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    • SCR-DL

      Public
      Accelerated Cardiac Cine with Spatio-Coil Regularized Deep Learning Reconstruction
      Python
      0000Updated Oct 21, 2024Oct 21, 2024
    • Myocardial Scar Enhancement in LGE Cardiac MRI using Localized Diffusion
      Python
      0200Updated Jul 8, 2024Jul 8, 2024
    • Python
      0000Updated Jun 20, 2024Jun 20, 2024
    • FastCSE

      Public
      Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network
      Python
      0000Updated Jun 4, 2024Jun 4, 2024
    • CRISPFlow

      Public
      Accelerated Phase Contrast MRI with Use of Resolution Enhancement Generative Adversarial Neural Network
      Python
      MIT License
      0000Updated Mar 30, 2024Mar 30, 2024
    • Python
      0000Updated Mar 13, 2024Mar 13, 2024
    • CineROI

      Public
      An open-source, flexible, plug-and-play inline CMR segmentation platform for the rapid deployment of ML models into the clinical workflow.
      Python
      0000Updated Nov 12, 2023Nov 12, 2023
    • Python
      0100Updated Sep 8, 2023Sep 8, 2023
    • Code for the paper "Gadolinium-Free Cardiac MRI Myocardial Scar Detection by 4D Convolution Factorization" MICCAI2023
      Python
      MIT License
      0500Updated Jul 20, 2023Jul 20, 2023
    • REGAIN

      Public
      The repository contains a source code for Resolution-Enhancement-Generative-Adversarial-Inline-Network (REGAIN). REGAIN is a generative adversarial neural network for REGAINing image sharpness and Spatial resolution. The trained network generates a resolution-enhanced image in the Phase-encoding (ky) direction in MRI.
      Python
      MIT License
      1100Updated May 9, 2023May 9, 2023
    • Jupyter Notebook
      0000Updated Apr 4, 2023Apr 4, 2023
    • DENT

      Public
      We developed a highly accelerated high-frame-rate cine for Ex-CMR by accelerating spatial resolution using REGAIN, followed by synthesizing new frames using Deformation ENcoding Transformer (DENT).
      Jupyter Notebook
      MIT License
      0010Updated Mar 2, 2023Mar 2, 2023
    • DRAPR

      Public
      We implemented a 3D (2D+time) convolutional neural network to suppress streaking artifacts from undersampled radial cine images. We trained the network using synthetic real-time radial cine images simulated using ECG-gated segmented Cartesian k-space data, which was acquired from 503 patients during breath-hold and at rest. Further, we implement…
      Jupyter Notebook
      47700Updated Feb 13, 2023Feb 13, 2023
    • Python
      0000Updated Jan 19, 2023Jan 19, 2023
    • 0000Updated Jan 17, 2023Jan 17, 2023
    • MyoMapNet

      Public
      We implemented a FC that uses pixel-wise T1-weighted signals and corresponding inversion time to estimate T1 values from a limited number of T1-weighted images. we studied how training the model using native, post-contrast T1 and a combination of both could impact performance of the MyoMapNet. We also explored two choices of number of T1 weighte…
      Python
      MIT License
      441500Updated Dec 27, 2022Dec 27, 2022
    • Python
      0300Updated Dec 7, 2022Dec 7, 2022
    • The repository contains a source code for measuring image sharpness. Image sharpness is defined as the absolute gradient of intensity profile extracted from the normalized image.
      Python
      1500Updated Nov 10, 2022Nov 10, 2022
    • Python code used to investigate the prognostic value of Radiomics analysis of LGE in predicting sudden cardiac death (SCD) in HCM patients..
      Python
      0000Updated Oct 13, 2022Oct 13, 2022
    • Code for identifying patients without scar using DL and Radiomics analyses of non-Gd bSSFP cine sequences.
      Python
      0100Updated Apr 22, 2022Apr 22, 2022
    • Code for Fahmy et al. "An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular Dysfunction in Nonischemic Cardiomyopathy". JACC Cardiovasc Imaging. 2022 doi: 10.1016/j.jcmg.2021.11.029.
      Python
      0000Updated Feb 18, 2022Feb 18, 2022
    • We implemented and tested three different classes of deep learning architectures for MyoMapNet: (a) a fully connected neural network (FC), (b) convolutional neural networks (VGG19, ResNet50), and (c) encoder-decoder networks with skip connections (ResUNet, U-Net). Modified Look-Locker Inversion Recovery (MOLLI) images from 749 patients at 3T wer…
      Python
      0300Updated Jan 9, 2022Jan 9, 2022