gistfile1.txt

{"nbformat_minor": 0, "cells": [{"source": "Data Conversion\n\n    siemens_to_ismrmrd -f meas_MID00218_FID33531_OLD_SNR_COR_RL_R1.dat -z 1 -o meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5\n    siemens_to_ismrmrd -f meas_MID00218_FID33531_OLD_SNR_COR_RL_R1.dat -z 2 -o meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5\n    siemens_to_ismrmrd -f meas_MID00229_FID33542_NEW_SNR_COR_RL_R1.dat -z 1 -o meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5\n    siemens_to_ismrmrd -f meas_MID00229_FID33542_NEW_SNR_COR_RL_R1.dat -z 2 -o meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5\n    siemens_to_ismrmrd -f meas_MID00241_FID33554_SIE_SNR_COR_RL_R1.dat -z 1 -o meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5\n    siemens_to_ismrmrd -f meas_MID00241_FID33554_SIE_SNR_COR_RL_R1.dat -z 2 -o meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5\n", "cell_type": "markdown", "metadata": {}}, {"execution_count": 107, "cell_type": "code", "source": "\nimport os\nimport sys\nimport ismrmrd\nimport ismrmrd.xsd\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as pp\nimport dicom\n\nsys.path.append(os.environ['GADGETRON_HOME'] + '/share/gadgetron/python')\n\nimport gadgetron_python_to_xml as p2x\nimport gadgetron_xml_to_python as x2p\nfrom gadgetron import gadget_chain_wait\nfrom gadgetron import gadget_chain_config\nfrom gadgetron import get_last_gadget\n\n\nfrom ismrmrdtools import show, transform, coils, grappa, sense\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 108, "cell_type": "code", "source": "def collect_data(filename_noise, filename_data):\n\n    # Read the noise data\n    if not os.path.isfile(filename_noise):\n        print(\"%s is not a valid file\" % filename_noise)\n        raise Exception('Invalid filename')\n\n    noise_dset = ismrmrd.Dataset(filename_noise, 'dataset', create_if_needed=False)\n\n\n    # Process the noise data\n    noise_reps = noise_dset.number_of_acquisitions()\n    a = noise_dset.read_acquisition(0)\n    noise_samples = a.number_of_samples\n    num_coils = a.active_channels\n    noise_dwell_time = a.sample_time_us\n\n    noise = np.zeros((num_coils,noise_reps*noise_samples),dtype=np.complex64)\n    for acqnum in range(noise_reps):\n        acq = noise_dset.read_acquisition(acqnum)\n    \n    if not acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n       raise Exception(\"Errror: non noise scan found in noise calibration\")\n\n    noise[:,acqnum*noise_samples:acqnum*noise_samples+noise_samples] = acq.data\n    \n    noise = noise.astype('complex64')\n    \n    # Read the data\n    if not os.path.isfile(filename_data):\n        print(\"%s is not a valid file\" % filename_data)\n        raise Exception('Invalid filename')\n\n    dset = ismrmrd.Dataset(filename_data, 'dataset', create_if_needed=False)\n\n    header = ismrmrd.xsd.CreateFromDocument(dset.read_xml_header())\n    enc = header.encoding[0]\n\n    # Matrix size\n    eNx = enc.encodedSpace.matrixSize.x\n    eNy = enc.encodedSpace.matrixSize.y\n    eNz = enc.encodedSpace.matrixSize.z\n    rNx = enc.reconSpace.matrixSize.x\n    rNy = enc.reconSpace.matrixSize.y\n    rNz = enc.reconSpace.matrixSize.z\n\n    # Field of View\n    eFOVx = enc.encodedSpace.fieldOfView_mm.x\n    eFOVy = enc.encodedSpace.fieldOfView_mm.y\n    eFOVz = enc.encodedSpace.fieldOfView_mm.z\n    rFOVx = enc.reconSpace.fieldOfView_mm.x\n    rFOVy = enc.reconSpace.fieldOfView_mm.y\n    rFOVz = enc.reconSpace.fieldOfView_mm.z\n\n    #Parallel imaging factor\n    acc_factor = enc.parallelImaging.accelerationFactor.kspace_encoding_step_1\n\n    # Number of Slices, Reps, Contrasts, etc.