EJNMMI Physics最新文献

{"title":"Quantitative in vivo Cherenkov luminescence imaging and dosimetry of ⁸⁶Y-NM600.","authors":"Campbell D Haasch, Malick Bio Idrissou, Sydney Jupitz, Aubrey Parks, Reinier Hernandez, Brian W Pogue, Bryan P Bednarz","doi":"10.1186/s40658-026-00836-w","DOIUrl":"10.1186/s40658-026-00836-w","url":null,"abstract":"<p><strong>Purpose: </strong>The rapid expansion of radiopharmaceutical therapy (RPT) development demands scalable preclinical dosimetry methods. While PET and SPECT remain the gold standards, their low throughput and high cost limit large-cohort studies. Cherenkov luminescence imaging (CLI) offers a high-throughput alternative but suffers from depth-dependent attenuation and photon scatter that compromise quantitative accuracy. This work develops and validates a quantitative CLI methodology incorporating attenuation and scatter corrections to enable accurate preclinical dosimetry.</p><p><strong>Methods: </strong>Depth-dependent attenuation was characterized using a tissue-mimicking phantom to derive calibration coefficients. Photon scatter was modeled using GEANT4-generated Cherenkov spread functions (CSFs), applied in a depth-weighted iterative Richardson-Lucy deconvolution/reconvolution framework. The method was evaluated in NU/NU mice (n = 4) bearing MC38 tumors after injection of ⁸⁶Y-NM600, an isotope suitable for both PET and CLI. Liver and tumor activities were quantified at four timepoints using PET and the proposed CLI method. Voxelized Monte Carlo dosimetry was performed for both modalities.</p><p><strong>Results: </strong>CLI-PET activity quantification yielded mean errors of 15.4% (liver) and 10.3% (tumor) over the first three timepoints. Tumor absorbed doses from CLI-derived synthetic PET images (3.4 ± 0.3 Gy/MBq) were statistically indistinguishable from PET-based estimates (3.2 ± 0.2 Gy/MBq, p = 0.31). Discrepancies increased at late timepoints due to low activity and background auto-luminescence.</p><p><strong>Conclusions: </strong>With appropriate depth-dependent optical attenuation calibration and Monte Carlo-derived ionizing scatter correction, CLI can provide quantitative biodistribution and dosimetry estimates comparable to PET. This approach enables high-throughput, low-cost in vivo dosimetry, expanding the feasibility of large-scale preclinical RPT studies and supporting translational radiopharmaceutical development.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13031687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131056","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":"Generative restoration for cardiac PET attenuation correction: a two-stage 3D DDIM framework optimizing fidelity and clinical controllability.","authors":"Junhang Deng, Hao Sun, Haifeng Wang, Xiaotong Hong, Weiping Xu, Fan Wang, Jianhua Ma, Chunfeng Lian, Lijun Lu","doi":"10.1186/s40658-026-00839-7","DOIUrl":"10.1186/s40658-026-00839-7","url":null,"abstract":"<p><p>PURPOSE: This study aims to develop a two-stage 3D denoising diffusion implicit model (DDIM) framework for CT-free attenuation correction in cardiac PET imaging, enabling direct generation of attenuation-corrected (AC) images from non-attenuation-corrected (NAC) PET scans. The method is comprehensively validated using both [18F]FDG PET and [13N]ammonia cardiac PET datasets to demonstrate its clinical applicability across different perfusion and metabolic imaging protocols. METHODS: The framework employs a two-stage approach: (1) a noise-to-image DDIM was first pretrained on all available AC images (i.e., no need of paired NACs) to learn a diverse AC distributions, enabling the high-fidelity generation of AC images with varying appearances; (2) the pretrained model was fine-tuned with a limited set of paired NAC-AC images to form a conditional DDIM, ensuring anatomically aligned, controllable generation. The model architecture uses a 3D U-Net, trained on 224 paired NAC-AC and 396 unpaired AC images for [18F]FDG, and 608 paired NAC-AC images and 885 unpaired AC images for [13N]ammonia. Performance was evaluated through quantitative metrics (including NMAE, NRMSE, SSIM and PSNR) and visual assessment. RESULTS: The proposed two-stage DDIM framework achieved excellent agreement with clinical CT-based attenuation correction (CT-AC), demonstrating superior correlation (slope = 0.78, [Formula: see text] = 0.95 for [18F]FDG; slope = 0.99, [Formula: see text]= 0.91 for [13N]ammonia) and lower errors compared to existing approaches. Ablation studies confirmed the benefits of both the two-stage training strategy and the incorporation of unpaired AC images, as evidenced by narrower confidence intervals in Bland-Altman analysis and reduced percentage errors. CONCLUSION: The two-stage 3D DDIM framework achieves performance comparable to clinical CT-AC while effectively leveraging unpaired data, demonstrating significant potential for robust cardiac PET attenuation correction. </p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12957678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104295","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":"Scanner-integrated reconstruction versus post-processing deep learning for low-count ¹⁸F-FDG PET/CT: a comparative clinical evaluation.","authors":"Qigang Long, Yan Tian, Yun Hu, Zhenchun Xu, Wenqian Zhang, Shanshan Xu, Wei Liu, Jingzheng Jin, Yunsong Peng","doi":"10.1186/s40658-026-00841-z","DOIUrl":"10.1186/s40658-026-00841-z","url":null,"abstract":"<p><strong>Objectives: </strong>To compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruction-level method, and a deep-learning image-domain post-processing enhancement (POST; RaDynPET).</p><p><strong>Methods: </strong>Sixty-seven patients who underwent whole-body ¹⁸F-FDG PET/CT were enrolled. PET images were reconstructed with ordered-subsets expectation maximization (OSEM) at 30/60/120 s/bed (O30, O60, O120 [clinical reference]) and with DPR at 30/60/90/120 s/bed (D30, D60, D90, D120). POST (RaDynPET) was applied to the unaltered O30 /O60 images to yield P30/P60. Two nuclear medicine physicians rated image quality using 5-point Likert scales. Liver signal-to-noise ratio (SNR), lesion tumour-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were calculated. Non-inferiority (NI) versus O120 was prespecified for overall quality (Δ = -0.5) and lesion CNR (ratio lower bound 0.90). Time-matched DPR versus POST and DL versus OSEM were also assessed. Agreement with O120 was evaluated using Lin's concordance correlation coefficient (CCC) and Bland-Altman analysis.</p><p><strong>Results: </strong>Both DPR and POST achieved higher reader scores than time-matched OSEM. Inter-reader agreement was substantial to almost perfect. POST was superior at 30 s, whereas DPR was at 60 s. D60 and P30 met both NI margins, whereas D30 failed overall quality and P60 failed CNR. Concordance with O120 was excellent by CCC, and Bland-Altman showed small biases with limited proportional effects. CNR and SNR increased monotonically with DPR, while POST yielded gains at 30 s that attenuated at 60 s. TBR improvements were confined to DPR.</p><p><strong>Conclusion: </strong>Both DPR and POST improved or preserved image quality while enabling scan-time reduction, with excellent agreement with the clinical reference. POST is supported for 1/4 acquisition time, whereas DPR is favored from 1/2 time onward.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146092386","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":"Image-space compensation of inter-crystal scattering in PET using a neural network based filter.","authors":"Dóra Varnyú, Ákos Rábely, László Szirmay-Kalos","doi":"10.1186/s40658-026-00837-9","DOIUrl":"10.1186/s40658-026-00837-9","url":null,"abstract":"<p><strong>Background: </strong>In positron emission tomography (PET), gamma photons arriving at the detector ring may undergo one or more Compton scattering events, potentially reaching a different scintillation crystal than its initial interaction point. This phenomenon, known as inter-crystal scattering (ICS), can lead to incorrect line of response (LOR) assignments, introducing spatial blurring and noise in the reconstructed image. Existing ICS correction methods include energy-based heuristics, Compton kinematics, Monte Carlo simulation, and deep learning to recover the first interaction position of the photons in the detector. These corrections are performed in the LOR domain, since that is where ICS manifests. However, in ordered-subset expectation-maximization (OS-EM) reconstruction, only a subset of LORs is processed at each iteration, making LOR-domain corrections difficult to apply on-the-fly.