The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Michael C Iannuzzi - One of the best experts on this subject based on the ideXlab platform.
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results of 188 whole body fluorodeoxyglucose positron emission tomography scans in 137 patients with sarcoidosis
Chest, 2007Co-Authors: Alvin S Teirstein, Josef Machac, Orlandino D Almeida, Ping Lu, Maria L Padilla, Michael C IannuzziAbstract:Background To study the role of whole-body 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans in the identification of occult biopsy sites and reversible granulomatous disease in patients with sarcoidosis. Methods A retrospective review was undertaken of 188 FDG PET scans performed in 137 patients with proven sarcoidosis. All patients had given a complete medical history and undergone a physical examination, standard chest radiograph, spirometry, diffusing capacity determination, and measurement of serum angiotensin-converting enzymes levels. Results One hundred thirty-nine whole-body scans had positive findings. The most common positive sites were mediastinal lymph nodes (54 scans), extrathoracic lymph nodes (30 scans), and lung (24 scans). The standardized uptake value (SUV) ranged from 2.0 to 15.8. Twenty occult disease sites were identified. Eleven repeat scans exhibited decreased SUV with corticosteroid therapy. The positive pulmonary FDG PET scan findings occurred in two thirds of patients with radiographic stage II and III sarcoidosis. Negative pulmonary FDG PET scan findings were common in patients with radiographic stage 0, I, and IV sarcoidosis. Conclusions Whole-body FDG PET scans are of value in identifying occult and reversible granulomas in patients with sarcoidosis. However, a positive FDG PET scan finding, by itself, is not an indication for treatment.
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results of 188 whole body fluorodeoxyglucose positron emission tomography scans in 137 patients with sarcoidosis
Chest, 2007Co-Authors: Alvin S Teirstein, Josef Machac, Orlandino D Almeida, Maria L Padilla, Michael C IannuzziAbstract:Background To study the role of whole-body 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans in the identification of occult biopsy sites and reversible granulomatous disease in patients with sarcoidosis. Methods A retrospective review was undertaken of 188 FDG PET scans performed in 137 patients with proven sarcoidosis. All patients had given a complete medical history and undergone a physical examination, standard chest radiograph, spirometry, diffusing capacity determination, and measurement of serum angiotensin-converting enzymes levels. Results One hundred thirty-nine whole-body scans had positive findings. The most common positive sites were mediastinal lymph nodes (54 scans), extrathoracic lymph nodes (30 scans), and lung (24 scans). The standardized uptake value (SUV) ranged from 2.0 to 15.8. Twenty occult disease sites were identified. Eleven repeat scans exhibited decreased SUV with corticosteroid therapy. The positive pulmonary FDG PET scan findings occurred in two thirds of patients with radiographic stage II and III sarcoidosis. Negative pulmonary FDG PET scan findings were common in patients with radiographic stage 0, I, and IV sarcoidosis. Conclusions Whole-body FDG PET scans are of value in identifying occult and reversible granulomas in patients with sarcoidosis. However, a positive FDG PET scan finding, by itself, is not an indication for treatment.
Daniel L Rubin - One of the best experts on this subject based on the ideXlab platform.
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artificial intelligence enables whole body positron emission tomography scans with minimal radiation exposure
European Journal of Nuclear Medicine and Molecular Imaging, 2021Co-Authors: Yanran Joyce Wang, Lucia Baratto, Ashok J Theruvath, Allison Pribnow, Avnesh S Thakor, Sergios Gatidis, Santosh E Gummidipundi, Elizabeth K Hawk, Jordi Garciadiaz, Daniel L RubinAbstract:PURPOSE To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. RESULTS The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). CONCLUSIONS Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.
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artificial intelligence enables whole body positron emission tomography scans with minimal radiation exposure
European Journal of Nuclear Medicine and Molecular Imaging, 2021Co-Authors: Yanran Joyce Wang, Lucia Baratto, Ashok J Theruvath, Allison Pribnow, Avnesh S Thakor, Sergios Gatidis, Santosh E Gummidipundi, Elizabeth K Hawk, Jordi Garciadiaz, Daniel L RubinAbstract:To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3–30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers’ diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.
Alvin S Teirstein - One of the best experts on this subject based on the ideXlab platform.
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results of 188 whole body fluorodeoxyglucose positron emission tomography scans in 137 patients with sarcoidosis
Chest, 2007Co-Authors: Alvin S Teirstein, Josef Machac, Orlandino D Almeida, Ping Lu, Maria L Padilla, Michael C IannuzziAbstract:Background To study the role of whole-body 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans in the identification of occult biopsy sites and reversible granulomatous disease in patients with sarcoidosis. Methods A retrospective review was undertaken of 188 FDG PET scans performed in 137 patients with proven sarcoidosis. All patients had given a complete medical history and undergone a physical examination, standard chest radiograph, spirometry, diffusing capacity determination, and measurement of serum angiotensin-converting enzymes levels. Results One hundred thirty-nine whole-body scans had positive findings. The most common positive sites were mediastinal lymph nodes (54 scans), extrathoracic lymph nodes (30 scans), and lung (24 scans). The standardized uptake value (SUV) ranged from 2.0 to 15.8. Twenty occult disease sites were identified. Eleven repeat scans exhibited decreased SUV with corticosteroid therapy. The positive pulmonary FDG PET scan findings occurred in two thirds of patients with radiographic stage II and III sarcoidosis. Negative pulmonary FDG PET scan findings were common in patients with radiographic stage 0, I, and IV sarcoidosis. Conclusions Whole-body FDG PET scans are of value in identifying occult and reversible granulomas in patients with sarcoidosis. However, a positive FDG PET scan finding, by itself, is not an indication for treatment.
