Magnetic Resonance Imaging,

Significant correlations between human cortical bone mineral density and quantitative susceptibility mapping (QSM) obtained with 3D Cones ultrashort echo time magnetic resonance imaging (UTE-MRI)

Publication date: Available online 25 June 2019Source: Magnetic Resonance ImagingAuthor(s): Saeed Jerban, Xing Lu, Hyungseok Jang, Yajun Ma, Behnam Namiranian, Nicole Le, Ying Li, Eric Y. Chang, Jiang DuAbstractPurposeQuantitative susceptibility mapping (QSM) MRI is a tool that can characterize changes in susceptibility, an intrinsic property which is associated with compositional changes in the tissue. Current QSM estimation of cortical bone is challenging because conventional clinical MRI cannot...

Magnetic Resonance Imaging

17h

Dynamic contrast-enhanced MRI to assess hepatocellular carcinoma response to Transarterial chemoembolization using LI-RADS criteria: A pilot study

Publication date: Available online 25 June 2019Source: Magnetic Resonance ImagingAuthor(s): Alana Thibodeau-Antonacci, Léonie Petitclerc, Guillaume Gilbert, Laurent Bilodeau, Damien Olivié, Milena Cerny, Hélène Castel, Simon Turcotte, Catherine Huet, Pierre Perreault, Gilles Soulez, Miguel Chagnon, Samuel Kadoury, An TangAbstractPurposeTo identify quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters indicating tumor response of hepatocellular carcinoma (HCC) to transarterial chemoembolization...

Magnetic Resonance Imaging

17h

Brain Tissue Segmentation using Improved Kernelized Rough-Fuzzy C-Means with Spatio-Contextual Information from MRI

Publication date: Available online 25 June 2019Source: Magnetic Resonance ImagingAuthor(s): Anindya Halder, Nur Alom TalukdarAbstractSegmentation of brain tissues from MRI often becomes crucial to properly investigate any region of the brain in order to detect abnormalities. However, the accurate segmentation of the brain tissues is a challenging task as the different tissue regions are usually imprecise, indiscernible, ambiguous, and overlapping. Additionally, different tissue regions are non linearly...

Magnetic Resonance Imaging

17h

Anatomical context improves deep learning on the brain age estimation task

Publication date: Available online 24 June 2019Source: Magnetic Resonance ImagingAuthor(s): Camilo Bermudez, Andrew J. Plassard, Shikha Chaganti, Yuankai Huo, Katherine E. Aboud, Laurie E. Cutting, Susan M. Resnick, Bennett A. LandmanAbstractDeep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include...

Magnetic Resonance Imaging

17h