license:
- cc-by-nc-sa-4.0
- other
pretty_name: DiSCo time segments
tags:
- diffusion-mri
- monte-carlo
- replay-pack
- tractography-phantom
DiSCo time segments
The DiSCo replay walk, contracted at nine gradient waveforms -- DiSCo's own timings, the replay paper's clinical, research and Connectome 2.0 timings, a stimulated echo, a cosine OGSE and a b-tensor encoding class -- so that a whole-grid image costs one pass over 169 bytes per walker. It comes from the same 321,138,027 walkers as the replay pack in SubstrateCommons/disco-replay. The seven square-lobe classes were contracted from the walk's unquantised coefficients beside each shard; the OGSE and b-tensor classes from the quantised replay pack, each one's quantisation charged in its own certificate term.
Choose the dataset by the question. Use this one for an acquisition on one of the waveforms below, where every other knob stays free. Use the replay pack for any other waveform: another δ or Δ, another OGSE, an arbitrary gradient.
What a timing class leaves free
Each class fixes the waveform's shape in time: a square-lobe class's terms are constant-gradient intervals, the OGSE class's two terms are its cosine blocks as stored, the b-tensor class's six terms are its three gradient shapes on each side of the 180. 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;
- on the b-tensor class, the encoding itself: any linear, planar or spherical b-tensor of its three shapes, at any rotation and b;
- the per-voxel delivered gradient (gradient nonlinearity, a background gradient; square-lobe classes);
- 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 |
cos-f50-T20 |
cosine 50 Hz, one 20 ms period each side of the 180, edges ramped at 200 T/m/s as built at b = 1000 s/mm² | spin echo, TE 53.5 |
qte-now-80 |
three shapes: the axes of NOW's maximum-b spherical encoding (Sjölund et al. 2015) at 80 mT/m and 200 T/m/s per axis | spin echo, TE 53.5 |
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 the seven square-lobe classes agree to 2.2 × 10⁻⁵,
in the OGSE class to 2.2 × 10⁻⁴ bare and 4.5 × 10⁻⁴ with every tier at 3 T, and in the b-tensor class (linear,
planar and spherical rows at b = 500 to 2000 s/mm²) to 1.2 × 10⁻⁵ bare and 4.5 × 10⁻⁴ at 3 T, 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; an OGSE row is the class's stored shape
scaled to its b (layout.partitions["cos-f50-T20"].shapes). partitions=None fetches every class (55 GB).
resident=False streams the packed words to the device a chunk at a time: one class of the whole grid with every
tier in about 3.4 GB (dmipy-sim#823); resident=True keeps them there across calls (about 21 GB with the tiers).
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 term 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.