\n    ncoils = header.acquisitionSystemInformation.receiverChannels\n    if enc.encodingLimits.slice != None:\n        nslices = enc.encodingLimits.slice.maximum + 1\n    else:\n        nslices = 1\n\n    if enc.encodingLimits.repetition != None:\n        nreps = enc.encodingLimits.repetition.maximum + 1\n    else:\n        nreps = 1\n\n    if enc.encodingLimits.contrast != None:\n        ncontrasts = enc.encodingLimits.contrast.maximum + 1\n    else:\n        ncontrasts = 1\n    \n    # In case there are noise scans in the actual dataset, we will skip them. \n    firstacq=0\n    for acqnum in range(dset.number_of_acquisitions()):\n        acq = dset.read_acquisition(acqnum)\n\n        if acq.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n            print \"Found noise scan at acq \", acqnum\n            continue\n        else:\n            firstacq = acqnum\n            print \"Imaging acquisition starts acq \", acqnum\n            break\n\n    #Calculate prewhiterner taking BWs into consideration\n    a = dset.read_acquisition(firstacq)\n    data_dwell_time = a.sample_time_us\n    noise_receiver_bw_ratio = 0.79\n    dmtx = coils.calculate_prewhitening(noise,scale_factor=(data_dwell_time/noise_dwell_time)*noise_receiver_bw_ratio)\n\n    \n    #%%\n    # Process the actual data\n    all_data = np.zeros((nreps, ncontrasts, nslices, ncoils, eNz, eNy, rNx), dtype=np.complex64)\n\n    # Loop through the rest of the acquisitions and stuff\n    for acqnum in range(firstacq,dset.number_of_acquisitions()):\n        acq = dset.read_acquisition(acqnum)\n\n        acq_data_prw = coils.apply_prewhitening(acq.data,dmtx)\n\n        # Remove oversampling if needed\n        if eNx != rNx:\n            xline = transform.transform_kspace_to_image(acq_data_prw, [1])\n            x0 = (eNx - rNx) / 2\n            x1 = (eNx - rNx) / 2 + rNx\n            xline = xline[:,x0:x1]\n            acq.resize(rNx,acq.active_channels,acq.trajectory_dimensions)\n            acq.center_sample = rNx/2\n            # need to use the [:] notation here to fill the data\n            acq.data[:] = transform.transform_image_to_kspace(xline, [1])\n\n        # Stuff into the buffer\n        rep = acq.idx.repetition\n        contrast = acq.idx.contrast\n        slice = acq.idx.slice\n        y = acq.idx.kspace_encode_step_1\n        z = acq.idx.kspace_encode_step_2\n        all_data[rep, contrast, slice, :, z, y, :] = acq.data\n\n    all_data = all_data.astype('complex64')\n\n    return all_data", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 109, "cell_type": "code", "source": "def combine(all_data):\n    all_data = transform.transform_image_to_kspace(np.squeeze(all_data),(2,3))\n    \n    nslices = all_data.shape[0]\n    ncoils = all_data.shape[1]\n    \n    recon_b1w = np.zeros((nslices, all_data.shape[2], all_data.shape[3]), dtype=np.complex64)\n    recon_rss = np.zeros((nslices, all_data.shape[2], all_data.shape[3]), dtype=np.complex64)\n    \n    for s in range(nslices):\n        (csm,rho) = coils.calculate_csm_walsh(all_data[s,:,:,:])\n        recon_b1w[s,:,:] = np.sum(all_data[s,:,:,:] * np.conj(csm),0)\n        recon_rss[s,:,:] = np.sqrt(np.sum(all_data[s,:,:,:]**2,0))\n        \n    return (recon_b1w, recon_rss)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 110, "cell_type": "code", "source": "data_folder = '/home/hansenms/data/QED_COIL/large/QEDTest20150415/'", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "all_data_old = recon_data(data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5')\nall_data_new = recon_data(data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5')\nall_data_sie = recon_data(data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5' , data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5')", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Imaging