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results of 188 whole body fluorodeoxyglucose positron emission tomography scans in 137 patients with sarcoidosis
Chest, 2007Co-Authors: Alvin S Teirstein, Josef Machac, Orlandino D Almeida, Maria L Padilla, Michael C IannuzziAbstract:Background To study the role of whole-body 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans in the identification of occult biopsy sites and reversible granulomatous disease in patients with sarcoidosis. Methods A retrospective review was undertaken of 188 FDG PET scans performed in 137 patients with proven sarcoidosis. All patients had given a complete medical history and undergone a physical examination, standard chest radiograph, spirometry, diffusing capacity determination, and measurement of serum angiotensin-converting enzymes levels. Results One hundred thirty-nine whole-body scans had positive findings. The most common positive sites were mediastinal lymph nodes (54 scans), extrathoracic lymph nodes (30 scans), and lung (24 scans). The standardized uptake value (SUV) ranged from 2.0 to 15.8. Twenty occult disease sites were identified. Eleven repeat scans exhibited decreased SUV with corticosteroid therapy. The positive pulmonary FDG PET scan findings occurred in two thirds of patients with radiographic stage II and III sarcoidosis. Negative pulmonary FDG PET scan findings were common in patients with radiographic stage 0, I, and IV sarcoidosis. Conclusions Whole-body FDG PET scans are of value in identifying occult and reversible granulomas in patients with sarcoidosis. However, a positive FDG PET scan finding, by itself, is not an indication for treatment.
Yanran Joyce Wang - One of the best experts on this subject based on the ideXlab platform.
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artificial intelligence enables whole body positron emission tomography scans with minimal radiation exposure
European Journal of Nuclear Medicine and Molecular Imaging, 2021Co-Authors: Yanran Joyce Wang, Lucia Baratto, Ashok J Theruvath, Allison Pribnow, Avnesh S Thakor, Sergios Gatidis, Santosh E Gummidipundi, Elizabeth K Hawk, Jordi Garciadiaz, Daniel L RubinAbstract:PURPOSE To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. RESULTS The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). CONCLUSIONS Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.
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artificial intelligence enables whole body positron emission tomography scans with minimal radiation exposure
European Journal of Nuclear Medicine and Molecular Imaging, 2021Co-Authors: Yanran Joyce Wang, Lucia Baratto, Ashok J Theruvath, Allison Pribnow, Avnesh S Thakor, Sergios Gatidis, Santosh E Gummidipundi, Elizabeth K Hawk, Jordi Garciadiaz, Daniel L RubinAbstract:To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3–30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers’ diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.
Kohji Nishida - One of the best experts on this subject based on the ideXlab platform.
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prevalence and associated factors of segmentation errors in the peripapillary retinal nerve fiber layer and macular ganglion cell complex in spectral domain optical coherence tomography images
Journal of Glaucoma, 2017Co-Authors: Atsuya Miki, Miho Kumoi, Shinichi Usui, Takao Endo, Rumi Kawashima, Takeshi Morimoto, Kenji Matsushita, Takashi Fujikado, Kohji NishidaAbstract:PURPOSE To determine the prevalence of errors in segmentation of the peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC) boundary in spectral-domain optical coherence tomography (SDOCT) images, and to identify factors associated with the errors. MATERIALS AND METHODS Peripapillary RNFL circle scans and macular 3-dimensional scans of consecutive cases imaged with SDOCT (RS-3000 Advance; Nidek, Gamagori, Japan) were retrospectively reviewed by a glaucoma specialist. Images with signal strength index (SSI)<6 were excluded. Threshold for segmentation failure was determined as 15 degrees in the RNFL scans and 1/24 of the scanned area in the GCC scans. Relationships between segmentation failure and clinical factors were statistically evaluated with univariable and multivariable analyses. RESULTS This retrospective cross-sectional study included 207 eyes of 117 subjects (mean age, 58.5±16.5 y). Segmentation failure was found in 20.7% of the peripapillary RNFL scans, 16.6% of the 9 mm GCC scans, and 6.9% of the 6 mm GCC scans in SDOCT images. In multivariable logistic regression analyses, low SSI, large disc area, and disease type significantly correlated with RNFL segmentation failure, whereas SSI was the only baseline factor that was significantly associated with GCC segmentation failure. CONCLUSIONS Although segmentation failure was common in both RNFL and GCC scans, it was less frequently observed in GCC scans. SSI, disc area, and disease type were significantly associated with segmentation failure. Predictive performance of baseline factors for failure was poor, underlining the importance of reviewing raw OCT images before using OCT parameters.
