Journal of nuclear medicine technology最新文献

{"title":"Count Differentials Between Ventilation and Perfusion in Lung Scanning.","authors":"Geoffrey M Currie","doi":"10.2967/jnmt.126.273674","DOIUrl":"https://doi.org/10.2967/jnmt.126.273674","url":null,"abstract":"","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SNMMI/EANM/ACNM/ANZSNM Procedure Standard/Procedure Guideline for Ventilation-Perfusion Pulmonary Scintigraphy.","authors":"Paul J Roach, Dale L Bailey, Geoffrey M Currie, Erin E Grady, Linda B Haramati, Michel LeBlanc, Alexander C W Lee, Pierre-Yves Le Roux, Darlene F Metter, Pierre-Yves Salaun, Barry A Siegel","doi":"10.2967/jnmt.126.273453","DOIUrl":"https://doi.org/10.2967/jnmt.126.273453","url":null,"abstract":"","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[⁶⁸Ga]Ga-PSMA-11 PET/CT Detection of Perineural Invasion in Prostate Cancer.","authors":"Sana Munir Gill, Fatima Yousaf, Mairah Razi, Sana Akhtar","doi":"10.2967/jnmt.126.272789","DOIUrl":"https://doi.org/10.2967/jnmt.126.272789","url":null,"abstract":"<p><p>Prostate cancer (PCa) is the most common cancer and third leading cause of mortality in males. Prostate-specific membrane antigen (PSMA) is a transmembrane protein that is overexpressed by 100- to 1000-fold in PCa. [⁶⁸Ga]Ga-PSMA-11 PET/CT plays an important role in the staging, restaging, and surveillance of PCa. We present the case of a 78-y-old man with previously treated high-risk PCa who developed biochemical recurrence, where [⁶⁸Ga]Ga-PSMA-11 PET/CT demonstrated tumor spread along sacral nerve roots and the sciatic nerve, suggestive of perineural invasion.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149025130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-Enhanced Learning in Nuclear Medicine: Theory, Modalities, and Applications Across the Training Continuum.","authors":"Geoffrey M Currie, Tarni Nelson, Kym Barry, Johnathan Hewis, David Gilmore","doi":"10.2967/jnmt.125.271962","DOIUrl":"10.2967/jnmt.125.271962","url":null,"abstract":"<p><p>Simulation-based education has evolved into a foundational component of nuclear medicine technologist training, driven by increasing procedural complexity, radiation safety imperatives, and growing pressure on clinical placement capacity. Nuclear medicine students must rapidly integrate cognitive, psychomotor, and interpersonal skills while working with radioactive materials and vulnerable patients in a complex regulatory environment. A diverse suite of modalities ranging from human actors through laboratory-based skills to virtual reality supports functional task alignment across the learning continuum. Evidence consistently demonstrates that simulation improves students' procedural accuracy, radiation-safety behaviors, clinical reasoning, emergency responsiveness, communication skills, and self-confidence while reducing placement-related anxiety and error risk. When conceptualized as an integrated ecosystem, simulation enhances learner outcomes, supports workforce resilience, and strengthens the transition to independent practice.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"260-266"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148211558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Empowered Nuclear Medicine Education, Part 3: Practical AI Applications for Learners.","authors":"Keith M Jacobs, Justin G Peacock","doi":"10.2967/jnmt.126.273333","DOIUrl":"10.2967/jnmt.126.273333","url":null,"abstract":"<p><p>Expanding clinical volumes, evolving radiopharmaceuticals, and new imaging technologies shape the training environment for nuclear medicine (NM) learners. Additionally, artificial intelligence (AI) tools are increasingly available. These tools can draft study guides, generate questions, simulate patient interactions, critique explanations, organize sources, and assist early research development. However, these benefits require disciplined use, because AI tools can also generate inaccurate, biased, unsupported, or overly fluent responses that interfere with durable learning. This learner-focused article provides practical AI workflows for NM technology students, residents, and fellows. It emphasizes preserving self-regulated learning, professional judgment, accountability, and source verification. Building on the theoretical foundations of part 1, this article treats AI as a structured learning partner rather than an educational authority. Direct evidence regarding NM learner outcomes remains limited. Nevertheless, broader health professions literature supports cautious AI exploration when learners prioritize verification, critical evaluation, and feedback over passive answer generation. Effective workflows require goal setting, initial independent effort, misconception identification, retrieval practice, self-explanation, source grounding, faculty review, and iterative revision. Furthermore, learners must avoid protected information, respect copyright, disclose AI assistance, and maintain skepticism toward generated output.