EJNMMI Physics最新文献

{"title":"Quantitative ¹⁶¹Tb SPECT/CT imaging for dosimetry using a ring-based digital CZT camera.","authors":"Eline Zoetelief, Marcel Segbers, Johannes Hofland, Anita A Harteveld, Tessa Brabander","doi":"10.1186/s40658-026-00890-4","DOIUrl":"10.1186/s40658-026-00890-4","url":null,"abstract":"<p><strong>Background: </strong>¹⁶¹Tb-labeled compounds are emerging as promising alternatives to ¹⁷⁷Lu-labeled compounds for radioligand therapy (RLT). The assessment of RLT safety and efficacy rely on accurate dosimetry, which necessitates quantitative activity measurement from multiple SPECT acquisitions. However, no studies to date have assessed ¹⁶¹Tb quantification using the ring-based cadmium-zinc-telluride (CZT) digital SPECT/CT systems, nor have optimal reconstruction parameters been established. This study aimed to optimize ¹⁶¹Tb SPECT reconstruction parameters for a CZT-based system.</p><p><strong>Methods: </strong>A phantom study was performed using the NEMA imaging quality phantom with the standard sphere set (diameters 10-37 mm), with the background prepared either with a 10.5:1 activity ratio or without activity. Imaging was conducted on a StarGuide (GE Healthcare) ring-based CZT digital SPECT/CT system using the manufacturer's recommended protocol. Scans were repeated over activity levels up to 2.2 GBq. Three reconstruction methods were evaluated: OSEM, Q.Clear without regularization term (QClearN) and Q.Clear with regularization term (QClearR), each tested with multiple iteration-subset combinations. To compare the reconstruction methods, recovery coefficient (RC), noise levels and spatial resolution were assessed.</p><p><strong>Results: </strong>RC values increased with sphere size and number of updates. At 864 updates, RC ranged from 0.56 to 0.95 for OSEM, 0.52-0.92 for QClearN, and 0.52-0.92 for QClearR. Noise levels, quantified by the coefficient of variation, in OSEM increased from 0.07 to 1.16 with increasing number of updates, while the noise levels of QClearN and QClearR ranged from 0.07 to 0.40 and 0.07 to 0.38, respectively. Spatial resolution was assessed using the full with at half maximum (FWHM) derived from matched-filter analysis of a digital phantom, with values of 7.9-17.0 mm for OSEM, 8.7-17.7 mm for QClearN, and 8.8-17.8 mm for QClearR. Gibbs artifact appeared in all methods, intensifying with higher number of updates.</p><p><strong>Conclusions: </strong>This study demonstrated the feasibility of quantitative ¹⁶¹Tb imaging using a ring-based CZT SPECT/CT system. Recommended number of updates are 48-96 for OSEM and approximately 200 for QClearN and QClearR. With these settings, ¹⁶¹Tb achieved higher RC values and improved spatial resolution compared to published ¹⁷⁷Lu data, supporting its potential as a substitute for ¹⁷⁷Lu for image-based dosimetry in RLT.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148053374","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":"Correcting fast irregular motion in PET: maximum-likelihood motion and activity (MLMA) reconstruction.","authors":"Rodrigo José Santo, Ethan Waterink, Cornelis A T van den Berg, Thomas Olausson, Alessandro Sbrizzi, Hugo W A M de Jong, Casper Beijst","doi":"10.1186/s40658-026-00894-0","DOIUrl":"10.1186/s40658-026-00894-0","url":null,"abstract":"<p><strong>Purpose: </strong>Positron emission tomography (PET) imaging naturally suffers from motion blur due to long acquisitions. As such, motion-compensation provides a promising solution to improve image quality. Traditional methods for motion-correction often involve a combination of gating and data-binning, assuming that motion is periodic, to accumulate sufficient counts per motion-state frame. Irregular motion can be estimated but it requires complex motion-capture systems or elaborate data-driven algorithms, which are difficult to configure (physical or model setup) and hampered by the high noise of short timeframes. We propose a new method that alternatingly estimates and corrects for motion at high temporal frequency in PET imaging: Maximum-Likelihood Motion and Activity (MLMA) reconstruction. MLMA estimates both the time-series of deformation vector fields and the motion-corrected activity image for the whole acquisition. Together, this allows to visualize anatomical structures moving in time.</p><p><strong>Methods: </strong>The method exploits the high compressibility and spatial smoothness of motion through a cubic B-spline motion-model and through spatial regularization. MLMA was configured to 2 Hz resolution and applied on (A) the digital XCAT phantom, (B) acquisitions of a moving anthropomorphic torso phantom and (C) clinical patient data.