Applications#
bartorch.tools: BART’s applications, one function per command. In each
section the hand-written wrappers come first; the rest are built from BART’s
own declaration of the command and take its options under their long names.
Simulation#
An analytical phantom, as an image or as its k-space. |
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simulation tool |
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Compute coil sensitivitity maps in x-space or k-space. |
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Simulate MR pulse sequence based on Extended Phase Graphs (EPG) |
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Recreate k-space from image and sensitivities. |
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Forward calculation of physical signal models. |
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Add noise with selected variance to input. |
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Pulse generation tool |
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Computes a GRE sequence. |
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Analytical simulation tool. |
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simulation tool |
Sampling and trajectories#
A k-space trajectory, in grid units. |
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Binning |
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Estimate gradient delays from radial data. |
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Estimate image dimension from non-Cartesian trajectory. |
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Compute sampling grid for x-space / k-space (and time). |
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Compute the Fourier transform of a basis function to be used in the nuFFT. |
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Compute sampling pattern from kspace |
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Computes Poisson-disc sampling pattern. |
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Calculate point-spread-function (PSF) of given trajectory. |
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Generate file with RAGA indices for given approximated tiny golden ratio angle/raga increment and full frame spokes. |
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Remove angle-dependent frequency |
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Perform SSA-FARY or Singular Spectrum Analysis. |
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Correct delays for a given trajectory. |
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Create a sampling pattern. |
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Generate a wave PSF in hybrid space. |
Coil calibration#
Coil sensitivities by ESPIRiT. |
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Coil sensitivities from the centre of k-space directly. |
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Compute calibration matrix. |
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Performs coil compression. |
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Apply coil compression forward/inverse operation. |
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Second part of ESPIRiT calibration. |
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Estimate scaling from k-space center. |
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Estimate the noise variance assuming white Gaussian noise. |
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Estimate coil sensitivities using ENLIVE calibration. |
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Detect and sample phase poles. |
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Compute coil compression matrix using ROVir. |
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Estimate coil sensitivities using walsh method (use with ecaltwo). |
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Apply multi-channel noise pre-whitening on <input> using noise data <ndata>. |
Reconstruction#
Parallel-imaging compressed-sensing reconstruction. |
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Nonlinear inversion: the image and the sensitivities together. |
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GROG calibration and gridding of radial data. |
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Perform homodyne reconstruction along dimension dim. |
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A simplified implementation of iterative sense reconstruction with l2-regularization. |
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Compute T1 map from M_0, M_ss, and R_1*. |
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Perform (multi-scale) low rank matrix completion |
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Model-based nonlinear inverse reconstruction |
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Pixel-wise fitting of physical signal models. |
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Perform POCSENSE reconstruction. |
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Jointly estimate a time-series of images and sensitivities with nonlinear inversion using {iter} iteration steps. |
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Use SAKE algorithm to recover a full k-space from undersampled data using low-rank matrix completion. |
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Parallel-imaging compressed-sensing reconstruction. |
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Perform a wave-caipi reconstruction. |
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Perform a wave-shuffling reconstruction. |