turbotwix

A fast, and opiniated, Siemens TWIX (.dat, VB and VD/VE) reader.

• 6x faster than pymapvbvd on a full Cartesian read, and able to pull a single shot out of a 24 GiB file in a couple of milliseconds without ever loading the rest.
• Use modern python (type annotations) and numpy structured dtype array.
• NB: It does not implement slice-geometry parsing or ramp-sampling regridding.

Disclosure

This library is not affiliated with Siemens, and the TWIX format is not documented by them. The format has been reverse-engineered from the files and the existing implementations:

• pymapvbvd
• twixtools,

Without them, turbotwix would not exists.

Usage

import turbotwix as tw

f = tw.open_twix("meas.dat")
f  # TwixFile(..., measurements=['AdjCoilSens', 'bold_spiral...'])

lines = f.lines  # one strided read of the line headers
lines.image  # LineTable: imaging lines only
lines.noise  # noise-calibration lines, for pre-whitening
len(lines.image), lines.shape  # 4800, (44, 15000)

samples = f.read(lines.image)  # (4800, 44, 15000) complex64

f.lines, f.hdr and f.read act on the last measurement (f.scan), which is the scan itself, the measurements before it are usually calibration data you don't necessary need. They are still there, by index or by iteration:

len(f)  # 2
f[0].protocol_name  # 'AdjCoilSens'
noise = f[0].lines.noise  # a calibration measurement, explicitly

Selections are boolean queries and compose, so a partial read is just a smaller selection. Nothing else is touched on disk:

img = f.lines.image
rep0 = img[img.counter("Rep") == 0]  # one volume's shots
vol = f.read(rep0)  # (40, 44, 15000), 201 MiB
shot = f.read(img[5:6])  # one shot, whatever the file's size

lines.has(tw.Flag.REFLECT)  # any of the 64 eval-info bits, by name
lines.image.headers()  # full scan headers on demand: SliceData,
# IceProgramPara, timestamps, centre indices

Read into your own buffer to bound memory on files that do not fit in RAM. The copy goes straight from the mapped file into it, with no intermediate buffer:

buf = np.empty((len(rep0), 44, 15000), dtype=np.complex64)
for r in np.unique(img.counter("Rep")):
    sel = img[img.counter("Rep") == r]
    f.read(sel, out=buf[: len(sel)])
    ...

The header/text protocol is parsed per buffer on first access, by attribute or by key:

f.hdr.Meas.alTR[0]
f.scan.protocol_name, f.scan.patient_name

You can also use a context manager if you want:

with tw.open_twix("meas.dat") as f:
    samples = f.read(f.lines.image)

The data model

A TWIX measurement is a list of acquisition lines, each with metadata and a (ncha, ncol) block of samples. turbotwix hands you exactly that: a queryable line table, and reads that return (ncha, n_lines, ncol) — channel first, matching its on-disk order, contiguous.

You can query and filter the lines, and when you want it get a numpy array of the data you need

PMU

Physiological (ECG/pulse/respiration) data interleaved with the acquisition, if any:

pmu = f.scan.pmu  # empty if this measurement carries no PMU data
pmu.signal["ECG1"]  # normalized waveform
pmu.trigger["PULS"]  # matching boolean trigger channel
pmu.timestamp["ECG1"]  # per-sample clock, 2.5 ms ticks since midnight

Cartesian data

dense = f.read(dims=("Lin", "Par"))  # (Lin, Par, Cha, Col)

read(dims=...) raises if several lines land on the same grid position — that normally means a counter is missing from dims, not that the data wants averaging — and names the counters responsible. dims="minimal" picks the axes for you: the counters that vary, minus those the others already determine.

Correctness

turbotwix is checked against two existing readers:

• pymapvbvd
• twixtools,

they are the prior art for this format, and the ground truth tests/test_parity.py verifies against.

turbotwix vs. twixtools vs. pymapvbvd

turbotwix trades the signal-processing conveniences (regridding, oversampling removal, geometry) for raw read speed and the ability to pull a small selection out of a file that doesn't fit in RAM. twixtools and pymapvbvd remain the more complete choice when you need those conveniences and can afford to load the full measurement.

Performance

Full read of a 234 MB Cartesian measurement, cold page cache, each library in its own process (scripts/bench_read.py):

The case this doesn't show is the one the design is really for: a selection out of a file far larger than RAM, where the reference readers have no choice but to assemble the whole array first. On a 24 GiB interleaved spiral measurement, turbotwix builds the line table in ~65 ms and pulls a single shot out of it in ~3 ms — pymapvbvd and twixtools have no equivalent operation, since without a k-space grid there is nothing partial to fold onto.

The methodology and where the speed comes from are in docs/implementation.md.

Known limitations

• A measurement whose line offsets are not 8-byte aligned raises UnsupportedLayoutError.
• Lines of differing (ncha, ncol) are tabled together but cannot be read in one call; select a single-shaped subset.
• SYNCDATA blocks are not ADC data and never appear in the line table; PMU ones decode separately via Measurement.pmu.
• No ramp-sampling regridding or slice-geometry parsing.
• No oversampling removal: it is signal processing (an FFT round-trip), and along a non-Cartesian readout it is not meaningful.

Documentation

• docs/twix-format.md: A candidate explanation of what a .dat file contains, byte by byte.
• docs/implementation.md: The big picture on how this reader works and why.

Development

uv sync                    # numpy + dev tools only
uv run pytest
uv run ruff check .
uv run ty check src
uv sync --group parity     # to also run tests/test_parity.py against real references