Pub Date : 2024-01-11DOI: 10.3389/fnume.2023.1327186
R. Frood, Julien M. Y. Willaime, Brad Miles, Greg Chambers, H’ssein Al-Chalabi, Tamir Ali, Natasha Hougham, Naomi Brooks, George Petrides, Matthew Naylor, Daniel Ward, Tom Sulkin, Richard Chaytor, Peter Strouhal, Chirag Patel, A. F. Scarsbrook
Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour.Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case.There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p < 0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p < 0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05).The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies.
{"title":"Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation","authors":"R. Frood, Julien M. Y. Willaime, Brad Miles, Greg Chambers, H’ssein Al-Chalabi, Tamir Ali, Natasha Hougham, Naomi Brooks, George Petrides, Matthew Naylor, Daniel Ward, Tom Sulkin, Richard Chaytor, Peter Strouhal, Chirag Patel, A. F. Scarsbrook","doi":"10.3389/fnume.2023.1327186","DOIUrl":"https://doi.org/10.3389/fnume.2023.1327186","url":null,"abstract":"Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour.Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case.There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p < 0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p < 0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05).The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139627094","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}
Pub Date : 2024-01-11DOI: 10.3389/fnume.2023.1362018
Jonathan Vigne, Giorgio Treglia
{"title":"Editorial: Molecular imaging of cardiovascular diseases: current and emerging approaches in nuclear medicine","authors":"Jonathan Vigne, Giorgio Treglia","doi":"10.3389/fnume.2023.1362018","DOIUrl":"https://doi.org/10.3389/fnume.2023.1362018","url":null,"abstract":"","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"53 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533930","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}
Pub Date : 2024-01-10DOI: 10.3389/fnume.2023.1323514
Anna Musket, Sandra Davern, Brianna M. Elam, Philip R. Musich, Jonathan P. Moorman, Yong Jiang
Radionuclide-mediated diagnosis and therapy have emerged as effective and low-risk approaches to treating breast cancer. Compared to traditional anatomic imaging techniques, diagnostic radionuclide-based molecular imaging systems exhibit much greater sensitivity and ability to precisely illustrate the biodistribution and metabolic processes from a functional perspective in breast cancer; this transitions diagnosis from an invasive visualization to a noninvasive visualization, potentially ensuring earlier diagnosis and on-time treatment. Radionuclide therapy is a newly developed modality for the treatment of breast cancer in which radionuclides are delivered to tumors and/or tumor-associated targets either directly or using delivery vehicles. Radionuclide therapy has been proven to be eminently effective and to exhibit low toxicity in eliminating both primary tumors and metastases, and even undetected tumors. In addition, the specific interaction between the surface modules of the delivery vehicles and the targets on the surface of tumor cells enables radionuclide targeting therapy, and this represents an exceptional potential for this treatment in breast cancer. This article reviews the development of radionuclide molecular imaging techniques that are currently employed for early breast cancer diagnosis and both the progress and challenges of radionuclide therapy employed in breast cancer treatment.
{"title":"The application of radionuclide therapy for breast cancer","authors":"Anna Musket, Sandra Davern, Brianna M. Elam, Philip R. Musich, Jonathan P. Moorman, Yong Jiang","doi":"10.3389/fnume.2023.1323514","DOIUrl":"https://doi.org/10.3389/fnume.2023.1323514","url":null,"abstract":"Radionuclide-mediated diagnosis and therapy have emerged as effective and low-risk approaches to treating breast cancer. Compared to traditional anatomic imaging techniques, diagnostic radionuclide-based molecular imaging systems exhibit much greater sensitivity and ability to precisely illustrate the biodistribution and metabolic processes from a functional perspective in breast cancer; this transitions diagnosis from an invasive visualization to a noninvasive visualization, potentially ensuring earlier diagnosis and on-time treatment. Radionuclide therapy is a newly developed modality for the treatment of breast cancer in which radionuclides are delivered to tumors and/or tumor-associated targets either directly or using delivery vehicles. Radionuclide therapy has been proven to be eminently effective and to exhibit low toxicity in eliminating both primary tumors and metastases, and even undetected tumors. In addition, the specific interaction between the surface modules of the delivery vehicles and the targets on the surface of tumor cells enables radionuclide targeting therapy, and this represents an exceptional potential for this treatment in breast cancer. This article reviews the development of radionuclide molecular imaging techniques that are currently employed for early breast cancer diagnosis and both the progress and challenges of radionuclide therapy employed in breast cancer treatment.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"7 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439474","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}
Pub Date : 2024-01-10DOI: 10.3389/fnume.2023.1292676
Lidia Delrieu, Damien Blanc, A. Bouhamama, Fabien Reyal, Frank Pilleul, Victor Racine, A. Hamy, Hugo Crochet, Timothée Marchal, Pierre Etienne Heudel
The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebrae and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Results were validated on an external independent group of CT scans.The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.
