| --- |
| 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](https://huggingface.co/datasets/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" |
| ``` |
|
|
| ```python |
| 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. |
|
|