</p><p><strong>Results: </strong>The results show that MLMA can accurately correct motion at high frequency (2 Hz), with subvoxel accuracy (up to 2.5 mm root-mean-square error (RMSE) on 4 mm isotropic voxels) and realistic breathing (amplitude range 14.7 mm and average period 4.5 s). This enables visually-noticeable improvements on image quality.</p><p><strong>Conclusion: </strong>The proposed MLMA reconstruction method resolves the motion encoded in very-short PET timeframes, irrespective of the very low counts and noise inherent to PET projection data.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148028389","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":"Improved patient models for simulation of clinically realistic ⁶⁸Ga-SSTR PET and ¹⁷⁷Lu-PRRT SPECT studies.","authors":"Johan Gustafsson, Philip Kalaitzidis, Selma Curkic Kapidzic, Michael Ljungberg, Katarina Sjögreen Gleisner","doi":"10.1186/s40658-026-00899-9","DOIUrl":"10.1186/s40658-026-00899-9","url":null,"abstract":"<p><strong>Background: </strong>The aim was to improve the realism of patient models for Monte-Carlo based evaluation of image-based quantification of tumour volume and activity in [⁶⁸Ga]Ga-DOTA-TOC PET and [¹⁷⁷Lu]Lu-DOTA-TATE SPECT images.</p><p><strong>Methods: </strong>Three phantoms from the XCAT population were used as basis. Tumour structures were obtained from segmentation of patient ⁶⁸Ga-PET images and positioned at relevant positions in the phantoms. Respiratory motion was modelled, and the centre-of-mass of tumours displaced according to the organ motion in the respiration. Tumour activity concentrations were sampled from a log-normal distribution derived from patient data. Intra-organ non-uniform activity concentration was modelled for spleen, liver, and intestines. The full phantom models included all these aspects. For assessment of the impact of the modelled effects, simulations were also carried out for phantoms without respiratory motion (full-without-motion) and with spherical lesions without background variability (standard models). Evaluation was focussed on the impact of the models on the errors in tumour activity concentrations estimated from SPECT and PET images with and without partial-volume correction (PVC).</p><p><strong>Results: </strong>The full models were successfully realized and were found to yield a visual appearance that more closely resembled that of patients, compared to the full-without-motion and standard models. For tumours in the volume range 0 mL to 10 mL, the mean relative errors obtained for ¹⁷⁷Lu-SPECT without PVC were - 51%, -38%, and - 34% for the full models, the full-without-motion models, and the standard models, respectively. Corresponding results for ⁶⁸Ga-PET without PVC were - 34%, -25%, and - 23%. Thus, for both PET and SPECT, the negative bias in estimated tumour activity concentrations demonstrated a clear sensitivity to the taken modelling approach.</p><p><strong>Conclusions: </strong>Respiratory motion is an important factor for modelling realistic patient images, in the context of quantitative SPECT and PET. Non-spherical tumours and background variability has a minor, but measurable impact.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13222913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013749","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 segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in ¹⁷⁷Lu-DOTATATE dosimetry.","authors":"Tetsu Nakaichi, Yuhei Shimizu, Satoshi Nakamura, Yasunori Shuto, Yusaku Kasai, Atsushi Shishido, Kouji Kunito, Hiroki Nakayama, Mihiro Takemori, Masashi Kawamura, Takahito Chiba, Hiroyuki Okamoto, Tairo Kashihara, Hiroshi Igaki, Kimiteru Ito","doi":"10.1186/s40658-026-00897-x","DOIUrl":"10.1186/s40658-026-00897-x","url":null,"abstract":"<p><strong>Background: </strong>The efficacy of deep learning-based artificial intelligence segmentation (AI-seg) in ¹⁷⁷Lu-DOTATATE dosimetry remains underexplored. This study evaluates AI-seg's contouring accuracy, dosimetric reliability, and time efficiency.</p><p><strong>Methods: </strong>We analyzed 23 patients treated with ¹⁷⁷Lu-DOTATATE. Four medical physicists (MPs) and four radiological technologists (RTs) manually delineated liver (including lesions), spleen, and kidneys on CT images. The most experienced MP modified AI-seg outputs to establish a reference contour, confirmed by a board-certified physician. Both the manual and AI-seg contours were checked against this reference using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA). Maximum, mean, and minimum absorbed doses were calculated from a single time-point image using SurePlan™ MRT (MIM Software Inc.) to assess dosimetric implications. The total time required for dosimetry-including image reconstruction, segmentation, and absorbed dose calculation-was compared between the manual and reference methods.