身体成分和肌肉疏松症对于癌症患者的预后和治疗耐受性非常重要，这一点已得到广泛认可，但由于需要耗费大量时间，常规评估并不多见。虽然 CT 扫描能提供精确的测量结果，但它们依赖于人工操作。我们开发并验证了一种深度学习算法，可自动选择和分割 CT 扫描中的腹部肌肉（SM）、内脏脂肪（VAT）和皮下脂肪（SAT）。第三腰椎和三种不同身体组织（内脏脂肪、内脏脂肪层和皮下脂肪）的检测均由人工标注。采用 5 倍交叉验证法开发算法，并在训练组群中验证其性能。自动 L3 切片选择算法在内部验证数据集和外部验证数据集上的平均绝对误差分别为 4 毫米和 5.5 毫米。在内部验证数据集中，身体成分的 DICE 相似系数中位数分别为 SM 0.94、VAT 0.93 和 SAT 0.86，而在外部验证数据集中，身体成分的 DICE 相似系数中位数分别为 SM 0.93、VAT 0.93 和 SAT 0.85。在内部和外部验证数据集中，我们的深度学习算法与肌肉疏松症指标的相关性都很高。我们的深度学习算法便于常规研究使用，并可集成到电子病历中，通过更好的监测和纳入有针对性的支持措施（如运动和营养）来加强护理。
{"title":"Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients","authors":"Lidia Delrieu, Damien Blanc, A. Bouhamama, Fabien Reyal, Frank Pilleul, Victor Racine, A. Hamy, Hugo Crochet, Timothée Marchal, Pierre Etienne Heudel","doi":"10.3389/fnume.2023.1292676","DOIUrl":"https://doi.org/10.3389/fnume.2023.1292676","url":null,"abstract":"The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebrae and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. Results were validated on an external independent group of CT scans.The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"81 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440521","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}
Pub Date : 2023-12-21DOI: 10.3389/fnume.2023.1342672
M. Petretta, C. Nappi, Alberto Cuocolo
{"title":"Editorial: Insights in PET and SPECT: 2023","authors":"M. Petretta, C. Nappi, Alberto Cuocolo","doi":"10.3389/fnume.2023.1342672","DOIUrl":"https://doi.org/10.3389/fnume.2023.1342672","url":null,"abstract":"","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"55 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166476","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}
Pub Date : 2023-12-14DOI: 10.3389/fnume.2023.1291253
Catherine Meyer, Laszlo Szidonya, Celeste Winters, Anna Mench, Nadine Mallak, Erik Mittra
PSMA-targeted radiopharmaceutical therapy is an established treatment option for metastatic castration-resistant prostate cancer (mCRPC). However, response rates and duration using 177Lu-PSMA-617 vary considerably between patients. Quantitative 177Lu SPECT imaging is one approach that may be leveraged to more closely monitor inter-cycle response, as well as patient-specific absorbed doses. In this work, we describe our experience implementing quantitative imaging throughout the course of 177Lu-PSMA treatment, including serial SPECT imaging to monitor response and for individualized dosimetry. We also describe our imaging protocols and dose calculation workflows for 3D voxelized patient-specific organ and tumor dosimetry, including a review of the current landscape and efforts towards harmonized dosimetry.
