies the suggested phantoms to use in SPECT quantification for two radionuclides, ^{99 m}Tc and ¹⁷⁷Lu.</p><p><strong>Methods: </strong>Acquisitions were made with a uniform phantom to test volume dependency and with a customised 'Revolver' phantom, based on the PET phantom described in Hatt (EJNMMI, 2022) but with local adaptations for SPECT. Each phantom was filled separately with ^{99 m}Tc and ¹⁷⁷Lu. Sixty-seven Textural Analysis features were extracted and tested for robustness and volume dependency.</p><p><strong>Results: </strong>Features showing high volume dependency or high Coefficient of Variation (indicating poor repeatability) were removed from the list of features that may be suitable for use in clinical studies. After feature reduction, there were 39 features for ^{99 m}Tc and 33 features for ¹⁷⁷Lu remaining.</p><p><strong>Conclusion: </strong>The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using ^{99 m}Tc or ¹⁷⁷Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"40"},"PeriodicalIF":3.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of ¹⁸F-FDG PET image quality and quantitative parameters between DPR and OSEM reconstruction algorithm in patients with lung cancer.","authors":"Ziyi Zhang, Wei Han, Zhehao Lyu, Hongyue Zhao, Xi Wang, Xinyue Zhang, Zeyu Wang, Peng Fu, Changjiu Zhao","doi":"10.1186/s40658-025-00748-1","DOIUrl":"https://doi.org/10.1186/s40658-025-00748-1","url":null,"abstract":"<p><strong>Objectives: </strong>The present study aimed to investigate the influence of the deep progressive learning reconstruction (DPR) algorithm on the ¹⁸F-FDG PET image quality and quantitative parameters.</p><p><strong>Methods: </strong>In this retrospective study, data were collected from 55 healthy individuals and 184 patients with primary malignant pulmonary tumors who underwent ¹⁸F-FDG PET/CT examinations. PET data were reconstructed using the ordered subset expectation maximization (OSEM) and DPR algorithms. The influence of DPR algorithm on quantitative parameters was explored, including the SUV_max, SUV_mean, standard deviation of SUV (SUV_SD), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-background uptake ratio (TBR). Finally, the differences in image quality parameters, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two reconstruction algorithms were evaluated.</p><p><strong>Results: </strong>DPR algorithm significantly reduced the SUV_max and SUV_SD of background tissues (all, P < 0.001) compared to OSEM algorithm, while no statistical difference was observed in SUV_mean between the two algorithms (all, P > 0.05). DPR algorithm notably increased the SUV_max, SUV_mean, and TBR of lesions (all, P < 0.001) and reduced MTV (P = 0.005), with minimal differences in TLG noted between the reconstruction algorithms (P < 0.001). The percentage differences in SUV_max (P = 0.001), SUV_mean (P = 0.005), and TBR (P = 0.001) between the two algorithms were significantly higher in solid nodules than in pure ground glass nodules (pGGNs). The ΔCNR between solid nodules (P = 0.031) and mixed ground glass nodules (P = 0.020) was greater than that between pGGNs. SNR and CNR obtained using the DPR algorithm were markedly improved compared to those determined using the OSEM algorithm (all, P < 0.001).</p><p><strong>Conclusion: </strong>Under identical acquisition conditions, the DPR algorithm enhanced the accuracy of quantitative parameters in pulmonary lesions and potentially improved lesion detectability. The DPR algorithm increased image SNR and CNR compared to those obtained using the OSEM algorithm, significantly optimizing overall image quality. This advancement facilitated precise clinical diagnosis, underpinning its potential to significantly contribute to the field of medical imaging.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"39"},"PeriodicalIF":3.