license: cc-by-nc-sa-4.0
pretty_name: DiSCo time segments
tags:
- diffusion-mri
- monte-carlo
- replay-pack
- tractography-phantom
DiSCo time segments
The DiSCo replay walk, contracted at DiSCo's own seven gradient timings so that a whole-grid image costs one pass over 125 bytes per walker. It comes from the same 321,138,027 walkers as the replay pack in SubstrateCommons/disco-replay. It was contracted from the walk's unquantised coefficients beside each shard, not from the pack.
Choose the dataset by the question. Use this one for an acquisition at one of the timings below, where every other knob stays free. Use the replay pack for any other waveform: another δ or Δ, OGSE, an arbitrary gradient.
What a timing class leaves free
Each class fixes the times at which the gradient may switch; its segments are constant-gradient intervals. Within a class, everything else is chosen at replay time:
- the b-value and direction of every measurement, up to the class's gradient amplitude;
- the per-voxel delivered gradient (gradient nonlinearity, a background gradient);
- T₂ and T₁ per pool, and surface relaxivity ρ₂ ≤ 3 µm/s;
- B₀ of 1.5, 3 or 7 T in any direction, and myelin χ up to 2 × 10⁻⁷, isotropic and anisotropic.
| class | timing (ms) | echo |
|---|---|---|
d7.6-D45.9 |
δ 7.6, Δ 45.9 | spin echo, TE 53.5 |
d8-D20 |
δ 8, Δ 20 | spin echo, TE 53.5 |
d10.2-D16.7 |
δ 10.2, Δ 16.7 | spin echo, TE 53.5 |
d12-D24 |
δ 12, Δ 24 | spin echo, TE 53.5 |
d17-D30 |
δ 17, Δ 30 | spin echo, TE 53.5 |
d17.7-D35.8 |
δ 17.7, Δ 35.8 | spin echo, TE 53.5 |
ste-d7.6-TM38.3 |
δ 7.6, mixing time 38.3 | stimulated echo |
Each class's widths are derived at the 1.003 % floor. The layout's own error bound is in disco/manifest.json under
certificate, term by term. Against the replay pack, 16 voxels in all seven classes agree to 2.2 × 10⁻⁵, against
a combined bound of 3.0 × 10⁻³.
Use me
pip install "git+https://github.com/dmrai-lab/dmipy-sim@main" "huggingface_hub>=0.25"
import numpy as np
from dmipy_sim import sequences
from dmipy_sim.replay.time_segments import TimeSegmentLayout
from dmipy_sim.spec.tissue import Tissue
layout = TimeSegmentLayout.open("hf://SubstrateCommons/disco-time-segments/disco",
partitions=["d10.2-D16.7"]) # fetches this class's words and the shared columns
seq = sequences.pgse([[1, 0, 0], [0, 0, 1]], 0.0102, 0.0167, bvalues=[1e9, 3.094e9], TE=0.0535,
slew_rate=np.inf) # square lobes: constant on the class's segments
S, floor = layout.image(seq) # (40, 40, 40, 2) S/S0 and the per-voxel floor
wm = Tissue(T2={"intra": 0.05, "extra": 0.055, "myelin": 0.01}, rho2=1e-6, chi_iso=-1e-7, chi_aniso=-1e-7)
S_3T, _ = layout.image(seq, tissue=wm, scanner=3.0) # relaxation, wall contact and the sheath field at 3 T
layout.fit(seq) names the class a sequence fits, or says why it fits none. partitions=None fetches every
class (40 GB). A whole-grid image keeps the class's
columns in memory, about 9 GB for bare diffusion and more with the tiers; resident=False frees them after the
call.
Files
| path | what |
|---|---|
disco/manifest.json |
grid, classes and their segments, widths, certificate, provenance |
disco/w.npy, disco/tiles.npy, disco/pool.npy |
each row's weight, its voxel half, and its pool |
disco/p_<class>.npy |
each class's segment vectors, packed |
disco/t_g0.npy, disco/t_g1.npy |
the relaxation, contact and field tiers of the spin-echo and stimulated-echo groups |
disco/bg_g0.npy, disco/bg_g1.npy |
the background-gradient term of each group |
The rows are in tiles of 128, sorted by voxel. Each voxel's rows are split into two halves, which give the split-half floor.
Licence
CC BY-NC-SA 4.0, or a commercial licence on request. The DiSCo substrate is the DiSCo authors' work, CC BY 4.0.
Citation
- Rafael-Patino J, Girard G, Truffet R, Pizzolato M, Caruyer E, Thiran J-P. The diffusion-simulated connectivity (DiSCo) dataset. Data in Brief 38 (2021) 107429.
- Fick RHJ. Replayable Monte Carlo MRI: from persistent tissue microphysics to queryable virtual phantoms (in preparation, 2026). dmipy-sim, https://github.com/dmrai-lab/dmipy-sim.