IRGN¶
Module holding the classes for IRGN Optimization without streaming.

class
pyqmri.irgn.
ICOptimizer
(par, model, trafo=1, imagespace=False, SMS=0, reg_type='TGV', config='', streamed=False, DTYPE=<class 'numpy.complex64'>, DTYPE_real=<class 'numpy.float32'>)¶ Main IC Optimization class.
This Class performs IC Optimization either with TGV or TV regularization.
Parameters:  par (dict) – A python dict containing the necessary information to setup the object. Needs to contain the number of slices (NSlice), number of scans (NScan), image dimensions (dimX, dimY), number of coils (NC), sampling points (N) and read outs (NProj) a PyOpenCL queue (queue) and the complex coil sensitivities (C).
 model (pyqmri.model) – Which model should be used for fitting. Expects a pyqmri.model instance.
 trafo (int, 1) – Select radial (1, default) or cartesian (0) sampling for the fft.
 imagespace (bool, false) – Perform the fitting from kspace data (false, default) or from the image series (true)
 SMS (int, 0) – Select if simultaneous multislice acquisition was used (1) or standard slicebyslice acquisition was done (0, default).
 reg_type (str, "TGV") – Select between “TGV” (default) or “TV” regularization.
 config (str, '') – Name of config file. If empty, default config file will be generated.
 streamed (bool, false) – Select between standard reconstruction (false) or streamed reconstruction (true) for large volumetric data which does not fit on the GPU memory at once.
 DTYPE (numpy.dtype, numpy.complex64) – Complex working precission.
 DTYPE_real (numpy.dtype, numpy.float32) – Real working precission.

par
¶ A python dict containing the necessary information to setup the object. Needs to contain the number of slices (NSlice), number of scans (NScan), image dimensions (dimX, dimY), number of coils (NC), sampling points (N) and read outs (NProj) a PyOpenCL queue (queue) and the complex coil sensitivities (C).
Type: dict

gn_res
¶ The residual values for of each GaussNewton step. Each iteration appends its value to the list.
Type: list of floats

irgn_par
¶ The parameters read from the config file to guide the IRGN optimization process
Type: dict

execute
(data)¶ Start the IRGN optimization.
This method performs iterative regularized GaussNewton optimization and calls the inner loop after precomputing the current linearization point. Results of the fitting process are saved after each linearization step to the output folder.
Parameters: data (numpy.array) – the data to perform optimization/fitting on.

class
pyqmri.irgn.
IRGNOptimizer
(par, model, trafo=1, imagespace=False, SMS=0, reg_type='TGV', config='', streamed=False, DTYPE=<class 'numpy.complex64'>, DTYPE_real=<class 'numpy.float32'>)¶ Main IRGN Optimization class.
This Class performs IRGN Optimization either with TGV or TV regularization.
Parameters:  par (dict) – A python dict containing the necessary information to setup the object. Needs to contain the number of slices (NSlice), number of scans (NScan), image dimensions (dimX, dimY), number of coils (NC), sampling points (N) and read outs (NProj) a PyOpenCL queue (queue) and the complex coil sensitivities (C).
 model (pyqmri.model) – Which model should be used for fitting. Expects a pyqmri.model instance.
 trafo (int, 1) – Select radial (1, default) or cartesian (0) sampling for the fft.
 imagespace (bool, false) – Perform the fitting from kspace data (false, default) or from the image series (true)
 SMS (int, 0) – Select if simultaneous multislice acquisition was used (1) or standard slicebyslice acquisition was done (0, default).
 reg_type (str, "TGV") – Select between “TGV” (default) or “TV” regularization.
 config (str, '') – Name of config file. If empty, default config file will be generated.
 streamed (bool, false) – Select between standard reconstruction (false) or streamed reconstruction (true) for large volumetric data which does not fit on the GPU memory at once.
 DTYPE (numpy.dtype, numpy.complex64) – Complex working precission.
 DTYPE_real (numpy.dtype, numpy.float32) – Real working precission.

par
¶ A python dict containing the necessary information to setup the object. Needs to contain the number of slices (NSlice), number of scans (NScan), image dimensions (dimX, dimY), number of coils (NC), sampling points (N) and read outs (NProj) a PyOpenCL queue (queue) and the complex coil sensitivities (C).
Type: dict

gn_res
¶ The residual values for of each GaussNewton step. Each iteration appends its value to the list.
Type: list of floats

irgn_par
¶ The parameters read from the config file to guide the IRGN optimization process
Type: dict

applyPrecond
(inp)¶

execute
(data)¶ Start the IRGN optimization.
This method performs iterative regularized GaussNewton optimization and calls the inner loop after precomputing the current linearization point. Results of the fitting process are saved after each linearization step to the output folder.
Parameters: data (numpy.array) – the data to perform optimization/fitting on.

removePrecond
(inp)¶