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data.","authors":"Anja Braune, René Hosch, David Kersting, Juliane Müller, Frank Hofheinz, Ken Herrmann, Felix Nensa, Jörg Kotzerke, Robert Seifert","doi":"10.1186/s40658-025-00745-4","DOIUrl":"https://doi.org/10.1186/s40658-025-00745-4","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;A reduction of dose and/or acquisition duration of PET examinations is desirable in terms of radiation protection, patient comfort and throughput, but leads to decreased image quality due to poorer image statistics. Recently, different deep-learning based methods have been proposed to improve image quality of low-count PET images. For example, one such approach allows the generation of AI-enhanced PET images (AI-PET) based on ultra-low count PET/CT scans. The performance of this algorithm has so far only been clinically evaluated on patient data featuring limited scan statistics and unknown actual activity concentration. Therefore, this study investigates the performance of this deep-learning algorithm using PET measurements of a phantom resembling different lesion sizes and count statistics (from ultra-low to high) to understand the capabilities and limitations of AI-based post processing for improved image quality in ultra-low count PET imaging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A previously trained pix2pixHD Generative Adversarial Network was evaluated. To this end, a NEMA PET body phantom filled with two sphere-to-background activity concentration ratios (4:1 and 10:1) and two attenuation scenarios to investigate the effects of obese patients was scanned in list mode. Images were reconstructed with 13 different acquisition durations ranging from 5 s up to 900 s. Image noise, recovery coefficients, SUV-differences, image quality measurement metrics such as the Structural Similarity Index Metric, and the contrast-to-noise-ratio were assessed. In addition, the benefits of the deep-learning network over Gaussian smoothing were investigated.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The presented AI-algorithm is very well suitable for denoising ultra-low count PET images and for restoring structural information, but increases image noise in ultra-high count PET scans. The generated AI-PET scans strongly underestimate SUV especially in small lesions with a diameter ≤ 17 mm, while quantitative measures of large lesions ≥ 37 mm in diameter were accurately recovered. In ultra-low count or low contrast images, the AI algorithm might not be able to recognize small lesions ≤ 13 mm in diameter. In comparison to standardized image post-processing using a Gaussian filter, the deep-learning network is better suited to improve image quality, but at the same time degrades SUV accuracy to a greater extent than post-filtering and quantitative SUV accuracy varies for different lesion sizes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Phantom-based validation of AI-based algorithms allows for a detailed assessment of the performance, limitations, and generalizability of deep-learning based algorithms for PET image enhancement. Here it was confirmed that the AI-based approach performs very well in denoising ultra-low count PET images and outperforms traditional Gaussian post-filtering. However, there are strong limitations in terms of quantitative accur","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"38"},"PeriodicalIF":3.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-linear mixed-effects modelling and population-based model selection for ¹³¹I kinetics in benign thyroid disease.","authors":"Deni Hardiansyah, Ade Riana, Heribert Hänscheid, Ambros J Beer, Michael Lassmann, Gerhard Glatting","doi":"10.1186/s40658-025-00735-6","DOIUrl":"10.1186/s40658-025-00735-6","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to determine a mathematical model for accurately calculating time-integrated activities (TIAs) of target tissue in ¹³¹I therapy for benign thyroid disease using the population-based model selection and non-linear mixed-effects (PBMS-NLME) method.</p><p><strong>Methods: </strong>Biokinetic data of ¹³¹I in target tissue were collected from seventy-three patients at 2, 6, 24, 48, and 96 (N = 53) or 120 (N = 20) h after oral capsule administration with 1 MBq ¹³¹I. Based on the Akaike weight, the best sum-of-exponential function (SOEF) describing the biokinetic data was selected using PBMS-NLME modelling. Nine SOEF with three to six parameters (including the function from the European Association of Nuclear Medicine Standard Operational Procedure (EANM SOP)) were used. The fittings were repeated 1000 times with different starting values of the SOE parameters to find the optimal fit. Akaike weight was used to identify the performance of the best model from PBMS-NLME and the EANM SOP SOE function with individual fitting.