EJNMMI Physics最新文献

{"title":"Comments on the paper \"Data-driven gating (DDG)-based motion match for improved CTAC registration. EJNMMI Physics. 2024;11(1):42.\"","authors":"Tinsu Pan","doi":"10.1186/s40658-024-00690-8","DOIUrl":"10.1186/s40658-024-00690-8","url":null,"abstract":"<p><p>Misregistration between CT and PET in PET/CT is mainly caused by respiratory motion or irregular respiration during the CT scan in PET/CT. Other than repeat CT, repeat PET/CT, or data-driven gated (DDG) CT, there is no practical approach to mitigate the misregistration artifacts and subsequent CT attenuation correction (CTAC) of the PET data. DDG PET derives a respiratory motion model based on the multiple phases of PET images without hardware gating and it allows for a potential correction of the misregistration artifacts based on the respiratory motion model. The purpose of this commentary was to compare the recent two publications on matching the random phase of helical CT with one of the PET phases derived from the motion model of DDG PET and warping the misregistered helical CT for CTAC of and registration with PET or DDG PET. The two publications were similar in methodology. However, the data sets used for the comparison were different and could potentially impact their conclusions.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"88"},"PeriodicalIF":3.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460728","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 deep learning based image quality enhancements for the BGO based GE omni legend PET/CT.","authors":"Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D'Asseler, Stefaan Vandenberghe","doi":"10.1186/s40658-024-00688-2","DOIUrl":"https://doi.org/10.1186/s40658-024-00688-2","url":null,"abstract":"<p><strong>Background: </strong>This study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.</p><p><strong>Methods: </strong>The current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.</p><p><strong>Results: </strong>The deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8 <math><mo>%</mo></math> , 17.2 <math><mo>%</mo></math> , and 14.3 <math><mo>%</mo></math> for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about -30 <math><mo>%</mo></math> ), while high precision deep learning increased the contrast (about 10 <math><mo>%</mo></math> ).</p><p><strong>Conclusion: </strong>This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"86"},"PeriodicalIF":3.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460727","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":"Development and validation of an innovative administration system to facilitate controlled holmium-166 microsphere administration during TARE.","authors":"Meike W M van Wijk, Gerhard van Wolfswinkel, Mark J Arntz, Marcel J R Janssen, Joey Roosen, J Frank W Nijsen","doi":"10.1186/s40658-024-00692-6","DOIUrl":"https://doi.org/10.1186/s40658-024-00692-6","url":null,"abstract":"<p><strong>Background: </strong>To develop and validate a novel administration device for holmium-166 transarterial radioembolisation (TARE) with the purpose of facilitating controlled fractional microsphere administration for a more flexible and image-guided TARE procedure.</p><p><strong>Methods: </strong>A Controlled Administration Device (CAD) was developed using MR-conditional materials. The CAD contains a rotating syringe to keep the microspheres in suspension during administration. Different rotational speeds were tested ex vivo to optimise the homogeneity of microsphere fractions administered from the device. The technical performance, accuracy, and safety was validated in three patients in a clinical TARE setting by administering a standard clinical dose in 5 fractions (identifier: NCT05183776). MRI-based dosimetry was used to validate the homogeneity of the given fractions in vivo, and serious adverse device event ((S)A(D)E) reporting was performed to assess safety of the CAD.</p><p><strong>Results: </strong>A rotational speed of 30 rpm resulted in the most homogeneous microsphere fractions with a relative mean deviation of 1.1% (range: -9.1-8.0%). The first and last fraction showed the largest deviation with a mean of -26% (std. 16%) and 7% (std. 13%). respectively. In the three patient cases the homogeneity of the microsphere fractions was confirmed given that MRI-based dosimetry showed near linear increase of mean absorbed target liver dose over the given fractions with R² values of 0.98, 0.97 and 0.99. No (S)A(D)E's could be contributed to the use of the CAD.</p><p><strong>Conclusions: </strong>The newly developed CAD facilitates safe and accurate fractional microsphere administration during TARE, and can be used for multiple applications in the current and future workflows of TARE.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"87"},"PeriodicalIF":3.