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"234-240"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13544230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics on ¹⁷⁷Lu-DOTATATE Posttreatment Scans: Feasibility, Preprocessing Optimization, and Planar-SPECT Comparison.","authors":"Flavio Montanini, Alessio Imperiale, Alice Monaci, Samuele Valente, Vittorio Briganti, Elisabetta M Abenavoli, Matilde Nerattini, Miriam Pepponi, Gabriele Simontacchi, Lorenzo Livi, Daniele Lavacchi, Lorenzo Antonuzzo, Gayane Aghakhanyan, Giuseppe Boni, Duccio Volterrani, Valentina Berti","doi":"10.2967/jnmt.126.272060","DOIUrl":"10.2967/jnmt.126.272060","url":null,"abstract":"<p><p>¹⁷⁷Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) is an established therapeutic option for patients with neuroendocrine tumors (NETs). Although radiomics has been increasingly applied in NET research, it was developed almost exclusively using ⁶⁸Ga-DOTATOC PET imaging, whereas PRRT-related scintigraphic acquisitions remain largely unexplored. This study evaluates the feasibility of extracting hepatic radiomic features from posttreatment whole-body planar scintigraphic (WBS) and tomographic (SPECT) scans, enabling the optimization of preprocessing strategies and a direct comparison of radiomic profiles between imaging modalities. <b>Methods:</b> We conducted a retrospective analysis of data for 49 patients with well-differentiated, progressive metastatic gastroenteropancreatic NETs from 2 centers. All patients underwent posttreatment imaging within 2 d after each ¹⁷⁷Lu-DOTATATE PRRT cycle, for a total of 183 WBS and 108 SPECT scans. Preprocessing operations included image-intensity normalization, dividing each image by the mean counts of a region of interest positioned on the spleen (S-norm) and the third lumbar vertebra (V-norm). Radiomic features were extracted with no spatial resampling, using 64-bin discretization and relative intensity rescaling. <b>Results:</b> Texture extraction was successfully performed on all planar and SPECT images from both centers. Correlations between intensity-based features measured using S-norm and V-norm images showed weak yet statistically significant <i>r</i> values. Conversely, there was a robust correlation between histogram-based and second-order features measured on S-norm and V-norm images, reflected in high <i>r</i> values. V-norm intensity-based features exhibited significantly higher values and lower SDs when compared with S-norm intensity-based variables. When comparing WBS and SPECT scans, a strong correlation was observed between intensity-based variables, whereas histogram-based and second-order features showed no significant correlation. <b>Conclusion:</b> Radiomic analysis on ¹⁷⁷Lu-DOTATATE posttreatment scans was feasible, with proportional scaling to third-lumbar-vertebra activity resulting in the most effective intensity normalization approach, given the decreased variability compared with scaling to spleen activity. Correlations between features assessed using the 2 different scaling methods were weak for intensity-based features and strong for histogram-based and second-order features. Except for intensity-based features, radiomic features derived from WBS images were not comparable with those obtained from SPECT scans.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"289-296"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing USP <825> Compliance in Transarterial Radioembolization Using ⁹⁰Y Resin Microspheres.","authors":"Xingyu Nie, Kimyli Lemieux, William Denton, Molly Buffington, Elizabeth Cheatham, Bryan Lemieux, Jie Zhang, M Elizabeth Oates, Driss Raissi, Riham El Khouli","doi":"10.2967/jnmt.125.271663","DOIUrl":"10.2967/jnmt.125.271663","url":null,"abstract":"<p><p>USP <825> mandates 1-h beyond-use date (BUD) for immediate-use radiopharmaceuticals, posing workflow challenges for complex transarterial radioembolization procedures using ⁹⁰Y-labeled resin microspheres (⁹⁰Y-RM). To enhance compliance and patient safety, our institution transitioned ⁹⁰Y-RM dose preparation from an in-house process to a local commercial nuclear pharmacy, enabling a BUD of up to 96 h. We retrospectively reviewed 120 cases of ⁹⁰Y-RM preparation and administration. In the old workflow (<i>n = 55</i>), the median time from dose preparation to administration readiness was 49.1 min (interquartile range, 42.0-69.5 min), with 25.5% of doses exceeding the BUD limit. No BUD violations occurred after the new workflow (<i>n = 65</i>) was implemented. The absolute deviation between prescribed and administered activities significantly decreased from 4.3% ± 3.1% to 2.2% ± 2.1% (<i>P</i> < 0.001), with a comparable percentage of administered activity (median, 98.4% vs. 98.6%; <i>P</i> = 0.190). The new workflow effectively achieved USP <825> compliance.