medical-imaging

Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network

To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS).

Siyeop Yoon, Salah Assana

An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance

To develop and evaluate a free-breathing and electrocardiogram-free real-time cine with deep learning-based radial acceleration for Ex-CMR.

Manuel Morales, Salah Assana

Improving accuracy of myocardial T1 estimation in MyoMapNet

To improve the accuracy and robustness of T1 estimation by MyoMapNet, a deep learning–based approach using 4 inversion‐recovery T1‐weighted images for cardiac T1 mapping.

Rui Guo, Zhensen Chen, Amine Amyar, Hossam El‐Rewaidy, Salah Assana

Impact of deep learning architectures on accelerated cardiac T1 mapping using MyoMapNet

The objective of the current study was to investigate the performance of various deep learning architectures for MyoMapNet, a DL model for T1 estimation using accelerated cardiac T1 mapping from four T1‐weighted images collected after a single inversion pulse.

Amine Amyar, Rui Guo, Xiaoying Cai, Salah Assana

Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach

To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4).

Rui Guo, Hossam El-Rewaidy, Salah Assana