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460729","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":"Method for co-registration of high-resolution specimen PET-CT with histopathology to improve insight into radiotracer distributions.","authors":"Luna Maris, Menekse Göker, Jens M Debacker, Kathia De Man, Bliede Van den Broeck, Jo Van Dorpe, Koen Van de Vijver, Vincent Keereman, Christian Vanhove","doi":"10.1186/s40658-024-00681-9","DOIUrl":"https://doi.org/10.1186/s40658-024-00681-9","url":null,"abstract":"<p><strong>Background: </strong>As the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology.</p><p><strong>Methods: </strong>We applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [ <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>18</mn></mmultiscripts> </math> F]fluorodeoxyglucose ([¹⁸F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist.</p><p><strong>Results: </strong>We imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake.</p><p><strong>Conclusions: </strong>We developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"85"},"PeriodicalIF":3.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11473743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460730","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":"Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.","authors":"Kyounghyoun Kwon, Dongkyu Oh, Ji Hye Kim, Jihyung Yoo, Won Woo Lee","doi":"10.1186/s40658-024-00686-4","DOIUrl":"10.1186/s40658-024-00686-4","url":null,"abstract":"<p><strong>Background: </strong>Accurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans.</p><p><strong>Results: </strong>A dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L₁) and three times the absolute gradient difference loss (3 × L_GDL), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%.</p><p><strong>Conclusion: </strong>The study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"84"},"PeriodicalIF":3.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11469987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406239","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":"Feasibility of shortening scan duration of ¹⁸F-FDG myocardial metabolism imaging using a total-body PET/CT scanner.","authors":"Xiaochun Zhang, Zeyin Xiang, Fanghu Wang, Chunlei Han, Qing Zhang, Entao Liu, Hui Yuan, Lei Jiang","doi":"10.1186/s40658-024-00689-1","DOIUrl":"10.1186/s40658-024-00689-1","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate ¹⁸F-FDG myocardial metabolism imaging (MMI) using a total-body PET/CT scanner and explore the feasible scan duration to guide the clinical practice.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 41 patients who underwent myocardial perfusion-metabolism imaging to assess myocardial viability. The patients underwent ¹⁸F-FDG MMI with a total-body PET/CT scanner using a list-mode for 600 s. PET data were trimmed and reconstructed to simulate images of 600-s, 300-s, 120-s, 60-s, and 30-s acquisition time (G600-G30). Images among different groups were subjectively evaluated using a 5-point Likert scale. Semi-quantitative evaluation was performed using standardized uptake value (SUV), myocardial to background activity ratio (M/B), signal to noise ratio (SNR), contrast to noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV). Myocardial viability analysis included indexes of Mismatch and Scar. G600 served as the reference.</p><p><strong>Results: </strong>Subjective visual evaluation indicated a decline in the scores of image quality with shortening scan duration. All the G600, G300, and G120 images were clinically acceptable (score ≥ 3), and their image quality scores were 4.9 ± 0.3, 4.8 ± 0.4, and 4.5 ± 0.8, respectively (P > 0.05). Moreover, as the scan duration reduced, the semi-quantitative parameters M/B, SNR, CNR, and CR decreased, while SUV and CV increased, and significant difference was observed in G300-G30 groups when comparing to G600 group (P < 0.05). For myocardial viability analysis of left ventricular and coronary segments, the Mismatch and Scar values of G300-G30 groups were almost identical to G600 group (ICC: 0.968-1.0, P < 0.001).</p><p><strong>Conclusion: </strong>Sufficient image quality for clinical diagnosis could be achieved at G120 for MMI using a total-body PET/CT scanner, while the image quality of G30 was acceptable for myocardial viability analysis.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"83"},"PeriodicalIF":3.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11467154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399764","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":"SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.","authors":"Farnaz Yousefzadeh, Mehran Yazdi, Seyed Mohammad Entezarmahdi, Reza Faghihi, Sadegh Ghasempoor, Negar Shahamiri, Zahra Abuee Mehrizi, Mahdi Haghighatafshar","doi":"10.1186/s40658-024-00687-3","DOIUrl":"10.1186/s40658-024-00687-3","url":null,"abstract":"<p><strong>Purpose: </strong>The problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR).