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{"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": 37, "cell_type": "code", "source": "import os\nimport ismrmrd\nimport ismrmrd.xsd\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as pp\n\nfrom ismrmrdtools import show, transform, coils, grappa, sense\n%matplotlib inline", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 7, "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": 13, "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": 3, "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": 41, "cell_type": "code", "source": "mask = np.ones((256,256))\nmask[64:192,20+64:20+192] = 0\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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7BgIvhRK3cQq3sBGBiXfxQdIiAmf5FfYa2fVk9q0IA7aFsyIF4I1NFqIRf4Oj7thnn9n/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Q2F44GdJ7zuWW/pa+Dv8JKdWkl9gmCxNNG0R16y1qHP3T5bCNqK7HwjvJFRyZwj+mzY0Kcl5COe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\nLvka8RiM24c5LUPvZHLQP7fIMfFmnx7o6xnTf0/KwFu3w03Ev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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac8ae11d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 48, "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": 48, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac9436550>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac940f090>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 49, "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": 49, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac963ff90>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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YG/gLsI8Z/2zm5J8AZxHmsb9chvDaLNvY+nxgaeB4S+z+yCE512ae1F2TJLYF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\n05oL6bLEQrr0+JrOnbszb+E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"text/plain": "<matplotlib.figure.Figure at 0x7f7ac96768d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 50, "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": 50, "output_type": "execute_result", "data": {"text/plain": "[<matplotlib.lines.Line2D at 0x7f7ac8afb190>]"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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