</p><p><strong>Results: </strong>Only one liver delineation by an MP required correction. Median (IQR) DSCs for liver, spleen, and kidneys were 0.955 (0.950-0.961), 0.929 (0.898-0.944), and 0.929 (0.918-0.942) for manual, 0.988 (0.946-0.998), 0.965 (0.858-0.995), and 0.994 (0.985-0.999) for AI-seg, respectively. All organs met the acceptable DSC threshold ([Formula: see text]0.8). Median HD in liver exceeded 10.0 mm for both methods, with AI-seg exceeding 30.0 mm in a few cases (liver: 3/23; spleen: 2/23) due to hepatomegaly. The median MDA in manual and AI-seg was below 2.0 mm. Median mean absorbed doses in the reference was 4.03 (1.69-5.81) Gy for liver, 1.55 (1.30-2.50) Gy for spleen, and 2.03 (1.45-2.44) Gy for kidneys. Only the kidneys absorbed dose from AI-seg differed significantly, showing a 1.4% increase. Dosimetry using the reference method took significantly less time than the manual approach (47.0 [30.0-58.0] min vs. 54.3 [49.5-67.0] min, p = 0.014).</p><p><strong>Conclusions: </strong>AI-seg with minor adjustment enables faster yet accurate absorbed dose estimation in ¹⁷⁷Lu-DOTATATE. Despite segmentation challenges in hepatomegaly cases, MPs and RTs demonstrated competent contouring performance, supporting collaborative dosimetry workflows with physician oversight.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13221549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148005057","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":"Improved attenuation correction registration for FDG PET/CT images using data-driven gating (DDG)-based motion match.","authors":"Zoë Wilson, Manjeet Kuhar, Kuan-Hao Su, Robert Johnsen, Kevin M Bradley, Daniel R McGowan","doi":"10.1186/s40658-026-00896-y","DOIUrl":"10.1186/s40658-026-00896-y","url":null,"abstract":"<p><strong>Background: </strong>Advancements in PET technologies and reconstruction methods have improved spatial resolution and noise in PET/CT, making respiratory motion artefacts more impactful on image quality. The motion-match CT (MMCT) algorithm estimates the phase of a helical CT and warps it towards the end expiration of a patient's respiratory cycle. This MMCT-attenuation correction (MMCTAC) can then be used to reconstruct whole body FDG PET/CT data. The performance of these images were compared to those reconstructed using the uncorrected clinical standard attenuation correction from helical CT (normAC), indicating the proportion of cases where MMCTAC images could reduce motion artefact and improve lesion detectability.</p><p><strong>Methods: </strong>A sequential cohort of 145 whole-body FDG PET/CT scans was previously evaluated for a data-driven gating study, which identified the gated region of interest (ROI) in 98 high-motion patients. 23 of these high-motion patients had small lesions (n=36) identified in the ROI by a clinician to undergo qualitative analysis comparing the two attenuation correction maps used. PET data were reconstructed using BSREM iterative reconstruction with 3 different portions of the collected data: 1. Ungated 6 min, 2. Ungated 3 min, 3. Quiescent period gated. These reconstructions were generated using normAC or MMCTAC, totaling six reconstructions for semi-quantitative analysis and clinician evaluation. An experienced radiologist ranked the 6 images and scored them on a 5-point Likert Scale for lesion detectability, diagnostic confidence, and image quality.</p><p><strong>Results: </strong>In 52% (n=12) of patients with lesions, the clinician ranked all three MMCTAC images higher than the three normAC equivalent reconstructions. Lesion detectability and diagnostic confidence scores retained positive mean differences across the three MMCTAC reconstructions compared to normAC. Similarly, the SUVmax values for 36 lesions demonstrated a significant increase (p < 0.05) for MMCTAC versus normAC images across all three reconstructions.</p><p><strong>Conclusions: </strong>The MMCT algorithm successfully enables the phase-matching of a helical CT to the PET data to reduce the presence of respiratory motion artefact for FDG PET/CT images. Overall, the motion-matched CTAC image scores demonstrated the algorithm's efficacy at reducing the motion artefacts present in FDG PET/CT images, improving diagnostic capability for assessment of lesions in the thorax and upper abdomen.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148005001","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":"A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.","authors":"Jingxi Hu, Yuqian Huang, Qiyang Zhang, Yingying Hu, Yumo Zhao, Xu Zhang, Wei Fan, Hairong Zheng, Dong Liang, Zhanli Hu, Changhui Jiang, Xiangjian He, Na Zhang","doi":"10.1186/s40658-026-00889-x","DOIUrl":"10.1186/s40658-026-00889-x","url":null,"abstract":"<p><strong>Background: </strong>Pediatric lymphoma patients undergo multiple ¹⁸F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumulative radiation dose, particularly from the CT component. We propose SnapPET, a CT-sparing deep learning-based framework that uses 2D ultra-short non-attenuation-corrected (NAC) PET (Maximum intensity projection) MIP images to generate 2D high-quality PET MIP images for rapid screening and triage.