</p><p><strong>Results: </strong>Based on the PBMS-NLME analysis, the SOEF <math> <mrow> <mfrac><msub><mi>λ</mi> <mn>1</mn></msub> <mrow><msub><mi>λ</mi> <mn>2</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mn>1</mn></msub> <mo>-</mo> <msub><mi>λ</mi> <mn>3</mn></msub> </mrow> </mfrac> <mfenced> <mrow><msup><mi>e</mi> <mrow><mo>-</mo> <mfenced> <mrow><msub><mi>λ</mi> <mn>3</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mrow><mi>phys</mi></mrow> </msub> </mrow> </mfenced> <mi>t</mi></mrow> </msup> <mo>-</mo> <msup><mi>e</mi> <mrow><mo>-</mo> <mfenced> <mrow><msub><mi>λ</mi> <mn>1</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mn>2</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mrow><mi>phys</mi></mrow> </msub> </mrow> </mfenced> <mi>t</mi></mrow> </msup> </mrow> </mfenced> <mo>+</mo> <msub><mi>a</mi> <mn>1</mn></msub> <msup><mi>e</mi> <mrow><mo>-</mo> <mfenced> <mrow><msub><mi>λ</mi> <mn>1</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mn>2</mn></msub> <mo>+</mo> <msub><mi>λ</mi> <mrow><mi>phys</mi></mrow> </msub> </mrow> </mfenced> <mi>t</mi></mrow> </msup> </mrow> </math> was selected as the function most supported by the data. The Akaike weight of the best function was approximately 100%. The best SOEF from the PBMS-NLME approach shows a better performance in describing the biokinetic data of ¹³¹I in the thyroid gland than the function from the EANM SOP with individual fitting, based on the Akaike weight.</p><p><strong>Conclusions: </strong>The best mathematical model from the PBMS-NLME approach has one more free parameter than the EANM SOP function, which could lead to more accurate TIAs.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"37"},"PeriodicalIF":3.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-source phantom with dedicated in-house software for image quality assurance in hybrid PET systems.","authors":"Carmen Salvador-Ribés, Carina Soler-Pons, María Jesús Sánchez-García, Tobias Fechter, Consuelo Olivas, Irene Torres-Espallardo, José Pérez-Calatayud, Dimos Baltas, Michael Mix, Luis Martí-Bonmatí, Montserrat Carles","doi":"10.1186/s40658-025-00741-8","DOIUrl":"10.1186/s40658-025-00741-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patients' diagnosis, treatment and follow-up increasingly rely on multimodality imaging. One of the main limitations for the optimal implementation of hybrid systems in clinical practice is the time and expertise required for applying standardized protocols for equipment quality assurance (QA). Experimental phantoms are commonly used for this purpose, but they are often limited to a single modality and single quality parameter, lacking automated analysis capabilities. In this study, we developed a multimodal 3D-printed phantom and software for QA in positron emission tomography (PET) hybrid systems, with computed tomography (CT) or magnetic resonance (MR), by assessing signal, spatial resolution, radiomic features, co-registration and geometric distortions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Phantom models and Python software for the proposed QA are available to download, and a user-friendly plugin compatible with the open-source 3D-Slicer software has been developed. The QA viability was proved by characterizing a Philips-Gemini-TF64-PET/CT in terms of signal response (mean, µ), intrinsic variability for three consecutive measurements (daily variation coefficient, CoV&lt;sub&gt;d&lt;/sub&gt;) and reproducibility over time (variation coefficient across 5 months, CoV&lt;sub&gt;m&lt;/sub&gt;). For this system, averaged recovery coefficient for activity concentration was µ = 0.90 ± 0.08 (CoV&lt;sub&gt;d&lt;/sub&gt; = 0.6%, CoV&lt;sub&gt;m&lt;/sub&gt; = 9%) in volumes ranging from 7 to 42 ml. CT calibration-curve averaged over time was &lt;math&gt;&lt;mrow&gt;&lt;mtext&gt;HU&lt;/mtext&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;mn&gt;951&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;12&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mtext&gt;density&lt;/mtext&gt; &lt;mo&gt;-&lt;/mo&gt; &lt;mo&gt;(&lt;/mo&gt; &lt;mn&gt;944&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;15&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/math&gt; with