</p><p><strong>Methods: </strong>We propose a novel ICS correction technique performed entirely in the image domain. A neural network is trained to predict spatially-varying 3D filter kernels that model the blurring effect of ICS across the image volume. These kernels are applied to the PET image estimate prior to each OS-EM forward projection. We evaluated our method on simulated and real PET data using two network variants: one predicting full 3D kernels (ICS-Net-direct), and another using a compact skew normal representation (ICS-Net-skewnorm).</p><p><strong>Results: </strong>Both ICS-Net-direct and ICS-Net-skewnorm significantly improved the spatial resolution and the contrast of the output image, while also being computationally efficient. We found that ICS-Net-skewnorm is better suited for structural, symmetric reconstruction objects, while ICS-Net-direct performs best in complex, real-world scenarios.</p><p><strong>Conclusions: </strong>The proposed image-domain ICS correction technique enables efficient and effective compensation of inter-crystal scattering for OS-EM reconstruction. Network selection should be guided by the target imaging scenario to maximize performance.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12932763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146046238","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":"Harmonisation of standard uptake values in long-axial field-of-view PET/CT: an exploratory study.","authors":"Yue Li, Zhu Zhang, Hanyu Bai, Xinmiao Lu, Hua Pang","doi":"10.1186/s40658-025-00819-3","DOIUrl":"10.1186/s40658-025-00819-3","url":null,"abstract":"<p><strong>Purpose: </strong>SUV harmonisation was proposed to reduce discrepancies in SUV measurements and ensure SUV comparability across different PET scanners. A SUV harmonisation protocol has been established based on short-axial field-of-view (SAFOV) PET. However, long-axial field-of-view (LAFOV) PET features a different scanning mode and clinical workflow compared with SAFOV PET, and no dedicated SUV harmonisation protocol has yet been developed. This study aimed to perform an exploratory investigation of SUV harmonisation on LAFOV PET.</p><p><strong>Methods: </strong>The long-axial detector was divided into five segments based on typical patient anatomical positioning. A NEMA IEC body phantom filled with ¹⁸F-FDG solution was sequentially scanned at each detector position. SUV harmonisation was performed separately in accordance with the EARL 1 and EARL 2 standards using post-reconstruction Gaussian filters. Position-specific optimal Gaussian full width at half maximum (FWHM) values, as well as a general harmonisation filter applicable across all positions, were derived. Recovery coefficients (RCs) were determined, and root mean square error (RMSE) between the harmonised RCs and the expected EARL reference values (EARL_expect ) was used to evaluate harmonisation performance. To assess the necessity of the detector-splitting phantom scanning strategy, the optimal Gaussian filters derived from a single detector position were applied to other positions, and the corresponding RMSE values were recalculated. A clinical case was further evaluated as a proof-of-concept application.</p><p><strong>Results: </strong>The position-specific optimal harmonisation filter parameters under EARL 1 were nearly identical across all detector positions, whereas under EARL 2 differences were observed. A general Gaussian filter derived by jointly considering all detector positions was sufficient to achieve EARL-compliant SUV harmonisation across the entire axial field of view. Moreover, when optimal filters derived from a single detector position were applied to other positions, harmonisation remained compliant with EARL 1, while deviations from EARL_expect increased under EARL 2 for some cases. When the proposed harmonisation strategy was applied to a clinical case, the SUV deviation in follow-up imaging caused by a change in PET scanner was eliminated.</p><p><strong>Conclusions: </strong>The study proposes a detector-splitting phantom scanning strategy as a practical framework for robust SUV harmonisation for LAFOV PET systems. Position-specific or jointly optimized harmonisation filters applicable across all axial detector positions may be beneficial for ensuring reliable and comparable SUV measurements, particularly in longitudinal follow-up and multi-scanner clinical settings.