</p><p><strong>Methods: </strong>Two hundred pediatric patients with FDG-avid lymphoma imaged on a total-body PET/CT scanner were retrospectively analyzed. From 10-min list-mode data, non-attenuation-corrected (NAC) PET images at 3-300 s were retrospectively reconstructed, and 600-s attenuation-corrected (AC) PET served as the reference. Whole-body coronal maximum-intensity projections (MIPs) were converted into 2D images and divided into 64 × 64 patches. For each acquisition duration, an independent latent diffusion-based autoencoder was trained to map the corresponding short-duration NAC PET MIPs to AC-like coronal PET MIPs. The intended clinical application was rapid pre-CT triage, with primary emphasis on the ultra-short 3-s setting, while longer durations were analyzed comparatively across count levels. Image quality was evaluated using PSNR, SSIM, and NMSE, as well as lesion ROI-based SUVmax and SUVmean. Three benchmark models (U-Net, GAN, and DDPM) were trained under identical settings for comparison. In addition, two nuclear medicine physicians assessed image quality using 5-point Likert scales, and lesion-level ROC and ALROC analyses were performed.</p><p><strong>Results: </strong>SnapPET substantially improved image quality over NAC input for all acquisition times. For 3-s scans, PSNR increased from 28.6 ± 4.3 to 35.3 ± 6.0 and SSIM from 0.85 ± 0.09 to 0.98 ± 0.02, while NMSE decreased from 0.23 ± 0.18 to 0.03 ± 0.01 (all p < 0.001). SUV metrics in lesion ROIs showed high correlation with reference AC PET, with minimal bias. Compared with UNet, GAN, and DDPM, SnapPET achieved the best quantitative metrics and the highest lesion-level ROC and ALROC curves, indicating superior detectability and localization. Reader scores for noise suppression, artifact reduction, contrast retention, and lesion discrimination were consistently higher for SnapPET images, particularly for 3- and 15-s acquisitions.</p><p><strong>Conclusion: </strong>SnapPET enables rapid pediatric PET imaging by generating high-quality, AC-like 2D images directly from ultra-short NAC acquisitions. The method improves image quality, preserves quantitative accuracy, and enhances lesion detectability compared with both NAC input and competing deep-learning baselines. This CT-sparing screening and triage approach has the potential to reduce reliance on attenuation-correction CT and support personalized, lower-dose CT strategies in pediatric lymphoma care.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147986997","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":"Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.","authors":"Chenyang Han, Andrew T Trout, Andi Li, Joseph G Meier, Vinicius P V Alves, Joseph MacLean, Nadeen Abu Ata, Yinan Li, Susan E Sharp, Christopher G Anton, Jing Tang","doi":"10.1186/s40658-026-00876-2","DOIUrl":"10.1186/s40658-026-00876-2","url":null,"abstract":"<p><strong>Background: </strong>Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging. However, conventional DL approaches typically require large and diverse training datasets to achieve generalizability. We propose a patch-based DL (PDL) approach that learns local structural representations from a single high-quality examination, with the goal of improving image quality and preserving quantitative accuracy of reduced-count pediatric whole-body PET while minimizing training burden.</p><p><strong>Methods: </strong>This retrospective study included PET/CT datasets acquired on a GE Discovery MI Gen 2 scanner. A single pediatric examination with high structural conspicuity and low noise was used for training. Testing was performed using a NEMA phantom and 88 clinical pediatric examinations (age < 12 years) acquired with routine pediatric FDG dosing and a 90‑s‑per‑bed protocol. Clinical list‑mode data were truncated to 20 s per bed to simulate reduced‑count acquisitions; training and phantom datasets were downsampled to match this noise level. The PDL model was trained using paired full‑count and reduced‑count image patches and applied to denoise reduced‑count phantom and patient images. Phantom evaluations assessed spatial resolution, contrast recovery, and noise. Clinical evaluation included a blinded observer study and quantitative SUV analysis, followed by assessment of the noise-bias tradeoff.