acquisition starts acq  0\nImaging acquisition starts acq  0\nImaging acquisition starts acq  0\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "(recon_b1w_old, recon_rss_old) = combine(all_data_old)\nrecon_b1w_old = recon_b1w_old[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_old = recon_rss_old[(5,0,6,1,7,2,8,3,9,4),:,:]\n(recon_b1w_new, recon_rss_new) = combine(all_data_new)\nrecon_b1w_new = recon_b1w_new[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_new = recon_rss_new[(5,0,6,1,7,2,8,3,9,4),:,:]\n(recon_b1w_sie, recon_rss_sie) = combine(all_data_sie)\nrecon_b1w_sie = recon_b1w_sie[(5,0,6,1,7,2,8,3,9,4),:,:]\nrecon_rss_sie = recon_rss_sie[(5,0,6,1,7,2,8,3,9,4),:,:]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 127, "cell_type": "code", "source": "mask = np.zeros((256,256))\nmask[64:192,20+64:20+192] = 1\n\nnslices = recon_b1w_old.shape[0]\ntmp = np.zeros((nslices,))\nsnr_b1w_old = np.sum(abs(recon_b1w_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_old = np.sum(abs(recon_rss_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_b1w_new = np.sum(abs(recon_b1w_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_new = np.sum(abs(recon_rss_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_b1w_sie = np.sum(abs(recon_b1w_sie)*mask,axis=(1,2)) / np.sum(mask)\nsnr_rss_sie = np.sum(abs(recon_rss_sie)*mask,axis=(1,2)) / np.sum(mask)\n\nshow.imshow(abs(recon_b1w_old[1,:,:])*mask)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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l3UgzW235aPcGmxkG7Z7b/Ig/Tl9TzXPenR/ATnOvDxi3e6qkJH0wFF8YqKD+CXSfEtKz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\nUUyATaoF2B2CGyoCW6pzxwln1BJjg0CBPVZcMCyCnGG4qB+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"text/plain": "<matplotlib.figure.Figure at 0x7f7a9f19ae90>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 128, "cell_type": "code", "source": "pp.plot(snr_b1w_new,'r')\npp.plot(snr_b1w_old,'b')\npp.plot(snr_b1w_sie,'g')", "outputs": [{"execution_count": 128, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7a9f097c90>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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I6qEmJLYB/oe0PO/xNreX2dAX3+zDmV/7DPddvhnjEZcDpznn+as8N4Q6FEk9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\nQieUXZ37EZok/14sHdGfdzc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"text/plain": "<matplotlib.figure.Figure at 0x7f7abd4e9210>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 129, "cell_type": "code", "source": "pp.plot(snr_rss_new,'r')\npp.plot(snr_rss_old,'b')\npp.plot(snr_rss_sie,'g')", "outputs": [{"execution_count": 129, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7a9eff9550>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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AAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7a9f052f10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 150, "cell_type": "code", "source": "pp.figure(figsize=(15,10))\npp.plot(snr_b1w_new,'r',label=\"b1w new\")\npp.plot(snr_b1w_old,'b',label=\"b1w old\")\npp.plot(snr_b1w_sie,'g',label=\"b1w siemens\")\npp.plot(snr_rss_new,'--r',label=\"rss new\")\npp.plot(snr_rss_old,'--b',label=\"rss old\")\npp.plot(snr_rss_sie,'--g',label=\"rss siemens\")\npp.legend()\n", "outputs": [{"execution_count": 150, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.legend.Legend at 