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12946547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028942","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":"Evaluation of precalculated attenuation correction map for preclinical cardiac PET/MR using a ¹H/²³Na surface coil.","authors":"Hanan Rida, Mona Guetlin, Mikael Naveau, Martin Haas, Alexandre Lebrun, Michael Joubert, Alain Manrique","doi":"10.1186/s40658-025-00832-6","DOIUrl":"10.1186/s40658-025-00832-6","url":null,"abstract":"<p><strong>Background: </strong>In preclinical PET/MR, attenuation correction (AC) uses a mix of pre-calculated attenuation maps accounting for animal cradles and organ segmentation obtained from whole-body MR using a volume coil. The development of X-nuclei such as ²³Na or ³¹P may benefit from high sensitivity surface coils, which could lead to inaccurate PET quantification. In this study, we evaluated the benefit of a pre-calculated attenuation map including a dedicated cradle enclosing a surface coil for ¹⁸F-FDG PET imaging.</p><p><strong>Materials and methods: </strong>We developed a 3D-printed cradle (DC) embedding a 20 mm surface coil made of PLA (polylactic acid), coated with a thin cap of epoxy. An attenuation map was generated using computed tomography and integrated into PET reconstruction. To validate AC, we compared PET images to those obtained using a conventional cradle and a volume coil (CC). Various image quality metrics were evaluated in various phantoms including a NEMA NU-4 (recovery coefficients (RC), uniformity (%STD), spillover ratio (SOR)), a homogenous phantom (slice and inter-slice uniformity) and a resolution phantom (spatial resolution). Finally, cardiac ¹⁸F-FDG PET images acquired with the 2 cradles were compared in Sprague-Dawley rats.</p><p><strong>Results: </strong>RC obtained using DC and CC configurations were not significantly different. However, the %STD was significantly increased with the DC (5.07 ± 0.18) vs. CC (3.29 ± 0.53, p = 0.0002) leading to a decreased CNR with DC. The SOR was similar between the two cradles. In homogenous phantom, there was a non-significant 1.58% underestimation of the PET signal near the surface coil and 5% overestimation on the opposite side when using the DC vs. CC. Uniformity between slice was significantly higher in DC than in CC (3.15 ± 1.2% for DC vs. 2.24 ± 0.7% for CC, p = 0.02) while uniformity inter-slice was similar in DC and CC (2.25 ± 0.83% for DC vs. 1.93 ± 0.44% for CC, p = 0.2). Spatial resolution was similar between DC and CC (axial: 1.80 vs. 1.63 mm, tangential: 1.62 vs. 1.79 mm and radial resolution: 1.53 vs. 1.76 mm for DC and CC respectively). In-vivo, cardiac standardized uptake values were similar between the two cradles (3.55 ± 0.89 and 3.59 ± 1.02 for DC and CC respectively, p = 0.81).</p><p><strong>Conclusion: </strong>Pre-calculated attenuation map using a standardized positioning of MR surface coil provided similar ¹⁸F-FDG PET images compared to a conventional PET/MR system with a volume coil, allowing its usage for combined PET and X-nuclei MR.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":"17"},"PeriodicalIF":3.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12901769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009342","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":"Clinical validation of a unified data-driven respiratory motion correction technique in ¹⁸F-FDG PET/CT imaging of upper abdominal lesions: a real-world study.","authors":"Tingting Han, Ying Wang, Zhiyong Quan, Shu Zong, Guiyu Li, Junling Wang, Miao Liu, Jia Wang, Yihuan Lu, Jing Wang, Fei Kang","doi":"10.1186/s40658-026-00833-z","DOIUrl":"10.1186/s40658-026-00833-z","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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 (SUV_max), metabolic tumor volume (MTV), tumor-to-background ratio (TBR), and horizontal-to-vertical ratio (HV_ratio) before and after correction. Percentage changes in lesion SUV_max 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.</p><p><strong>Results: </strong>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 SUV_max (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 SUV_max improvements being more pronounced in lesions with larger displacement (17.8% vs. 9.8%, p < 0.001).