variability of slope and y-intercept of (CoV&lt;sub&gt;d&lt;/sub&gt; = 0.4%, CoV&lt;sub&gt;m&lt;/sub&gt; = 1.2%) and (CoV&lt;sub&gt;d&lt;/sub&gt; = 0.4%, CoV&lt;sub&gt;m&lt;/sub&gt; = 1.6%), respectively. Radiomics reproducibility resulted in (CoV&lt;sub&gt;d&lt;/sub&gt; = 18%, CoV&lt;sub&gt;m&lt;/sub&gt; = 30%) for PET and (CoV&lt;sub&gt;d&lt;/sub&gt; = 15%, CoV&lt;sub&gt;m&lt;/sub&gt; = 22%) for CT. Co-registration was assessed by Dice-Similarity-Coefficient (DSC) along 37.8 cm in superior-inferior (z) direction (well registered if DSC ≥ 0.91 and Δz ≤ 2 mm), resulting in 3/7 days well co-registered. Applicability to other scanners was additionally proved with Philips-Vereos-PET/CT (V), Siemens-Biograph-Vison-600-PET/CT (S) and GE-SIGNA-PET/MR (G). PET concentration accuracy was (µ = 0.86, CoV&lt;sub&gt;d&lt;/sub&gt; = 0.3%) for V, (µ = 0.87, CoV&lt;sub&gt;d&lt;/sub&gt; = 0.8%) for S, and (µ = 1.10, CoV&lt;sub&gt;d&lt;/sub&gt; = 0.34%) for G. MR(T2) was well co-registered with PET in 3/4 cases, did not show significant distortion within a transaxial diameter of 27.8 cm and along 37 cm in z, and its radiomic variability was CoV&lt;sub&gt;d&lt;/sub&gt; = 13%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Open-source QA protocol for PET hybrid systems has been presented and its general applicability has been proved. This package facilitates simultaneously simple and semi-a","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"35"},"PeriodicalIF":3.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes.","authors":"Dongyang Du, Isaac Shiri, Fereshteh Yousefirizi, Mohammad R Salmanpour, Jieqin Lv, Huiqin Wu, Wentao Zhu, Habib Zaidi, Lijun Lu, Arman Rahmim","doi":"10.1186/s40658-025-00750-7","DOIUrl":"10.1186/s40658-025-00750-7","url":null,"abstract":"<p><strong>Background: </strong>Medical imaging data frequently encounter image-generation heterogeneity and class imbalance properties, challenging strong generalized predictive performances with data-driven machine-learning methods. The purpose of this study was to investigate the impact of harmonization and oversampling methods on multi-center imbalanced datasets, with specific application to PET-based radiomics modeling for histologic subtype prediction in non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>The retrospective study included 245 patients with adenocarcinoma (ADC) and 78 patients with squamous cell carcinoma (SCC) from 4 centers. Utilizing 1502 radiomics features per patient, we trained, validated, and tested 4 machine-learning classifiers, to investigate the effect of no harmonization (NoH) or 4 feature harmonization methods, paired with no oversampling (NoO) or 5 oversampling methods on subtype prediction. Model performance was evaluated using the average area under the ROC curve (AUROC) and G-mean via 5 times 5-fold cross-validations. Statistical comparisons of the combined models against baseline (NoH + NoO) were performed for each fold of cross-validation using the DeLong test.</p><p><strong>Results: </strong>The number of cross-combinations with both AUROC and G-mean outperforming baseline in validation and testing was 15, 4, 2, and 7 (out of 29) for random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM), respectively. ComBat harmonization combined with oversampling (SMOTE) via RF yielded better performance than baseline (AUROC and G-mean of validation: 0.725 vs. 0.608 and 0.625 vs. 0.398; testing: 0.637 vs. 0.567 and 0.506 vs. 0.287), though statistical significances were not observed.</p><p><strong>Conclusions: </strong>Applying harmonization and oversampling methods in multi-center imbalanced datasets can improve NSCLC-subtype prediction, but the effect varies widely across classifiers. We have created open-source comparisons of harmonization and oversampling on different classifiers for comprehensive evaluations in different studies.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"34"},"PeriodicalIF":3.