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"305-308"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147774190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of AI-Driven Automatic Segmentation Algorithms for Lung Shunt Fraction Estimation in ⁹⁰Y-Labeled Microsphere Therapy.","authors":"Alex M Heglin, Orhan K Öz, Mi-Ae Park","doi":"10.2967/jnmt.125.271114","DOIUrl":"10.2967/jnmt.125.271114","url":null,"abstract":"<p><p>⁹⁰Y-labeled microsphere (⁹⁰Y-microsphere) hepatic arterial radioembolization is a direct treatment for hepatic neoplasms. Before therapy, the fraction of ⁹⁰Y-microspheres that may shunt past the liver to the lungs, or lung shunt fraction (LSF), is estimated using ^99mTc-macroaggregated albumin. LSF is commonly estimated using regions of interest around the lungs and liver on planar images, a method limited by indeterminate margins and overlap between the lungs and liver. The estimation of LSF using SPECT/CT with manual segmentation is time-consuming and poorly reproducible. In this study, we evaluated 2 commercially available artificial intelligence (AI)-driven automatic segmentation algorithms for estimating pretreatment LSF in ⁹⁰Y-microsphere therapy. <b>Methods:</b> A retrospective analysis of 91 SPECT/CT studies from 43 patients who have underwent pretherapy ^99mTc-macroaggregated albumin and posttherapy ⁹⁰Y SPECT/CT imaging was performed using 2 organ segmentation tools: AI-Rad Companion Organs RT (ORT) and Contour ProtégéAI+ (CPAI). All CTs were reconstructed using a B30s kernel, whereas an additional B60s kernel was included for 61 examinations. Manual adjustments were applied to the contours and SPECT data when needed (e.g., diaphragmatic misregistration). Organ volumes and LSFs were compared between the 2 AI tools and 2 reconstruction kernels. The repeatability of LSF and lung and liver volume measurements was tested on 20 CT studies. <b>Results:</b> ORT estimates of lung and liver volumes were significantly larger with the B60s kernel compared with the B30s kernel (lung, <i>P</i> < 0.001; liver, <i>P</i> = 0.006). Conversely, with CPAI, lung and liver volumes were significantly greater using the B30s kernel compared with the B60s kernel (lung, <i>P</i> = 0.010; liver, <i>P</i> = 0.010). Lung volumes were higher using CPAI versus ORT for both the B30s (<i>P</i> < 0.001) and B60s (<i>P</i> < 0.001) kernels. Consequently, LSFs calculated using CPAI were significantly higher than ORT (<i>P</i> = 0.002). Both AI tools exhibited excellent reproducibility, with an average percent coefficient of variation near 1%. <b>Conclusion:</b> AI software can produce highly repeatable organ segmentation contours, enabling more precise calculation of LSF. SPECT/CT-based LSF calculation may offer more accurate risk assessment and dose planning before ⁹⁰Y radioembolization compared with planar LSF estimation, although further validation is warranted before widespread adoption.</p>","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"297-304"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147774214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"September Means School Is Back in Session.","authors":"Julie Dawn Bolin","doi":"10.2967/jnmt.126.273629","DOIUrl":"https://doi.org/10.2967/jnmt.126.273629","url":null,"abstract":"","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":"54 3","pages":"215-216"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148887910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The SNMMI-TS Molecular Therapy Task Force Therapy Intensive and Certificate Program Initiative.","authors":"Megan Hungerford, David Gilmore, Julie Dawn Bolin, Cybil J Nielsen, Dmitry D Beyder, Jay J Smith, Shannon N Youngblood","doi":"10.2967/jnmt.126.273390","DOIUrl":"10.2967/jnmt.126.273390","url":null,"abstract":"","PeriodicalId":16548,"journal":{"name":"Journal of nuclear medicine technology","volume":" ","pages":"241-243"},"PeriodicalIF":1.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148549476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}