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":"12"},"PeriodicalIF":3.2,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12855654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994326","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":"SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.","authors":"Chunhao Li, Jiangshan Huang, Jiahui Dong, Chunlei Liu, Yankun Shi, Yu Du, Zhonglin Lu, Chunjie Cao, Greta S P Mok, Hui Wang, Jingzhang Sun","doi":"10.1186/s40658-025-00825-5","DOIUrl":"10.1186/s40658-025-00825-5","url":null,"abstract":"<p><strong>Background: </strong>Improving the image quality of cardiac gating myocardial perfusion single-photon emission computed tomography (CG MP-SPECT) is crucial for accurate diagnosis. Diffusion model (DM) has recently shown promise in MP-SPECT image denoising, but traditional DM typically require extensive computational resources and prolonged processing times. The study aims to develop and evaluate a lightweight generalized DM for efficient denoising in CG MP-SPECT images.</p><p><strong>Methods: </strong>We propose a novel step mapping generalized diffusion model (SMGDiff) that incorporates cardiac-gated MP-SPECT images as diffusion endpoints instead of traditional Gaussian noise, alongside a novel mean-preserving degradation operator to significantly reduce sampling steps and inference time. Additionally, a stepwise mapping and error optimization module (SMEO) was designed to precisely calibrate stepwise features using contextual information, thereby minimizing cumulative errors during reconstruction. A retrospective dataset of 50 MP-SPECT scans from 36 patients was used, each gated into 8 (CG-8) or 16 (CG-16) cardiac phases, generating 400/800 image pairs for CG-8/CG-16, respectively. The dataset was divided into training (35 scans), validation (5 scans), and testing (10 scans). Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized mean square error (NMSE), joint histogram, linear regression analysis and a paired two-tail t-test were employed for quantitative evaluation. Two board-certified nuclear medicine physicians performed a blinded and randomized reader study on resulted images. Images were rated on 5-point Likert scales for image quality and diagnostic confidence, with significance evaluated by Wilcoxon signed-rank tests.</p><p><strong>Results: </strong>The SMGDiff model with 5 diffusion steps (SMGDiff-5) achieved the best overall performance across all evaluation metrics for both gating configurations. SMGDiff-5 also demonstrated superior computational efficiency, requiring only 0.024 s per slice compared to 4.982 s for the 1000-step diffusion model. Furthermore, SMGDiff-5 significantly outperformed established deep learning methods including CNN, U-Net, GAN, and the denoising diffusion probabilistic model, as evidenced by higher PSNR and SSIM and lower NMSE (p < 0.05). Joint histogram and linear regression analyses confirmed these quantitative findings. Reader study results aligned with the quantitative trends which SMGDiff-5 received the highest or near-reference ratings for image quality (CG-8 4.725; CG-16 4.550) and diagnostic confidence (CG-8 4.600; CG-16 4.525), clearly above original gated images and comparable to static MP-SPECT.</p><p><strong>Conclusions: </strong>The proposed SMGDiff-5 model provides robust and efficient denoising of CG MP-SPECT images, offering superior performance compared to traditional deep learning methods with significantly reduced computational demand.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":"7"},"PeriodicalIF":3.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12830531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988868","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":"[¹⁷⁷Lu]Lu-DOTA-TATE tumour and organ time-activity curves: prediction from a single-time-point [⁶⁸Ga]Ga-DOTA-TATE PET/CT measurement.","authors":"Valentina Vasić, Johan Gustafsson, Elham Yousefzadeh-Nowshahr, Ambros J Beer, Katarina Sjögreen Gleisner, Gerhard Glatting","doi":"10.1186/s40658-025-00826-4","DOIUrl":"10.1186/s40658-025-00826-4","url":null,"abstract":"<p><strong>Aim: </strong>Predicting the time-activity curve (TAC) of [¹⁷⁷Lu]Lu-DOTA-TATE for organs at risk and neuroendocrine tumours (NETs) is an essential element in the calculation of the absorbed dose (AD) and a critical step for individualising peptide receptor radionuclide therapy (PRRT) treatment planning. This study aims to predict the TAC of [¹⁷⁷Lu]Lu-DOTA-TATE using a single quantitative image of [⁶⁸Ga]Ga-DOTA-TATE and population data with a physiologically based pharmacokinetic (PBPK) model.