twixtools 0.24

file reader for Siemens twix(.dat)-files

twixtools

Purpose

twixtools provide reading and limited writing capability of Siemens MRI raw data files (.dat).

Installation

Navigate to the twixtools folder in an open terminal and install twixtools with pip:

pip install .

Installation through python setup.py install is currently not possible.

Demo code

A jupyter notebook that demonstrates the basic functionality of the read_twix, map_twix, and write_twix tools can be found in demo/recon_example.ipynb.

read_twix: "low-level" access to twix data

The raw data file can be parsed using the read_twix function:

import twixtools
multi_twix = twixtools.read_twix(filename)

The function returns a list of individual measurements (of length >=1). The last measurement usually corresponds to the imaging scan, earlier measurements often include calibration data. Each measurement contains a python dict() with the following entries:

• 'mdb': measurement data divided into blocks (return type: list)
• 'hdr': dict of parsed protocol header strings (each dict element contains another dict with protocol information)
• 'hdr_str': dict of original protocol header strings (divided into different protocol types)
  • note that this is the protocol information that is used for twix file writing (by write_twix), so make sure to make necessary adjustments here and not in ['hdr']
• 'geometry': Geometry (slice position and orientation) information
• 'pmu': physiological (PMU) data (if available and parse_pmu is set to True)
• ('raidfile_hdr': required for twix file writing, otherwise of little importance)

Each invididual 'mdb' in the list of mdbs consists of a data and a header (line counters and such) part, which can be accessed as follows:

mdb = multi_twix[-1]['mdb'][0] # first mdb element of last measurement
mdb.data # data of first mdb (may or may not be imaging data)
mdb.mdh # full miniheader information stored in a ctypes structure

To print an overview of an mdb, you can just call python's print function on it:

print(mdb)

Different data types can be distinguished by returning a list of active flags, or by directly checking whether the data is assumed to be from an imaging scan (and not from a calibration scan such as a phase correction scan or a noise measurement):

mdb.get_active_flags() # get all active MDH flags
mdb.is_image_scan() # check if this an image scan (True or False)

Line Counters can be accessed as follows:

mdb.cLin   # returns line number
mdb.cPar   # returns partition number
mdb.c<tab> # with line completion enabled, this should give you a list of all counters

The full minidata header (mdh) information is stored in a mdb.mdh ctypes structure of type 'Scan_header'. You can print a list of its entry names by printing mdb.mdh.__class__.__dict__.

Example code

import numpy as np
import twixtools

# read all image data from file
def read_image_data(filename):
    out = list()
    for mdb in twixtools.read_twix(filename)[-1]['mdb']:
        if mdb.is_image_scan():
            out.append(mdb.data)
    return np.asarray(out)  # 3D numpy array [acquisition_counter, n_channel, n_column]


# read image data from list of mdbs and sort into 3d k-space (+ coil dim.)
def import_kspace(mdb_list)
    image_mdbs = []
    for mdb in mdb_list:
        if mdb.is_image_scan():
            image_mdbs.append(mdb)

    n_line = 1 + max([mdb.cLin for mdb in image_mdbs])
    n_part = 1 + max([mdb.cPar for mdb in image_mdbs])
    n_channel, n_column = image_mdbs[0].data.shape

    out = np.zeros([n_part, n_line, n_channel, n_column], dtype=np.complex64)
    for mdb in image_mdbs:
        # '+=' takes care of averaging, but careful in case of other counters (e.g. echoes)
        out[mdb.cPar, mdb.cLin] += mdb.data

    return out  # 4D numpy array [n_part, n_line, n_channel, n_column]

map_twix: "high level" access to twix data

map_twix is a high-level function that takes the data obtained from read_twix (in the form of Mdb objects), and maps it to multi-dimensional "k-space" arrays. These twix_array objects are generated for different data types (image/noise adjust/phase-correction/... scan) and can be accessed with numpy.ndarray array-slicing syntax.

Optional flags control additional feature and also have an impact on size and shape of the multidimensional arrays. The following flags are currently available (stored in the flags dict within each twix_array object):

• average: dict of bools that determines which dimensions should be averaged.
• squeeze_ave_dims: bool that determines whether averaged dimensions should be removed/squeezed from the array's shape.
• remove_os: oversampling removal. Reduces the number of columns by a factor of two.
• regrid: bool that controls ramp-sampling regridding (if applicable)
• skip_empty_lead: skips to first line & partition that is found in mdb list (e.g. if first line counter is 10, the output array starts at line counter 10).
• zf_missing_lines: zero-fill k-space to include lines and partitions that are higher than the maximum counter found in the mdb list, but are still within the k-space matrix according to the twix header.

If available, physiological (PMU) data is stored in the returned dict under the 'pmu' key.

For example code, please look at the demo/recon_example.ipynb jupyter file.

Acknowledgements

The protocol header parsing code originates from William Clarke's excellent pymapvbvd project (https://github.com/wexeee/pymapvbvd).