Journal: Magnetic resonance in medicine

Article Title: Open-Source MR Imaging and Reconstruction Workflow

doi: 10.1002/mrm.29384

Figure Lengend Snippet: Overview of the whole workflow with data acquisition at an MRI scanner (light blue) or in JEMRIS simulations (light green). Pulseq sequence and MRD metadata files are created with either PyPulseq or JEMRIS. The sequence file is executed at the scanner using a vendor-specific interpreter. Raw data are sent to the reconstruction server via the FIRE interface and the metadata from the MRD file are merged. Images are reconstructed with BART and are sent back to the scanner via FIRE. In an offline reconstruction, the FIRE interface is replaced by an MRD converter and a Python-based client. Acquired data from JEMRIS simulations is merged with the metadata inside JEMRIS and saved in the MRD format. The same reconstruction pipeline as for data from an MRI scanner data is executed.

Article Snippet: For identification of the files, the MD5 hash of the Pulseq sequence file is calculated and appended to both the sequence and the metadata file as a signature. . PyPulseq The PyPulseq toolbox implements the functionalities of the official Pulseq MATLAB toolbox in Python.

Techniques: Sequencing

Journal: Scientific Reports

Article Title: New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms

doi: 10.1038/s41598-020-72193-2

Figure Lengend Snippet: Boundary problems Real Life Example. Left: Adapted from Sarlis et al. paper . EMD decomposition of the magnitude time-series of GCMT collected from 1 January 1976 to 1 October 2014. The red boxes highlight artifact wave peaks at the boundaries of the IMFs, while the blue asterisks pinpoint anomalous IMFs amplitudes (larger than the original signal). Right: Decomposition of the GCMT magnitude time-series from January 1st 1976 to October 1st 2014 produced using the EEMD function released on March 04 2009 by Zhaohua Wu. The red line in each panel represents the zero reference line. Total computational time: 213.9069 s.

Article Snippet: The global earthquake magnitude time series is shown in the top row of Fig. . We run the decomposition of this signal using both the EMD algorithm, included in matlab distribution 2018a and later versions, and the eemd algorithm (it can be downloaded from the official website of the Taiwanese Research Center for Adaptive Data Analysis https://in.ncu.edu.tw/~ncu34951/research1.htm and is contained in the repository https://in.ncu.edu.tw/~ncu34951/Matlab_runcode.zip ) written by Zhaohua Wu in 2009 .

Techniques: Produced