scholarly journals Corrections to “Deep Learning Based Multi-Modal Fusion for Fast MR Reconstruction” [Nov 18 2105-2114]

2020 ◽  
Vol 67 (9) ◽  
pp. 2705-2705
Author(s):  
Lei Xiang ◽  
Yong Chen ◽  
Wei-Tang Chang ◽  
Yiqiang Zhan ◽  
Weili Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Reconstruction

scholarly journals Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm

2021 ◽  
Author(s):  
Kelly C. Zochowski ◽  
Ek T. Tan ◽  
Erin C. Argentieri ◽  
Bin Lin ◽  
Alissa J. Burge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Peripheral Nerve ◽  
Reconstruction Algorithm ◽  
Mr Reconstruction

scholarly journals Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction

2019 ◽  
Vol 66 (7) ◽  
pp. 2105-2114 ◽  
Author(s):  
Lei Xiang ◽  
Yong Chen ◽  
Weitang Chang ◽  
Yiqiang Zhan ◽  
Weili Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Reconstruction

scholarly journals Improvement of Peripheral Nerve Visualization Using a Deep Learning-based MR Reconstruction Algorithm

2021 ◽  
Author(s):  
Kelly C. Zochowski ◽  
Ek Tsoon Tan ◽  
Erin C. Argentieri ◽  
Bin Lin ◽  
Alissa J. Burge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Peripheral Nerves ◽  
Image Interpretation ◽  
Reconstruction Algorithm ◽  
Standard Of Care ◽  
Interrater Agreement ◽  
Reconstruction Method ◽  
Reconstruction Methods ◽  
Bulk Motion ◽  
Mr Reconstruction

Abstract Objective: To assess a new deep learning-based MR reconstruction method, “DLRecon,” for clinical evaluation of peripheral nerves.Methods: Sixty peripheral nerves were prospectively evaluated in 29 patients (mean age: 49±16 years, 17 female) undergoing standard-of-care (SOC) MR neurography for clinically suspected neuropathy. SOC-MRIs and DLRecon-MRIs were obtained through conventional and DLRecon reconstruction methods, respectively. Two radiologists randomly evaluated blinded images for outer epineurium conspicuity, fascicular architecture visualization, pulsation artifact, ghosting artifact, and bulk motion. Results: DLRecon-MRIs were likely to score better than SOC-MRIs for outer epineurium conspicuity (OR=1.9, p=0.007) and visualization of fasicular architecture (OR=1.8, p<0.001) and were likely to score worse for ghosting (OR=2.8, p=0.004) and pulsation artifacts (OR=1.6, p=0.004). There was substantial to almost-perfect inter-reconstruction method agreement (AC=0.73-1.00) and fair to almost-perfect interrater agreement (AC=0.34-0.86) for all features evaluated. DLRecon-MRI had improved interrater agreement for outer epineurium conspicuity (AC=0.71, substantial agreement) compared to SOC-MRIs (AC=0.34, fair agreement). In >80% of images, the radiologist correctly identified an image as SOC- or DLRecon-MRI.Discussion: Outer epineurium and fasicular architecture conspicuity, two key morphological features critical to evaluating a nerve injury, were improved in DLRecon-MRIs compared to SOC-MRIs. Although pulsation and ghosting artifacts increased in DLRecon images, image interpretation was unaffected.

K-space refinement in deep learning MR reconstruction via regularizing scan specific SPIRiT-based self consistency

2021 ◽  
Author(s):  
Kanghyun Ryu ◽  
Cagan Alkan ◽  
Chanyeol Choi ◽  
Ikbeom Jang ◽  
Shreyas Vasanawala
Keyword(s):  
Deep Learning ◽  
Mr Reconstruction ◽  
Self Consistency

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning

Computertomografie: Bildrekonstruktion mit Deep Learning in der klinischen Routine

2020 ◽  
Author(s):  
A Heinrich ◽  
M Engler ◽  
D Dachoua ◽  
U Teichgräber ◽  
F Güttler
Keyword(s):  
Deep Learning
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