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deriving tissue physical densities based on Dixon magnetic resonance images and tissue composition prior knowledge for voxel-based internal dosimetry.","authors":"Cheng-Ting Shih, Ko-Han Lin, Bang-Hung Yang, Chien-Ying Li, Tzu-Lin Lin, Greta S P Mok, Tung-Hsin Wu","doi":"10.1186/s40658-025-00737-4","DOIUrl":"10.1186/s40658-025-00737-4","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance (MR) images have been applied in diagnostic and therapeutic nuclear medicine to improve the visualization and characterization of soft tissues and tumors. However, the physical density (ρ) and elemental composition of human tissues required for dosimetric calculation cannot be directly converted from MR images, obstructing MR-based personalized internal dosimetry. In this study, we proposed a method to derive physical densities from Dixon MR images for voxel-based internal dose calculation.</p><p><strong>Methods: </strong>The proposed method defined human tissues as composed of four basic tissues. The physical densities of the human tissues were calculated using the standard tissue composition of the basic tissues and the volume fraction maps calculated from Dixon images. The derived ρ map was applied to calculate the whole-body internal dosimetry using a multiple voxel S-value (MSV) approach. The accuracy of the proposed method in deriving ρ and calculating the internal dose of ¹⁸F-FDG PET imaging was evaluated by comparing with those obtained from computed tomography (CT) images of the same patient and was compared with those obtained using generative adversarial networks (GANs).</p><p><strong>Results: </strong>The proposed method was superior to the GANs in deriving ρ from Dixon MR images and the following internal dose calculation. On average of a validation set, the mean absolute percent errors (MAPEs) of the whole-body ρ derivation and internal dose calculation using the proposed method were 14.28 ± 11.11% and 3.31 ± 0.69%, respectively. The MAPEs were respectively reduced to 5.97 ± 2.51 and 2.75 ± 0.69% after excluding the intestinal gas with different locations in the Dixon MR and CT images.</p><p><strong>Conclusions: </strong>The proposed method could be applied for accurate and efficient personalized internal dosimetry evaluation in MR-integrated nuclear medicine clinical applications.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"36"},"PeriodicalIF":3.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of a deep progressive reconstruction algorithm on low-dose or fast-scan PET image quality and Deauville score in patients with lymphoma.","authors":"Wenli Qiao, Taisong Wang, Hongyuan Yi, Xuebing Li, Yang Lv, Chen Xi, Runze Wu, Ying Wang, Ye Yu, Yan Xing, Jinhua Zhao","doi":"10.1186/s40658-025-00739-2","DOIUrl":"10.1186/s40658-025-00739-2","url":null,"abstract":"<p><strong>Background: </strong>A deep progressive learning method for PET image reconstruction named deep progressive reconstruction (DPR) method was developed and presented in previous works. It has been shown in previous study that the DPR with one-third duration can maintain the image quality as OSEM with standard dose (3.7 MBq/kg). Subsequent studies have shown we can reduce the administered activity of ¹⁸F-FDG by up to 2/3 in a real-world deployment with DPR. The aim of this study is to assess the impact of the use of DPR on Deauville score (DS) and clinical interpretation of PET/CT in patients with lymphoma.</p><p><strong>Methods: </strong>A total of 87 lymphoma patients (age, 45.1 ± 14.9 years) who underwent ¹⁸F-FDG PET imaging for during or post-treatment follow-up from November 2020 to February 2024 were prospectively enrolled. The patients were randomly assigned to two groups, including the 1/3 standard dose group and the standard dose group. Forty-four patients were injected with 1/3 standard dose (1.23 MBq/kg) and scanned for 6 min per bed and were reconstructed: ordered-subsets expectation maximization (OSEM) with 6 min per bed (OSEM_6 min_1/3), OSEM_2 min_1/3 and DPR_2 min_1/3. Forty-three patients were scanned according to the standard protocol (3.7 MBq/kg) and were reconstructed: OSEM with 2 min per bed (OSEM_2 min_full), OSEM_40 s_full and DPR_40 s_full. Additionally, the conventional 5-point scale measurement analysis was performed and DS for lymphoma were determined in different groups. Wilcoxon signed-rank test was used to compare the mean values of liver SUVmax and mediastinal blood pool (MBP) SUVmax in each group. Likert scale and DS were evaluated using Wilcoxon signed rank test.