0x7f7a9edb5650>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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rSnpvXyO3jPVlSRtWY71c0k69OA9JO0l6dfXyyfn9XIMlxVoMvdKuPhyYWHeU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\nIIJRNa+Z5rGdRrqLtlmn5rFJvYC7gNeJOKDWLGZmZmbNVHO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tef2PczA/4jfAuZHFl0XH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Dy5YDccOMIzvgHe+j6MwzbsxH/s89+sHNnK8jH9WT5uS1YPe5P1/V/kmRMehKbCMDenXAivzhfR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\nAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7a9e6bf590>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 130, "cell_type": "code", "source": "pp.plot(snr_b1w_new/snr_b1w_old,'r')\npp.plot(snr_rss_new/snr_rss_old,'g')", "outputs": [{"execution_count": 130, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7a9ef3fd50>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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8mVk/qdDywE6khL8T8Dgp2d9E6t2/kDG8eTjBm5kNQrkh22akyv4jwChgFjAJ\nmFx+vTtq8Vq2GJ3gzcyGrmznrAtsUV42B9YG/kxK9p2J//5GzcF3gjczq5Nyde0oUrLvTPorAHfS\nVeVPAh6tx6ZpTvBmZg2kQsuQWjudSX8LYAHmbO1MHo6tFYac4CWNA3YDnoyIDXt5zOmkQYlXgf0j\nYmp5+xjgx8AI4JyIOHmwQZqZtaJy6+SVmLO1sxlpf/zurZ27ohavDui5hyHBb0c6yWV8Twle0q7A\n4RGxq6QtgdMiYitJI4C/kOaZzir/AHtFxJ8HE2SjSRodER254+jOMfVfM8blmPqnHWIqD1BZmzmr\n/PWBB5kz6d/T26pbSaOBW+aXO0f2dWdE3CpptT4esgdwQfnYOyQtLWkFYHXgwYh4pAzmUuDjpAGJ\nVjAa6Mgcw9xG45j6azTNF9doHFN/jKbiMZXbHP+5vIyHf++nsxEp2W8DHAWsqkLTmLOf/1DZzx/d\nn9fqM8H3w0rAo92uzyxvW7GH27cc4muZmVVS1OJ1UiKf3HmbCr0D2JSU9D8FnAwsrkKTWZ9FuXf+\nzzvUBA/QVO0VM7MqiFq8CNxSXgBQoRWAzXmbr/XnOeY7i6Zs0VzTSw/+DKAjIi4tr99P2pt5dWBs\nRIwpb/8O8HZPA62SfOaimdkgDKkH3w9XA4cDl0raCng+ImZLegZYs3xzeAz4HLDXYAI0M7PB6TPB\nS5pAqsiXlfQoUAMWBIiIMyPiOkm7SnoQeAU4oLzvLUmHk47UGgGc29MMGjMzq5/sC53MzKw+Fsj1\nwpLGSLpf0gOSjskVR3eSxkmaLWlG7lg6SVpF0i2S7pV0j6SvNkFMi0i6Q9I0SfdJOjF3TJ0kjZA0\nVdI1uWMBkPSIpOllTJNyxwNQTme+TNKfy9/fVk0Q09rl31Hn5YUm+bf+nfL/3gxJl0hauAliOrKM\n5x5JR/b54Iho+IXUtnkQWI3U8pkGrJsjlrni2o60t8SM3LF0i2kFYOPy+yVIC8ia4e9qsfLrSOBP\nwLa5Yyrj+TpwMXB17ljKeB4Glskdx1wxXQAc2O33t1TumOaKbwHSVr2rZI5jNeAhYOHy+v8C+2WO\naQNgBrBImUdvAtbo7fG5KvgtKBdCRcSbQOdCqKwi4lbgudxxdBcRT0TEtPL7l0mLI1bMGxVE/HtZ\n9UKkf2jPZgwHAEkrA7sC59Bc03ebJhZJSwHbRcQ4SONlEc21zzlpBfzfIuLR+T6yvl4E3gQWkzQS\nWIy0Mj8jksTTAAACgElEQVSndYA7IuK1iPgX8Dtgz94enCvB97ZAyvpQzkoaBdyRNxKQtICkacBs\n0pLp+3LHBPwI+Bbwdu5AuglgoqQpkg7JHQxpCvNTks6TdJeksyUtljuoufwHcEnuICLiWeBU4B+k\n2YDPR8TEvFFxD7CdpGXK39tuwMq9PThXgvfI7gBJWgK4DDiyrOSzioi3I2Jj0j+uD5V7Y2Qj6WOk\nTfGm0kQVM7BNRIwibch3WLm/U04jgU2An0fEJqTZb9/OG1IXSQsBuwO/aoJY1gC+RmrVrAgsIWnv\nnDFFxP2kFa03kk6bmkofBU2uBD+LdLp5p1VIVbz1QNKCwOXARRFxVe54uis/3l9L2iEvpw8Ce0h6\nGJgAfFjS+MwxERGPl1+fAq4ktSdzmgnMjIjOJfGXkRJ+s9gFuLP8+8ptM+C2iHgmIt4CriD9O8sq\nIsZFxGYRsT3wPGlcrke5EvwUyoVQ5Tv250iLpmwukgScC9wXET/OHQ+ApGUlLV1+vyjwUVIlkU1E\nfDciVomI1Ukf8W+OiC/kjEnSYpKWLL9fnHTOZ9YZWhHxBPCopLXKm3aE/uxq0jB7kd6gm8H9wFaS\nFi3/H+4IZG9FSnp3+XVV4JP00c4ajr1oBiyadCFUt4Vd7yoXdh0fEedlDmsbYB9guqTOJPqdiLgh\nY0zvAS6QtACpSLgwIn6bMZ6eNEMbcHngypQbGAlcHBE35g0JgCOAi8vi6m+UCxRzK98EdwSaYayC\niLi7/BQ4hdQGuQs4K29UAFwm6V2kAeCvRMSLvT3QC53MzCoq20InMzOrLyd4M7OKcoI3M6soJ3gz\ns4pygjczqygneDOzinKCNzOrKCd4M7OK+n+karQEiQS5kQAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7a9ef3fcd0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 131, "cell_type": "code", "source": "\n# Automatically generated Python representation of /home/hansenms/local/share/gadgetron/config/default_measurement_dependencies.xml\n\nfrom gadgetron import WrapperGadget\n\ndef define_gadget_chain_dependencies():\n    g2 = WrapperGadget(\"gadgetron_mricore\", \"NoiseAdjustGadget\", gadgetname=\"NoiseAdjust\", next_gadget=None)\n    g2.set_parameter(\"NoiseAdjust\", \"noise_dependency_prefix\", \"GadgetronNoiseCovarianceMatrix\")\n    g2.set_parameter(\"NoiseAdjust\", \"pass_nonconformant_data\", \"true\")\n    return g2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 132, "cell_type": "code", "source": "# Automatically generated Python representation of /home/hansenms/local/share/gadgetron/config/GT_2DT_Cartesian_GFactor.xml\n\nfrom