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Datasets:
SubstrateCommons
/
disco-time-segments

Tags:
diffusion-mri
monte-carlo
replay-pack
tractography-phantom
License:
Dataset card Files
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1 Parent(s): ad6db03

Add the b-tensor class qte-now-80 (the three axes of NOW's maximum-b spherical encoding at TE 53.5 ms, 80 mT/m and 200 T/m/s per axis, as six shaped terms) from the quantised replay pack; group g0's tiers (#4)

Browse files

- Add the b-tensor class qte-now-80 (the three axes of NOW's maximum-b spherical encoding at TE 53.5 ms, 80 mT/m and 200 T/m/s per axis, as six shaped terms) from the quantised replay pack; group g0's tiers (a7cc798d1684c8bbc5c59031c2d876ad08d018f6)

Files changed (3) hide show
  1. README.md +14 -8
  2. disco/manifest.json +2879 -3
  3. disco/p_qte-now-80.npy +3 -0
README.md CHANGED
@@ -12,12 +12,12 @@ tags:
12
 
13
  # DiSCo time segments
14
 
15
- The DiSCo replay walk, contracted at eight gradient waveforms -- DiSCo's own timings, the replay paper's clinical,
16
- research and Connectome 2.0 timings, a stimulated echo and a cosine OGSE -- so that a whole-grid image costs one pass
17
- over 133 bytes per walker. It comes from the same 321,138,027 walkers as the replay pack in
18
  [SubstrateCommons/disco-replay](https://huggingface.co/datasets/SubstrateCommons/disco-replay). The seven square-lobe
19
- classes were contracted from the walk's unquantised coefficients beside each shard; the OGSE class from the
20
- quantised replay pack, its quantisation charged in its own certificate term.
21
 
22
  **Choose the dataset by the question.** Use this one for an acquisition on one of the waveforms below, where every
23
  other knob stays free. Use the replay pack for any other waveform: another δ or Δ, another OGSE, an arbitrary gradient.
@@ -25,9 +25,12 @@ other knob stays free. Use the replay pack for any other waveform: another δ or
25
  ## What a timing class leaves free
26
 
27
  Each class fixes the waveform's shape in time: a square-lobe class's terms are constant-gradient intervals, the OGSE
28
- class's two terms are its cosine blocks as stored. Within a class, everything else is chosen at replay time:
 
29
 
30
  - the b-value and direction of every measurement, up to the class's gradient amplitude;
 
 
31
  - the per-voxel delivered gradient (gradient nonlinearity, a background gradient; square-lobe classes);
32
  - T₂ and T₁ per pool, and surface relaxivity ρ₂ ≤ 3 µm/s;
33
  - B₀ of 1.5, 3 or 7 T in any direction, and myelin χ up to 2 × 10⁻⁷, isotropic and anisotropic.
@@ -42,10 +45,13 @@ class's two terms are its cosine blocks as stored. Within a class, everything el
42
  | `d17.7-D35.8` | δ 17.7, Δ 35.8 | spin echo, TE 53.5 |
43
  | `ste-d7.6-TM38.3` | δ 7.6, mixing time 38.3 | stimulated echo |
44
  | `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 |
 
45
 
46
  Each class's widths are derived at the 1.003 % floor. The layout's own error bound is in `disco/manifest.json` under
47
  `certificate`, term by term. Against the replay pack, 16 voxels in the seven square-lobe classes agree to 2.2 × 10⁻⁵,
48
- and in the OGSE class to 2.2 × 10⁻⁴ bare and 4.5 × 10⁻⁴ with every tier at 3 T, against a combined bound of 3.0 × 10⁻³.
 
 
49
 
50
  ## Use me
51
 
@@ -69,7 +75,7 @@ S_3T, _ = layout.image(seq, tissue=wm, scanner=3.0) # relaxation, wall
69
  ```
70
 
71
  `layout.fit(seq)` names the class a sequence fits, or says why it fits none; an OGSE row is the class's stored shape
72
- scaled to its b (`layout.partitions["cos-f50-T20"].shapes`). `partitions=None` fetches every class (43 GB).
73
  `resident=False` streams the packed words to the device a chunk at a time: one class of the whole grid with every
74
  tier in about 3.4 GB (dmipy-sim#823); `resident=True` keeps them there across calls (about 21 GB with the tiers).
75
 
 
12
 
13
  # DiSCo time segments
14
 
15
+ The DiSCo replay walk, contracted at nine gradient waveforms -- DiSCo's own timings, the replay paper's clinical,
16
+ research and Connectome 2.0 timings, a stimulated echo, a cosine OGSE and a b-tensor encoding class -- so that a
17
+ 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
18
  [SubstrateCommons/disco-replay](https://huggingface.co/datasets/SubstrateCommons/disco-replay). The seven square-lobe
19
+ classes were contracted from the walk's unquantised coefficients beside each shard; the OGSE and b-tensor classes
20
+ from the quantised replay pack, each one's quantisation charged in its own certificate term.
21
 
22
  **Choose the dataset by the question.** Use this one for an acquisition on one of the waveforms below, where every
23
  other knob stays free. Use the replay pack for any other waveform: another δ or Δ, another OGSE, an arbitrary gradient.
 
25
  ## What a timing class leaves free
26
 
27
  Each class fixes the waveform's shape in time: a square-lobe class's terms are constant-gradient intervals, the OGSE
28
+ class's two terms are its cosine blocks as stored, the b-tensor class's six terms are its three gradient shapes on
29
+ each side of the 180. Within a class, everything else is chosen at replay time:
30
 
31
  - the b-value and direction of every measurement, up to the class's gradient amplitude;
32
+ - on the b-tensor class, the encoding itself: any linear, planar or spherical b-tensor of its three shapes, at any
33
+ rotation and b;
34
  - the per-voxel delivered gradient (gradient nonlinearity, a background gradient; square-lobe classes);
35
  - T₂ and T₁ per pool, and surface relaxivity ρ₂ ≤ 3 µm/s;
36
  - B₀ of 1.5, 3 or 7 T in any direction, and myelin χ up to 2 × 10⁻⁷, isotropic and anisotropic.
 
45
  | `d17.7-D35.8` | δ 17.7, Δ 35.8 | spin echo, TE 53.5 |
46
  | `ste-d7.6-TM38.3` | δ 7.6, mixing time 38.3 | stimulated echo |
47
  | `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 |
48
+ | `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 |
49
 
50
  Each class's widths are derived at the 1.003 % floor. The layout's own error bound is in `disco/manifest.json` under
51
  `certificate`, term by term. Against the replay pack, 16 voxels in the seven square-lobe classes agree to 2.2 × 10⁻⁵,
52
+ 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,
53
+ 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
54
+ of 3.0 × 10⁻³.
55
 
56
  ## Use me
57
 
 
75
  ```
76
 
77
  `layout.fit(seq)` names the class a sequence fits, or says why it fits none; an OGSE row is the class's stored shape
78
+ scaled to its b (`layout.partitions["cos-f50-T20"].shapes`). `partitions=None` fetches every class (55 GB).
79
  `resident=False` streams the packed words to the device a chunk at a time: one class of the whole grid with every
80
  tier in about 3.4 GB (dmipy-sim#823); `resident=True` keeps them there across calls (about 21 GB with the tiers).
81
 
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