{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "477cecdb", "metadata": {}, "source": [ "# Fat-water reconstruction algorithms" ] }, { "cell_type": "markdown", "id": "1ad9ddc4", "metadata": {}, "source": [ "## Table of evaluated state-of-the-art open-source fat-water reconstruction algorithms" ] }, { "cell_type": "code", "execution_count": 1, "id": "ba313695", "metadata": { "nbsphinx": "hidden", "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "import pandas as pd\n", "import os \n", "import yaml\n", "import glob\n", "import os.path as op" ] }, { "cell_type": "code", "execution_count": 2, "id": "7510394b", "metadata": { "nbsphinx": "hidden", "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "def convLink(name,url):\n", " return '{}'.format(url,name)" ] }, { "cell_type": "code", "execution_count": 3, "id": "0b326507", "metadata": { "nbsphinx": "hidden", "tags": [ "hide-input" ] }, "outputs": [], "source": [ "algoInfoYmlList=glob.glob('./../../../**/info_algo.yml')\n", "algoInfoList=[]\n", "for algoInfoFile in algoInfoYmlList:\n", " with open(op.abspath(algoInfoFile), 'r') as algoInfoYml:\n", " algoInfoDict=yaml.safe_load(algoInfoYml)\n", " algoInfoList.append(algoInfoDict)\n", "\n", "df=pd.DataFrame(algoInfoList)\n", "df['Code repository']=df.apply(lambda x: convLink(x['Code repository'],x['Code repository Url']),axis=1)\n", "df.drop(columns=[col for col in df.columns if col.find('Url')!=-1],axis=1,inplace=True)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "8c776891", "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reference Referred as Method Code 2D/3D Echo spacing Year Code repository main CREAM code
0Liu et al.B0-NICERegion-BasedMatlab3DUniform2015B0-Nice repositoryB0NICE_main
1Bydder et al.IDEAL-CEIDEALMatlab3DFree2020IDEAL-CE repositoryBydder_main
2Berglund et al.Fatty_RIOT_GCGraph-cutMatlab3DFree2012Fatty_RIOT_GC repositoryBerglund_main
3Andersson et al.MSCGA-AGraph-cutMatlab3DUniform2018MSCGA-A repositorySnubben_main
4Cui et al.GOOSEGraph-cutMatlab2DUniform/Free2015Goose repositoryGOOSE_main
5Berglund et al.MSCGA-BGraph-cutPython3DUniform2017MSCGA-B repositorymain
6Boehm et al.VGLCAGraph-cutMatlab3DUniform2021VGLCA repositoryGC_main
7Hernando et al.Hernado-GCGraph-cutMatlab2DUniform/Free2015Hernado-GC repositoryhernando_main
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.style" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 5 }