Liver T1 and T2 mapping in a large cohort of healthy subjects: normal ranges and correlation with age and sex
DISCUSSION: Liver T1 and T2 mapping is feasible and reproducible and the provided normal ranges may help to establish diagnosis and progression of various liver diseases.PMID:38019376 | DOI:10.1007/s10334-023-01135-6 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Antonella Meloni Aldo Carnevale Paolo Gaio Vincenzo Positano Cristina Passantino Alessia Pepe Andrea Barison Giancarlo Todiere Chrysanthos Grigoratos Giovanni Novani Laura Pistoia Melchiore Giganti Filippo Cademartiri Alberto Cossu Source Type: research
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images
CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.PMID:38019377 | DOI:10.1007/s10334-023-01133-8 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Chuanli Cheng Bingxia Wu Lei Zhang Qian Wan Hao Peng Xin Liu Hairong Zheng Huimao Zhang Chao Zou Source Type: research
Liver T1 and T2 mapping in a large cohort of healthy subjects: normal ranges and correlation with age and sex
DISCUSSION: Liver T1 and T2 mapping is feasible and reproducible and the provided normal ranges may help to establish diagnosis and progression of various liver diseases.PMID:38019376 | DOI:10.1007/s10334-023-01135-6 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Antonella Meloni Aldo Carnevale Paolo Gaio Vincenzo Positano Cristina Passantino Alessia Pepe Andrea Barison Giancarlo Todiere Chrysanthos Grigoratos Giovanni Novani Laura Pistoia Melchiore Giganti Filippo Cademartiri Alberto Cossu Source Type: research
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images
CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.PMID:38019377 | DOI:10.1007/s10334-023-01133-8 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Chuanli Cheng Bingxia Wu Lei Zhang Qian Wan Hao Peng Xin Liu Hairong Zheng Huimao Zhang Chao Zou Source Type: research
Liver T1 and T2 mapping in a large cohort of healthy subjects: normal ranges and correlation with age and sex
DISCUSSION: Liver T1 and T2 mapping is feasible and reproducible and the provided normal ranges may help to establish diagnosis and progression of various liver diseases.PMID:38019376 | DOI:10.1007/s10334-023-01135-6 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Antonella Meloni Aldo Carnevale Paolo Gaio Vincenzo Positano Cristina Passantino Alessia Pepe Andrea Barison Giancarlo Todiere Chrysanthos Grigoratos Giovanni Novani Laura Pistoia Melchiore Giganti Filippo Cademartiri Alberto Cossu Source Type: research
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images
CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.PMID:38019377 | DOI:10.1007/s10334-023-01133-8 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Chuanli Cheng Bingxia Wu Lei Zhang Qian Wan Hao Peng Xin Liu Hairong Zheng Huimao Zhang Chao Zou Source Type: research
Liver T1 and T2 mapping in a large cohort of healthy subjects: normal ranges and correlation with age and sex
DISCUSSION: Liver T1 and T2 mapping is feasible and reproducible and the provided normal ranges may help to establish diagnosis and progression of various liver diseases.PMID:38019376 | DOI:10.1007/s10334-023-01135-6 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Antonella Meloni Aldo Carnevale Paolo Gaio Vincenzo Positano Cristina Passantino Alessia Pepe Andrea Barison Giancarlo Todiere Chrysanthos Grigoratos Giovanni Novani Laura Pistoia Melchiore Giganti Filippo Cademartiri Alberto Cossu Source Type: research
Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images
CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.PMID:38019377 | DOI:10.1007/s10334-023-01133-8 (Source: Magma)
Source: Magma - November 29, 2023 Category: Radiology Authors: Chuanli Cheng Bingxia Wu Lei Zhang Qian Wan Hao Peng Xin Liu Hairong Zheng Huimao Zhang Chao Zou Source Type: research
Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI...
Source: Magma - November 22, 2023 Category: Radiology Authors: Rishabh Sharma Panagiotis Tsiamyrtzis Andrew G Webb Ernst L Leiss Nikolaos V Tsekos Source Type: research
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck
DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.PMID:37989922 | DOI:10.1007/s10334-023-01129-4 (Source: Magma)
Source: Magma - November 22, 2023 Category: Radiology Authors: Noriyuki Fujima Junichi Nakagawa Hiroyuki Kameda Yohei Ikebe Taisuke Harada Yukie Shimizu Nayuta Tsushima Satoshi Kano Akihiro Homma Jihun Kwon Masami Yoneyama Kohsuke Kudo Source Type: research
Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI...
Source: Magma - November 22, 2023 Category: Radiology Authors: Rishabh Sharma Panagiotis Tsiamyrtzis Andrew G Webb Ernst L Leiss Nikolaos V Tsekos Source Type: research
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck
DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.PMID:37989922 | DOI:10.1007/s10334-023-01129-4 (Source: Magma)
Source: Magma - November 22, 2023 Category: Radiology Authors: Noriyuki Fujima Junichi Nakagawa Hiroyuki Kameda Yohei Ikebe Taisuke Harada Yukie Shimizu Nayuta Tsushima Satoshi Kano Akihiro Homma Jihun Kwon Masami Yoneyama Kohsuke Kudo Source Type: research
Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI...
Source: Magma - November 22, 2023 Category: Radiology Authors: Rishabh Sharma Panagiotis Tsiamyrtzis Andrew G Webb Ernst L Leiss Nikolaos V Tsekos Source Type: research
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck
DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.PMID:37989922 | DOI:10.1007/s10334-023-01129-4 (Source: Magma)
Source: Magma - November 22, 2023 Category: Radiology Authors: Noriyuki Fujima Junichi Nakagawa Hiroyuki Kameda Yohei Ikebe Taisuke Harada Yukie Shimizu Nayuta Tsushima Satoshi Kano Akihiro Homma Jihun Kwon Masami Yoneyama Kohsuke Kudo Source Type: research
Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI...
Source: Magma - November 22, 2023 Category: Radiology Authors: Rishabh Sharma Panagiotis Tsiamyrtzis Andrew G Webb Ernst L Leiss Nikolaos V Tsekos Source Type: research