</p><p><strong>Methods: </strong>In this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the first phase, the network is trained by a confined image region around the heart in transverse view. The second phase improves the network's generalization by tuning the network weights with the full image size as the input. The network was trained and tested by a dataset of 247 patients who underwent two immediate serially high- and low-noise SPECT-MPI.</p><p><strong>Results: </strong>Quantitative results show that compared to post-reconstruction low pass filtering and post-reconstruction deep denoising methods, our proposed method can decline the COV_bkg of the images by up to 10.28% and 12.52% and enhance the CNR by up to 54.54% and 45.82%, respectively.</p><p><strong>Conclusion: </strong>The iterative deep denoising method outperforms 2D low-pass Gaussian filtering with an 8.4-mm FWHM and post-reconstruction deep denoising approaches.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"82"},"PeriodicalIF":3.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11461437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142388947","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":"PET radiomics in lung cancer: advances and translational challenges.","authors":"Yongbai Zhang, Wenpeng Huang, Hao Jiao, Lei Kang","doi":"10.1186/s40658-024-00685-5","DOIUrl":"10.1186/s40658-024-00685-5","url":null,"abstract":"<p><p>Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"81"},"PeriodicalIF":3.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364846","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":"Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.","authors":"Yu Du, Jingzhang Sun, Chien-Ying Li, Bang-Hung Yang, Tung-Hsin Wu, Greta S P Mok","doi":"10.1186/s40658-024-00680-w","DOIUrl":"10.1186/s40658-024-00680-w","url":null,"abstract":"<p><strong>Background: </strong>Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.</p><p><strong>Methods: </strong>Fifty anonymized patients who underwent routine ^99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.</p><p><strong>Results: </strong>AttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.</p><p><strong>Conclusions: </strong>AttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"80"},"PeriodicalIF":3.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361340","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":"Reproducibility of [¹⁸F]MK-6240 kinetics in brain studies with shortened dynamic PET protocol in healthy/cognitively normal subjects.","authors":"Phelipi N Schuck, Xiuyuan H Wang, Emily B Tanzi, Sally Xie, Yi Li, Sadek A Nehmeh","doi":"10.1186/s40658-024-00679-3","DOIUrl":"https://doi.org/10.1186/s40658-024-00679-3","url":null,"abstract":"<p><strong>Background: </strong>[¹⁸F]MK-6240 is a neurofibrillary tangles PET radiotracer that has been broadly used in aging and Alzheimer's disease (AD) studies. Majority of [¹⁸F]MK-6240 PET studies use dynamic acquisitions longer than 60 min to assess the tracer kinetic parameters. As of today, no consensus has been established on the optimum dynamic PET scan time. In this study, we assess the reproducibility of [¹⁸F]MK-6240 quantitative metrics using shortest dynamic PET protocols in cognitively normal subjects. PET metrics were measured through two-tissue compartment model (2TCM) and Logan model to estimate VT and DVR, as well as SUVR from 90 to 120 min (SUVR_{90 - 120 min}) post-tracer injection for brain regions. 2TCM was carried out using the 120 min dynamic coffee break dataset (first scan from 0 to 60 min p.i., second scan from 90 to 120 min p.i.) and then repeated after stepwise shortening it by 5 min. The dynamic scan length that reproduced the 120 min dynamic scans-based VT to within 10% error was defined as the shortest acquisition time (SAT). The SAT SUVR_{90 - 120 min} was deduced from the SAT dataset by extrapolation of each image pixel time-activity curve to 120 min. The reproducibility of the 120 min dynamic scans-based VT_2TCM, DVR_2TCM, DVR_Logan, and SUVR using the SAT was assessed using Passing-Bablock analysis. The limits of reproducibility of each PET metrics were determined using Bland-Altman analysis.</p><p><strong>Results: </strong>A dynamic SAT of 40 min yielded < 10% error in [¹⁸F]MK-6240 VT_2TCM's for all brain regions, compared to those measured using the 120 min datasets. SAT-based analysis did not show statistically significant systemic or proportional biases in VT_2TCM, DVR_2TCM, DVR_Logan, or SUVR compared to those deduced from the full dynamic dataset of 120 min. A mean difference between the 120 min- and SAT-based analysis of less than 4%, 10%, 15%, and 20% existed in the VT_2TCM, DVR_2TCM, DVR_Logan, and SUVR respectively.</p><p><strong>Conclusion: </strong>Kinetic modeling of [¹⁸F]MK-6240 PET can be accurately performed using dynamic scan times as short as 40 min. This can facilitate studies with [¹⁸F]MK-6240 PET and improve patients accrual. Further work would be necessary to confirm the reproducibility of these results for patients in dementia spectra.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"79"},"PeriodicalIF":3.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142343806","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}