gadgetron import WrapperGadget\n\ndef define_gadget_chain_gfactor():\n    g2 = WrapperGadget(\"gadgetronPlus\", \"GtPlusRecon2DTGadget\", gadgetname=\"Recon\", next_gadget=None)\n    g2.set_parameter(\"Recon\", \"dim_4th\", \"DIM_NONE\")\n    g2.set_parameter(\"Recon\", \"dim_5th\", \"DIM_NONE\")\n    g2.set_parameter(\"Recon\", \"workOrder_ShareDim\", \"DIM_NONE\")\n    g2.set_parameter(\"Recon\", \"no_acceleration_averageall_ref\", \"true\")\n    g2.set_parameter(\"Recon\", \"no_acceleration_ref_numOfModes\", \"0\")\n    g2.set_parameter(\"Recon\", \"no_acceleration_same_combinationcoeff_allS\", \"true\")\n    g2.set_parameter(\"Recon\", \"no_acceleration_whichS_combinationcoeff\", \"0\")\n    g2.set_parameter(\"Recon\", \"interleaved_same_combinationcoeff_allS\", \"false\")\n    g2.set_parameter(\"Recon\", \"interleaved_whichS_combinationcoeff\", \"0\")\n    g2.set_parameter(\"Recon\", \"interleaved_ref_numOfModes\", \"0\")\n    g2.set_parameter(\"Recon\", \"embedded_averageall_ref\", \"true\")\n    g2.set_parameter(\"Recon\", \"embedded_ref_numOfModes\", \"0\")\n    g2.set_parameter(\"Recon\", \"embedded_fullres_coilmap\", \"true\")\n    g2.set_parameter(\"Recon\", \"embedded_fullres_coilmap_useHighestSignal\", \"false\")\n    g2.set_parameter(\"Recon\", \"embedded_same_combinationcoeff_allS\", \"true\")\n    g2.set_parameter(\"Recon\", \"embedded_whichS_combinationcoeff\", \"0\")\n    g2.set_parameter(\"Recon\", \"embedded_ref_fillback\", \"false\")\n    g2.set_parameter(\"Recon\", \"separate_averageall_ref\", \"true\")\n    g2.set_parameter(\"Recon\", \"separate_ref_numOfModes\", \"0\")\n    g2.set_parameter(\"Recon\", \"separate_fullres_coilmap\", \"true\")\n    g2.set_parameter(\"Recon\", \"separate_same_combinationcoeff_allS\", \"true\")\n    g2.set_parameter(\"Recon\", \"separate_whichS_combinationcoeff\", \"0\")\n    g2.set_parameter(\"Recon\", \"same_coil_compression_coeff_allS\", \"true\")\n    g2.set_parameter(\"Recon\", \"upstream_coil_compression\", \"false\")\n    g2.set_parameter(\"Recon\", \"upstream_coil_compression_thres\", \"-1\")\n    g2.set_parameter(\"Recon\", \"upstream_coil_compression_num_modesKept\", \"-1\")\n    g2.set_parameter(\"Recon\", \"downstream_coil_compression\", \"true\")\n    g2.set_parameter(\"Recon\", \"coil_compression_thres\", \"-1\")\n    g2.set_parameter(\"Recon\", \"coil_compression_num_modesKept\", \"-1\")\n    g2.set_parameter(\"Recon\", \"coil_map_algorithm\", \"ISMRMRD_SOUHEIL\")\n    g2.set_parameter(\"Recon\", \"csm_kSize\", \"7\")\n    g2.set_parameter(\"Recon\", \"csm_powermethod_num\", \"3\")\n    g2.set_parameter(\"Recon\", \"csm_true_3D\", \"false\")\n    g2.set_parameter(\"Recon\", \"csm_iter_num\", \"5\")\n    g2.set_parameter(\"Recon\", \"csm_iter_thres\", \"0.001\")\n    g2.set_parameter(\"Recon\", \"recon_algorithm\", \"ISMRMRD_GRAPPA\")\n    g2.set_parameter(\"Recon\", \"recon_kspace_needed\", \"false\")\n    g2.set_parameter(\"Recon\", \"recon_auto_parameters\", \"true\")\n    g2.set_parameter(\"Recon\", \"gfactor_needed\", \"true\")\n    g2.set_parameter(\"Recon\", \"wrap_around_map_needed\", \"false\")\n    g2.set_parameter(\"Recon\", \"grappa_kSize_RO\", \"5\")\n    g2.set_parameter(\"Recon\", \"grappa_kSize_E1\", \"4\")\n    g2.set_parameter(\"Recon\", \"grappa_kSize_E2\", \"4\")\n    g2.set_parameter(\"Recon\", \"grappa_reg_lamda\", \"0.0005\")\n    g2.set_parameter(\"Recon\", \"grappa_calib_over_determine_ratio\", \"0\")\n    g2.set_parameter(\"Recon\", \"spirit_kSize_RO\", \"7\")\n    g2.set_parameter(\"Recon\", \"spirit_kSize_E1\", \"7\")\n    g2.set_parameter(\"Recon\", \"spirit_kSize_E2\", \"5\")\n    g2.set_parameter(\"Recon\", \"spirit_reg_lamda\", \"0.005\")\n    