</p><p><strong>Results: </strong>PDL preserved spatial resolution and contrast while achieving substantial noise reduction in phantom experiments. In the observer study, PDL‑enhanced 20‑s‑per‑bed images were rated as equivalent or superior in overall image quality and noise relative to standard 90‑s‑per‑bed images in more than 85% of cases. Small negative relative bias in SUV_max and SUV_mean was observed in the liver, mediastinal blood pool, and lesions, with no statistically significant differences compared with standard‑count images. Compared with highly regularized reconstruction, PDL achieved greater noise reduction with reduced bias.</p><p><strong>Conclusions: </strong>PDL enhancement for pediatric whole-body PET imaging may enable a reduction in acquisition time to 20 s per bed or a reduction in administered activity to approximately 22% of current clinical practice, while maintaining spatial resolution, contrast, quantitative accuracy, and diagnostic image quality.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13369067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981086","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":"A novel dynamic cardiac phantom for comparing blood pool and myocardial perfusion tracers: initial evaluation with CZT-ERNA and PET-MPI.","authors":"Marc Østergaard Nielsen, Rebekka Wozniak, Theis Cornelius Hansen, Linus Leonhart Duchstein, Jonas Damm Hermansen, Svend Hvidsten, Lars Thorbjørn Jensen, Bo Zerahn","doi":"10.1186/s40658-026-00888-y","DOIUrl":"10.1186/s40658-026-00888-y","url":null,"abstract":"<p><strong>Background: </strong>Nuclear imaging modalities can quantify cardiac volumes and left ventricular ejection fraction (LVEF), but systematic differences between techniques complicate direct comparison. Phantom-based evaluations comparing myocardial perfusion and blood-pool methods are scarce.</p><p><strong>Methods: </strong>We used a dynamic two-compartment cardiac phantom (myocardium and left ventricular [LV] lumen) with adjustable stroke volume and heart rate. Static and gated acquisitions were performed on cadmium-zinc-telluride equilibrium radionuclilde angiocardiography (CZT-ERNA) and gated positron emission tomography myocardial perfusion imaging (PET-MPI) under identical volumes and heart-rate settings. Computed tomography (CT) served as the reference for end-diastolic volume (EDV) and end-systolic volume (ESV). LVEF was derived from these volumes for all modalities. We also assessed the impact of phantom position relative to detectors, as well as the effect of attenuation correction via a thorax phantom.</p><p><strong>Results: </strong>CZT-ERNA overestimated static LV volumes relative to CT, whereas PET-MPI underestimated them. Both modalities underestimated LVEF compared with CT-derived values, with a larger bias for PET-MPI. On PET-MPI, attenuation correction produced similar volumes with and without the thorax phantom. CZT-ERNA volumes decreased as detector distance increased.</p><p><strong>Conclusion: </strong>Under controlled phantom conditions, CZT-ERNA and PET-MPI show inverse biases for LV volumes and both underestimated LVEF versus CT. These findings point to the influence of reconstruction and contouring algorithms, attenuation correction, and camera positioning. The dynamic phantom enables direct comparison of perfusion- and blood-pool-based measurements and may support modality-specific calibration when translating absolute values across techniques.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959894","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":"Assessing the suitability of automated registration and segmentation for dosimetry calculations in SIRT treatment planning.","authors":"Félix Quinton, Romain Popoff, Fabrice Meriaudeau, Jean-Marc Vrigneaud, Olivier Chevallier, Julie Pellegrinelli, Jean-Louis Alberini, Benoit Presles","doi":"10.1186/s40658-026-00858-4","DOIUrl":"10.1186/s40658-026-00858-4","url":null,"abstract":"<p><strong>Background: </strong>Selective internal radiation therapy (SIRT) increasingly relies on accurate magnetic resonance imaging (MRI) to computed tomography (CT) registration and accurate liver and tumour segmentation, for effective pre-treatment planning. This study evaluates the impact of automatic registration and segmentation techniques on dosimetry calculations for SIRT treatment planning. It compares semi-automatic and automatic registration, as well as manual and automatic deep learning-based segmentation.</p><p><strong>Methods: </strong>Pre-treatment data from 90 patients with hepatocellular carcinoma were analysed. The dataset consisted of contrast-enhanced T1-weighted MRI scans with manually delineated liver and tumour volumes of interest (VOIs), as well as single-photon emission computed tomography (SPECT)/CT scans with manually or semi-automatically delineated liver VOI. The clinical routine pipeline, which involves semi-automatic registration and manual/semi-automatic segmentation, was used as the baseline pipeline and compared to experimental pipelines that use intensity-based deformable automatic registration or deep learning-based automatic segmentation. Dosimetric accuracy was assessed via metrics such as the mean absorbed dose, the minimum dose received by 70% of the volume (D70), and inverted cumulative dose-volume histograms.