{"title":"Quantitative imaging for 177Lu-PSMA treatment response monitoring and dosimetry","authors":"Catherine Meyer, Laszlo Szidonya, Celeste Winters, Anna Mench, Nadine Mallak, Erik Mittra","doi":"10.3389/fnume.2023.1291253","DOIUrl":"https://doi.org/10.3389/fnume.2023.1291253","url":null,"abstract":"PSMA-targeted radiopharmaceutical therapy is an established treatment option for metastatic castration-resistant prostate cancer (mCRPC). However, response rates and duration using 177Lu-PSMA-617 vary considerably between patients. Quantitative 177Lu SPECT imaging is one approach that may be leveraged to more closely monitor inter-cycle response, as well as patient-specific absorbed doses. In this work, we describe our experience implementing quantitative imaging throughout the course of 177Lu-PSMA treatment, including serial SPECT imaging to monitor response and for individualized dosimetry. We also describe our imaging protocols and dose calculation workflows for 3D voxelized patient-specific organ and tumor dosimetry, including a review of the current landscape and efforts towards harmonized dosimetry.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"58 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139179721","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}
Pub Date : 2023-12-12DOI: 10.3389/fnume.2023.1306251
Milou Boswinkel, R. Raavé, A. Veltien, T. Scheenen, N. Fransén Petterson, René in ‘t Zandt, Lars E. Olsson, Karin von Wachenfeldt, Sandra Heskamp, Irma Mahmutovic Persson
Accurate imaging biomarkers that indicate disease progression at an early stage are highly important to enable timely mitigation of symptoms in progressive lung disease. In this context, reproducible experimental models and readouts are key. Here, we aim to show reproducibility of a lung injury rat model, by inducing disease and assessing disease progression by multi-modal non-invasive imaging techniques at two different research sites. Furthermore, we evaluated the potential of fibroblast activating protein (FAP) as an imaging biomarker in the early stage of lung fibrosis.An initial lung injury rat model was set up at one research site (Lund University, Lund, Sweden) and repeated at a second site (Radboudumc, Nijmegen, The Netherlands). To induce lung injury, Sprague-Dawley rats received intratracheal instillation of bleomycin as one single dose (1,000 iU in 200 µL) or saline as control. Thereafter, longitudinal images were acquired to track inflammation in the lungs, at 1 and 2 weeks after the bleomycin challenge by magnetic resonance imaging (MRI) and [18F]FDG-PET. After the final [18F]FDG-PET scan, rats received an intravenous tracer [89Zr]Zr-DFO-28H1 (anti-FAP antibody) and were imaged at day 15, to track fibrogenesis. Upon termination, bronchoalveolar lavage (BAL) was performed to assess cell and protein concentration. Subsequently, the biodistribution of [89Zr]Zr-DFO-28H1 was measured ex vivo and the spatial distribution in lung tissue was studied by autoradiography. Lung sections were stained, and fibrosis assessed using the modified Ashcroft score.Bleomycin-challenged rats showed body weight loss and increased numbers of immune cells and protein concentrations after BAL compared with control animals. The initiation and progression of the disease were reproduced at both research sites. Lung lesions in bleomycin-exposed rats were visualized by MRI and confirmed by histology. [18F]FDG uptake was higher in the lungs of bleomycin-challenged rats compared with the controls, similar to that observed in the Lund study. [89Zr]Zr-DFO-28H1 tracer uptake in the lung was increased in bleomycin-challenged rats compared with control rats (p = 0.03).Here, we demonstrate a reproducible lung injury model and monitored disease progression using conventional imaging biomarkers MRI and [18F]FDG-PET. Furthermore, we showed the first proof-of-concept of FAP imaging. This reproducible and robust animal model and imaging experimental set-up allows for future research on new therapeutics or biomarkers in lung disease.
{"title":"Utilizing MRI, [18F]FDG-PET and [89Zr]Zr-DFO-28H1 FAP-PET tracer to assess inflammation and fibrogenesis in a reproducible lung injury rat model: a multimodal imaging study","authors":"Milou Boswinkel, R. Raavé, A. Veltien, T. Scheenen, N. Fransén Petterson, René in ‘t Zandt, Lars E. Olsson, Karin von Wachenfeldt, Sandra Heskamp, Irma Mahmutovic Persson","doi":"10.3389/fnume.2023.1306251","DOIUrl":"https://doi.org/10.3389/fnume.2023.1306251","url":null,"abstract":"Accurate imaging biomarkers that indicate disease progression at an early stage are highly important to enable timely mitigation of symptoms in progressive lung disease. In this context, reproducible experimental models and readouts are key. Here, we aim to show reproducibility of a lung injury rat model, by inducing disease and assessing disease progression by multi-modal non-invasive imaging techniques at two different research sites. Furthermore, we evaluated the potential of fibroblast activating protein (FAP) as an imaging biomarker in the early stage of lung fibrosis.An initial lung injury rat model was set up at one research site (Lund University, Lund, Sweden) and repeated at a second site (Radboudumc, Nijmegen, The Netherlands). To induce lung injury, Sprague-Dawley rats received intratracheal instillation of bleomycin as one single dose (1,000 iU in 200 µL) or saline as control. Thereafter, longitudinal images were acquired to track inflammation in the lungs, at 1 and 2 weeks after the bleomycin challenge by magnetic resonance imaging (MRI) and [18F]FDG-PET. After the final [18F]FDG-PET scan, rats received an intravenous tracer [89Zr]Zr-DFO-28H1 (anti-FAP