</p><p><strong>Methods: </strong>A PBPK model was developed for [⁶⁸Ga]Ga-DOTA-TATE and [¹⁷⁷Lu]Lu-DOTA-TATE, including organs and NETs. To generate reference TACs, general physiological parameters were taken from the literature, while individual model parameters were estimated using pre-therapy (PET/CT) and post-therapy (planar and SPECT/CT) image-based organ activity measurements from patients with NETs. Different error models were evaluated to determine the best one. To predict the TAC of [¹⁷⁷Lu]Lu-DOTA-TATE from a single [⁶⁸Ga]Ga-DOTA-TATE PET/CT, individual model parameters were estimated using only [⁶⁸Ga]Ga-DOTA-TATE organ and tumour activity measurements. Finally, the predicted [¹⁷⁷Lu]Lu-DOTA-TATE TACs for modelled organs and NETs were compared to the reference.</p><p><strong>Results: </strong>The best error model was the proportional data-based error model, where the proportionality parameter b differs between diagnostic and therapeutic data, and between tumours and organs: b_{T, Organ}, b_{T, Tumour}, and b_{D, Organ}, b_{D, Tumour}. The medians for b_{T, Organ}, b_{T, Tumour} and b_{D, Organ}, b_{D, Tumour} were determined to be 0.16, 0.39, 0.35, and 0.27, respectively. For the prediction, b_{D, Organ} and b_{D, Tumour} were used as patient-specific proportional errors. The relative prediction error (RPE) was calculated for the predicted time-integrated activity (TIA). The mean and standard deviation for the RPEs were found to be (- 5 ± 51)%, (- 4 ± 22)%, (- 13 ± 40)%, and (- 10 ± 21)% for tumours, kidneys, liver, and spleen, respectively. The mean absolute percentage errors (MAPEs) were 43%, 18%, 31% and 17% for tumours, kidney, liver, and spleen, respectively.</p><p><strong>Conclusion: </strong>The integration of the PBPK model with a data-based proportional error model represents a significant improvement in predicting TACs for estimating tumour and organ ADs following [¹⁷⁷Lu]Lu-DOTA-TATE therapy, using single-time-point PET/CT imaging with [⁶⁸Ga]Ga-DOTA-TATE. These results emphasise the importance of error model analysis in PBPK modelling.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":"9"},"PeriodicalIF":3.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12847613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988850","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":"Quantitative performance of a 3D CZT and conventional SPECT/CT for Tb-161 imaging.","authors":"Victor Nuttens, Laetitia Imbert, Astrid Delker, Michel Koole","doi":"10.1186/s40658-025-00827-3","DOIUrl":"10.1186/s40658-025-00827-3","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to assess the imaging performance of a 3D CZT SPECT/CT system for Tb-161 and compare its quantitative accuracy and image quality with those of a conventional [NaI(Tl)]-based SPECT/CT system.</p><p><strong>Methods: </strong>A NEMA Image Quality (IQ) phantom was scanned with a 3D CZT SPECT/CT (Veriton, Spectrum Dynamics) for 8 min per bed position and with a conventional SPECT/CT (Symbia T16, by Siemens) for 16 min per bed position. The 3D CZT SPECT acquisition time was virtually shortened by reparsing the listmode data. The NEMA IQ phantom was scanned with a cold background and with a sphere-to-background ratio (SBR) of 20:1 and 10:1, with 0.71 MBq/mL in the spheres. Clinical reconstruction protocols were chosen by maximizing the SBR (for the four largest spheres) while minimizing the background noise.</p><p><strong>Results: </strong>The optimal SBR-to-noise trade-off was achieved at 48 updates for the Symbia and 50 updates for the Veriton. The Veriton exhibited a 20% and 34% higher SBR with a 19% and 10% lower CoV compared to the Symbia for the 10:1 and 20:1 spheres to background ratios, respectively. The calibration factors were determined as 15.17 and 4.8 [Formula: see text] cps/MBq for the Symbia and Veriton, respectively. Reducing the scan time had minimal effect on SBR, but a twofold count reduction increased background noise by 11%.</p><p><strong>Conclusion: </strong>Quantitative Tb-161 SPECT imaging can be performed using both a conventional and a 3D CZT SPECT/CT. Given its higher sensitivity, the 3D CZT system is preferred, as it achieved better image quality with an 8-min acquisition compared to a conventional system with a 16-min acquisition.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":"10"},"PeriodicalIF":3.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12855655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951434","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}