</p><p><strong>Results: </strong>Semi-automatic and automatic MRI to CT liver registration achieved comparable Dice scores of 92%. However, tumour registration varied significantly between registration methods yielding average Dice scores of 79%. Multimodal tumour segmentation approaches outperformed monomodal ones, achieving average Dice scores of 66.6 versus 62.5%. Using the baseline pipeline, the average tumour absorbed dose per patient was 115.6 Gy. Using the fully automatic approach, tumour absorbed doses differed from baseline values by an average of 6.6 Gy. Differences ranged from 0.3 Gy in the best case to 202.8 Gy when the automatic tumour segmentation markedly deviated from the manual delineation. Finally, it was found that a Dice score of at least 80% was required to avoid statistically significant differences in absorbed dose estimates between the clinical and automatic approaches.</p><p><strong>Conclusion: </strong>Semi-automatic and automatic registration show equivalent performance, allowing for complete automation. Automatic segmentation demonstrates promising results, with approximately 40% of patients achieving tumour Dice scores above the 80% threshold. 30% of cases show intermediate performance (60-80% tumour Dice scores), while the remaining 30% are still challenging. Further refinement of segmentation methods is required to enhance dosimetric accuracy.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959965","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":"Approximate CT-free patient specific attenuation correction method for cardiac-dedicated pinhole CZT system: a feasibility phantom study.","authors":"Michel Hesse, Florian Dupont, Véronique Roelants","doi":"10.1186/s40658-026-00893-1","DOIUrl":"10.1186/s40658-026-00893-1","url":null,"abstract":"<p><strong>Background: </strong>Multi-pinhole cardiac-dedicated CZT gamma cameras improve clinical workflow by reducing acquisition duration and/or injected activities. As most of these systems lack integrated CT scanner, attenuation artifacts may appear in the reconstructed image depending on patient morphology. External CT-based attenuation correction is possible but requires manual SPECT-CT registration and increases patient absorbed dose. The aim of the present study was to develop an approach to estimate patient-specific attenuation maps directly from emission data acquired on a pinhole cardiac CZT camera.</p><p><strong>Methods: </strong>Two anthropomorphic torso phantoms were used. Their heart wall (with or without defects), liver insert and background cavity were filled with ^99mTc solutions to simulate clinical activities. Reference acquisitions were obtained using a CZT SPECT/CT system. For each phantom, two acquisitions were performed on a pinhole cardiac CZT camera: a standard cardiac acquisition and a second acquisition with detectors radially moved away. Combination of both acquisitions enabled delineation of the body contour and segmentation of soft-tissue and lung regions. From this segmentation, a CT-free attenuation map was generated and imported into the manufacturer's reconstruction software to produce attenuation corrected (AC) images. These CT-free AC images were semi-quantitatively compared, using 17-segment polar maps, to (1) images corrected using a registered external CT, (2) AC images from the CZT SPECT/CT system, and (3) the true activity distribution in the phantoms. A three-dimensional voxel-wise comparison was also performed between CT-free AC and CT-based AC images of the pinhole CZT camera.</p><p><strong>Results: </strong>CT-free attenuation maps produced tracer distribution and 17-segment polar maps closely matching those obtained with CT-based attenuation correction. Heart wall defects in the anterior and inferior areas remained clearly visible except for half-obstructive defects. Differences relative to CZT SPECT/CT images were mainly attributable to camera geometry and reconstruction algorithm.</p><p><strong>Conclusions: </strong>The novel CT-free patient-specific attenuation correction method yields images comparable to CT-based AC images and shows good agreement with AC images from a CZT SPECT/CT system. This approach could enhance the diagnostic performance of current cardiac pinhole CZT cameras without requiring additional CT acquisitions.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959932","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}