Stability analysis of dynamic radiomics features for cardiac magnetic resonance

Introduction

This repository contains the source code for the paper

Stability of Dynamic Radiomics Features in Cardiac MRI by Mike D. Klaus, Fabian Laqua, Bettina Baeßler, and Markus J. Ankenbrand. In preparation

General workflow

Data sets

MRXCAT

Simulations with different SNR (5,10,20,30) using MRXCAT [1] version v1.4 and the breathhold phantom. Code is in code/mrxcat_simulations and simulation results in data/mrxcat_simulations. The simulations were performed with Matlab version R2021b. Simulation results have to be converted from the cpx format to the nifti format for further analysis. Steps include:

1. .cpx to .csv convertion
2. .csv to .nifti convertion The masks were then extracted from the breathhold phantom used for the simulation in MRXCAT.

ACDC

The ACDC dataset was published as part of the Automatic Cardiac Detection Challenge [2].

BAE

Data from the publication A systematic evaluation of three different cardiac T2-mapping sequences at 1.5 and 3T in healthy volunteers [3].

References

1. Wissmann, L., Santelli, C., Segars, W.P. et al. MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance. J Cardiovasc Magn Reson 16, 63 (2014). https://doi.org/10.1186/s12968-014-0063-3
2. Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Transactions on Medical Imaging, 37, 11, 2514-2525 (2018). https://doi.org/10.1109/TMI.2018.2837502
3. Baeßler, B. et al. A systematic evaluation of three different cardiac T2-mapping sequences at 1.5 and 3T in healthy volunteers. Eur. J. Radiol. 84, 2161–2170 (2015). https://doi.org/10.1016/j.ejrad.2015.08.002