g2.set_parameter(\"Recon\", \"spirit_calib_over_determine_ratio\", \"0\")\n    g2.set_parameter(\"Recon\", \"spirit_solve_symmetric\", \"false\")\n    g2.set_parameter(\"Recon\", \"spirit_iter_max\", \"90\")\n    g2.set_parameter(\"Recon\", \"spirit_iter_thres\", \"0.0015\")\n    g2.set_parameter(\"Recon\", \"spirit_print_iter\", \"false\")\n    g2.set_parameter(\"Recon\", \"spirit_perform_linear\", \"true\")\n    g2.set_parameter(\"Recon\", \"spirit_perform_nonlinear\", \"true\")\n    g2.set_parameter(\"Recon\", \"spirit_parallel_imaging_lamda\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"spirit_image_reg_lamda\", \"0.0025\")\n    g2.set_parameter(\"Recon\", \"spirit_data_fidelity_lamda\", \"0\")\n    g2.set_parameter(\"Recon\", \"spirit_ncg_iter_max\", \"10\")\n    g2.set_parameter(\"Recon\", \"spirit_ncg_iter_thres\", \"0.0001\")\n    g2.set_parameter(\"Recon\", \"spirit_ncg_print_iter\", \"true\")\n    g2.set_parameter(\"Recon\", \"spirit_use_coil_sen_map\", \"true\")\n    g2.set_parameter(\"Recon\", \"spirit_use_moco_enhancement\", \"false\")\n    g2.set_parameter(\"Recon\", \"spirit_recon_moco_images\", \"false\")\n    g2.set_parameter(\"Recon\", \"spirit_RO_enhancement_ratio\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"spirit_E1_enhancement_ratio\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"spirit_E2_enhancement_ratio\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"spirit_temporal_enhancement_ratio\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"spirit_2D_scale_per_chunk\", \"false\")\n    g2.set_parameter(\"Recon\", \"min_intensity_value\", \"64\")\n    g2.set_parameter(\"Recon\", \"max_intensity_value\", \"4095\")\n    g2.set_parameter(\"Recon\", \"scalingFactor\", \"10\")\n    g2.set_parameter(\"Recon\", \"scalingFactor_gfactor\", \"100\")\n    g2.set_parameter(\"Recon\", \"scalingFactor_snr_image\", \"10\")\n    g2.set_parameter(\"Recon\", \"scalingFactor_std_map\", \"1000\")\n    g2.set_parameter(\"Recon\", \"start_frame_for_std_map\", \"5\")\n    g2.set_parameter(\"Recon\", \"use_constant_scalingFactor\", \"false\")\n    g2.set_parameter(\"Recon\", \"filterRO\", \"ISMRMRD_FILTER_NONE\")\n    g2.set_parameter(\"Recon\", \"filterRO_sigma\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"filterRO_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterE1\", \"ISMRMRD_FILTER_NONE\")\n    g2.set_parameter(\"Recon\", \"filterE1_sigma\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"filterE1_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterE2\", \"ISMRMRD_FILTER_NONE\")\n    g2.set_parameter(\"Recon\", \"filterE2_sigma\", \"1.0\")\n    g2.set_parameter(\"Recon\", \"filterE2_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterRefRO\", \"ISMRMRD_FILTER_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterRefRO_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterRefRO_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterRefE1\", \"ISMRMRD_FILTER_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterRefE1_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterRefE1_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterRefE2\", \"ISMRMRD_FILTER_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterRefE2_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterRefE2_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierRO\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierRO_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierRO_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierRO_densityComp\", \"false\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE1\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE1_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE1_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE1_densityComp\", \"false\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE2\", \"ISMRMRD_FILTER_TAPERED_HANNING\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE2_sigma\", \"1.5\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE2_width\", \"0.15\")\n    