antibody) and were imaged at day 15, to track fibrogenesis. Upon termination, bronchoalveolar lavage (BAL) was performed to assess cell and protein concentration. Subsequently, the biodistribution of [89Zr]Zr-DFO-28H1 was measured ex vivo and the spatial distribution in lung tissue was studied by autoradiography. Lung sections were stained, and fibrosis assessed using the modified Ashcroft score.Bleomycin-challenged rats showed body weight loss and increased numbers of immune cells and protein concentrations after BAL compared with control animals. The initiation and progression of the disease were reproduced at both research sites. Lung lesions in bleomycin-exposed rats were visualized by MRI and confirmed by histology. [18F]FDG uptake was higher in the lungs of bleomycin-challenged rats compared with the controls, similar to that observed in the Lund study. [89Zr]Zr-DFO-28H1 tracer uptake in the lung was increased in bleomycin-challenged rats compared with control rats (p = 0.03).Here, we demonstrate a reproducible lung injury model and monitored disease progression using conventional imaging biomarkers MRI and [18F]FDG-PET. Furthermore, we showed the first proof-of-concept of FAP imaging. This reproducible and robust animal model and imaging experimental set-up allows for future research on new therapeutics or biomarkers in lung disease.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139182292","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}
Pub Date : 2023-12-11DOI: 10.3389/fnume.2023.1343913
T. Puri, Amelia E. B. Moore, Abhishek Mahajan, Alan McWilliam, M. Vrist, G. M. Blake
{"title":"Editorial: Quantitative [18F]NaF PET in metastatic and metabolic bone diseases","authors":"T. Puri, Amelia E. B. Moore, Abhishek Mahajan, Alan McWilliam, M. Vrist, G. M. Blake","doi":"10.3389/fnume.2023.1343913","DOIUrl":"https://doi.org/10.3389/fnume.2023.1343913","url":null,"abstract":"","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"26 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139183542","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}
Pub Date : 2023-11-27DOI: 10.3389/fnume.2023.1336481
C. Poulie, Markus Piel, Bernd Neumaier, Tobias Ross, Matthias Herth
{"title":"Editorial: Prof. Frank Rösch's legacy in the radiopharmaceutical chemistry field","authors":"C. Poulie, Markus Piel, Bernd Neumaier, Tobias Ross, Matthias Herth","doi":"10.3389/fnume.2023.1336481","DOIUrl":"https://doi.org/10.3389/fnume.2023.1336481","url":null,"abstract":"","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"176 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229711","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}
Pub Date : 2023-11-24DOI: 10.3389/fnume.2023.1194961
L. Watkins, Bryan Haddock, Ananya Goyal, Feliks Kogan
Accurately estimating bone perfusion and metabolism using [18F]NaF kinetics from shorter scan times could help address concerns related to patient comfort, motion, and throughput for PET scans. We examined the impact of changing the PET scan duration on the accuracy of [18F]NaF kinetic parameters in the knee.Both knees of twenty participants with and without osteoarthritis were scanned using a hybrid PET-MRI system (53 ± 13 years, BMI 25.9 ± 4.2 kg/m2, 13 female). Seventeen participants were scanned for 54 ± 2 min, and an additional three participants were scanned for 75 min. Patlak Ki and Hawkins kinetic parameters (Ki, K1, extraction fraction) were assessed using 50- or 75-minutes of scan data as well as for scan durations that were retrospectively shortened. The error of the kinetic uptake parameters was calculated in bone regions throughout the knee.The mean error of Patlak Ki, Hawkins Ki, K1, and extraction fraction was less than 10% for scan durations exceeding 30 min and decreased with increasing scan duration.The length of dynamic data acquisition can be reduced to as short as 30 min while retaining accuracy within the limits of reproducibility of Hawkins kinetic uptake parameters.
{"title":"Effects of dynamic [18F]NaF PET scan duration on kinetic uptake parameters in the knee","authors":"L. Watkins, Bryan Haddock, Ananya Goyal, Feliks Kogan","doi":"10.3389/fnume.2023.1194961","DOIUrl":"https://doi.org/10.3389/fnume.2023.1194961","url":null,"abstract":"Accurately estimating bone perfusion and metabolism using [18F]NaF kinetics from shorter scan times could help address concerns related to patient comfort, motion, and throughput for PET scans. We examined the impact of changing the PET scan duration on the accuracy of [18F]NaF kinetic parameters in the knee.Both knees of twenty participants with and without osteoarthritis were scanned using a hybrid PET-MRI system (53 ± 13 years, BMI 25.9 ± 4.2 kg/m2, 13 female). Seventeen participants were scanned for 54 ± 2 min, and an additional three participants were scanned for 75 min. Patlak Ki and Hawkins kinetic parameters (Ki, K1, extraction fraction) were assessed using 50- or 75-minutes of scan data as well as for scan durations that were retrospectively shortened. The error of the kinetic uptake parameters was calculated in bone regions throughout the knee.The mean error of Patlak Ki, Hawkins Ki, K1, and extraction fraction was less than 10% for scan durations exceeding 30 min and decreased with increasing scan duration.The length of dynamic data acquisition can be reduced to as short as 30 min while retaining accuracy within the limits of reproducibility of Hawkins kinetic uptake parameters.","PeriodicalId":505895,"journal":{"name":"Frontiers in Nuclear Medicine","volume":"148 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139240800","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}
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