</p><p><strong>Results: </strong>The patients with OSEM_6 min_1/3 and DPR_2 min_1/3 showed good image quality with 5(5,5) and 5(4,5) of Likert scoring, as well as the patients with OSEM_2 min_full and DPR_40 s_full. No significant difference was found between the OSEM_6 min_1/3 and DPR_2 min_1/3 groups in terms of liver SUVmax and MBP SUVmax (P = 0.452 and 0.430), as well as the patients with OSEM_2 min_full and DPR_40 s_full (P = 0.105 and 0.638). No significant difference was found between the OSEM_6 min_1/3 and DPR_2 min_1/3 groups in terms of lesion SUVmax (P = 0.080). There was a significant differences in lesion SUVmax between OSEM-2 min_full with DPR-40 s_full (P = 0.027). The DS results were consistent (100%) between OSEM-6 min_1/3 with DPR_2 min_1/3, and between OSEM-2 min_full with DPR-40 s_full, respectively.</p><p><strong>Conclusions: </strong>DPR reconstruction demonstrated feasibility in reducing PET injection dose or scanning time, while ensuring the preservation of image quality and DS for during or post-treatment follow-up patients with lymphoma.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"33"},"PeriodicalIF":3.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in diagnostic nuclear medicine in Sweden (2008-2023): utilisation, radiation dose, and methodological insights.","authors":"Anja Almén","doi":"10.1186/s40658-025-00747-2","DOIUrl":"10.1186/s40658-025-00747-2","url":null,"abstract":"<p><strong>Background: </strong>Diagnostic imaging is a dynamic medical field. In nuclear medicine, advancements introduce new procedures utilising innovative radiopharmaceuticals. These developments may influence supply requirements and exposure levels for the patient population. Surveying the frequency of procedures, types of pharmaceuticals, and administered activities provides valuable insights into utilisation trends and radionuclide demand. This knowledge also guides the prioritisation of radiation protection efforts at national and local levels. In Europe, radiation dose assessments for medical exposures are mandatory according to the directive´s requirements.</p><p><strong>Methods: </strong>This study evaluated the utilisation of diagnostic nuclear medicine procedures in Sweden over 15 years (2008-2023), focusing on procedure frequency, effective dose, and collective effective dose. Comprehensive data from all Swedish clinics performing nuclear medicine were analysed, incorporating information on radiopharmaceuticals and administered activities. The method suggested by the UNSCEAR, which includes so-called essential procedures, was used for comparison. The study also investigated some frequent procedures in more detail.</p><p><strong>Results: </strong>The study identifies noteworthy trends, including a threefold increase in the number of clinics offering Positron Emission Tomography (PET) procedures and a significant rise in PET usage. PET procedures constituted over 50% of the collective effective dose for adults in 2023. Despite this, Gamma Camera (GC) procedures still dominate in frequency but exhibit a steady decline. Procedures using ^99mTc and ¹⁸F accounted for 93% of procedures in 2023. The collective effective dose rose 22% over the study period, with PET procedures driving this increase. PET procedures increasing role became evident by the increased contribution to the total collective dose from 15 to 52%. The UNSCEAR methodology captured 67% of the total frequency and underestimated the collective effective dose by 16%. Administered activity remained stable for the selected procedures and showed low variation between clinics.</p><p><strong>Conclusions: </strong>PET procedures are increasing in scope and now constitute the largest contribution to radiation dose, and in-house production of PET radiopharmaceuticals is available in around 40% of clinics. The number of radionuclides decreased over the study period, and GC procedures declined. In general, the amount of administered activity remained stable over the period for the procedures studied. Accurately assessing utilisation and exposure trends requires extensive data, and the methodology used affects the result significantly.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"12 1","pages":"32"},"PeriodicalIF":3.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}