g2.set_parameter(\"Recon\", \"filterPartialFourierE2_densityComp\", \"false\")\n    g2.set_parameter(\"Recon\", \"partialFourier_algo\", \"ISMRMRD_PF_ZEROFILLING_FILTER\")\n    g2.set_parameter(\"Recon\", \"partialFourier_homodyne_iters\", \"6\")\n    g2.set_parameter(\"Recon\", \"partialFourier_homodyne_thres\", \"0.01\")\n    g2.set_parameter(\"Recon\", \"partialFourier_homodyne_densityComp\", \"false\")\n    g2.set_parameter(\"Recon\", \"partialFourier_POCS_iters\", \"6\")\n    g2.set_parameter(\"Recon\", \"partialFourier_POCS_thres\", \"0.01\")\n    g2.set_parameter(\"Recon\", \"partialFourier_POCS_transitBand\", \"24\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_RO\", \"5\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_E1\", \"5\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_kSize_E2\", \"5\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_thresReg\", \"0.01\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_sameKernel_allN\", \"false\")\n    g2.set_parameter(\"Recon\", \"partialFourier_FengHuang_transitBand\", \"24\")\n    g2.set_parameter(\"Recon\", \"debugFolder\", \"\")\n    g2.set_parameter(\"Recon\", \"debugFolder2\", \"\")\n    g2.set_parameter(\"Recon\", \"performTiming\", \"true\")\n    g2.set_parameter(\"Recon\", \"verboseMode\", \"true\")\n    g2.set_parameter(\"Recon\", \"timeStampResolution\", \"0.0025\")\n    g2.set_parameter(\"Recon\", \"job_split_by_S\", \"false\")\n    g2.set_parameter(\"Recon\", \"job_num_of_N\", \"32\")\n    g2.set_parameter(\"Recon\", \"job_max_Megabytes\", \"10240\")\n    g2.set_parameter(\"Recon\", \"job_overlap\", \"2\")\n    g2.set_parameter(\"Recon\", \"job_perform_on_control_node\", \"true\")\n    g2.set_parameter(\"Recon\", \"CloudComputing\", \"false\")\n    g2.set_parameter(\"Recon\", \"cloudNodeFile\", \"myCloud_2DT.txt\")\n    g2.set_parameter(\"Recon\", \"CloudNodeXMLConfiguration\", \"GT_2DT_Cartesian_CloudNode.xml\")\n    g2.prepend_gadget(\"gadgetronPlus\", \"GtPlusAccumulatorWorkOrderTriggerGadget\", gadgetname=\"Acc\")\n    g2.set_parameter(\"Acc\", \"verboseMode\", \"false\")\n    g2.set_parameter(\"Acc\", \"noacceleration_triggerDim1\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"noacceleration_triggerDim2\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"noacceleration_numOfKSpace_triggerDim1\", \"1\")\n    g2.set_parameter(\"Acc\", \"interleaved_triggerDim1\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"interleaved_triggerDim2\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"interleaved_numOfKSpace_triggerDim1\", \"4\")\n    g2.set_parameter(\"Acc\", \"embedded_triggerDim1\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"embedded_triggerDim2\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"embedded_numOfKSpace_triggerDim1\", \"1\")\n    g2.set_parameter(\"Acc\", \"separate_triggerDim1\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"separate_triggerDim2\", \"DIM_NONE\")\n    g2.set_parameter(\"Acc\", \"separate_numOfKSpace_triggerDim1\", \"1\")\n    g2.set_parameter(\"Acc\", \"other_kspace_matching_Dim\", \"DIM_Repetition\")\n    g2.prepend_gadget(\"gadgetron_mricore\", \"RemoveROOversamplingGadget\", gadgetname=\"RemoveROOversampling\")\n    g2.prepend_gadget(\"gadgetron_mricore\", \"AsymmetricEchoAdjustROGadget\", gadgetname=\"AsymmetricEcho\")\n    g2.prepend_gadget(\"gadgetron_mricore\", \"NoiseAdjustGadget\", gadgetname=\"NoiseAdjust\")\n    g2.set_parameter(\"NoiseAdjust\", \"noise_dependency_prefix\", \"GadgetronNoiseCovarianceMatrix\")\n    return g2", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 133, "cell_type": "code", "source": "def run_gadget_chain(gadget_chain_function, ismrmrd_file):\n    g = globals()[gadget_chain_function]()\n    dset = ismrmrd.Dataset(ismrmrd_file, 'dataset', create_if_needed=False)                 \n\n    gadget_chain_config(g,dset.read_xml_header())\n\n    # Loop through the rest of the acquisitions and stuff\n    for acqnum in range(0,dset.number_of_acquisitions()):\n        acq = dset.read_acquisition(acqnum)\n        g.process(acq.getHead(),acq.data.astype('complex64'))\n    \n    gadget_chain_wait(g)\n    return get_last_gadget(g).get_results()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 134, "cell_type": "code", "source": "def run_snr_scaled_recon(filename_noise, filename_data):\n    junk = run_gadget_chain(\"define_gadget_chain_dependencies\", filename_noise)\n    snr_scale = run_gadget_chain(\"define_gadget_chain_gfactor\", filename_data)\n    nslices = len(snr_scale)\n    Nx = snr_scale[0][1].shape[0]\n    Ny = snr_scale[0][1].shape[1]\n    \n    out = np.zeros((nslices, Ny, Nx), dtype=np.complex64)\n    for s in range(nslices):\n        out[s,:,:] = np.flipud(np.fliplr(snr_scale[s][1]))\n        \n    return out[(5,0,6,1,7,2,8,3,9,4),:,:]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 135, "cell_type": "code", "source": "snr_scale_old = run_snr_scaled_recon(data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00218_FID33531_OLD_SNR_COR_RL_R1_data.h5')\nsnr_scale_new = run_snr_scaled_recon(data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00229_FID33542_NEW_SNR_COR_RL_R1_data.h5')\nsnr_scale_sie = run_snr_scaled_recon(data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_noise.h5', data_folder + 'meas_MID00241_FID33554_SIE_SNR_COR_RL_R1_data.h5')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 136, "cell_type": "code", "source": "nslices = snr_scale_old.shape[0]\n\nsnr_scale_roi_old = np.sum(abs(snr_scale_old)*mask,axis=(1,2)) / np.sum(mask)\nsnr_scale_roi_new = np.sum(abs(snr_scale_new)*mask,axis=(1,2)) / np.sum(mask)\nsnr_scale_roi_sie = np.sum(abs(snr_scale_sie)*mask,axis=(1,2)) / np.sum(mask)\n\nshow.imshow(abs(snr_scale_old[1,:,:])*mask,colorbar=True)", "outputs": [{"output_type": "display_data", "data": {"image/png": 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JLXknoAxDAmPYXk2pYkvRiTDGkoYZrWhFXFZEEWS3DMlbkFxWQl5aneMSITLt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\nT78Jv/3N1320vwf8B8aY/01E/gLw63yvW/ur/ED5TR/U7it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"text/plain": "<matplotlib.figure.Figure at 0x7f7a9ee0d190>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 137, "cell_type": "code", "source": "pp.plot(snr_scale_roi_new,'r')\npp.plot(snr_scale_roi_old,'b')\npp.plot(snr_scale_roi_sie,'g')", "outputs": [{"execution_count": 137, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ab0035d90>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7a9eebefd0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 138, "cell_type": "code", "source": "pp.plot(snr_scale_roi_new/snr_scale_roi_old,'r')\n", "outputs": [{"execution_count": 138, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ab0049b50>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7a9ef82650>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 139, "cell_type": "code", "source": "dcm = dicom.read_file(data_folder + 'DICOM/QEDTEST20150415/QEDTEST20150415_HANSEN.MR.HANSEN_GENERAL.0038.0006.2015.04.15.14.12.27.763810.125323084.IMA')", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.6", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}