Data · dataset · 2026
Simulation Dataset for Deuterium Metabolic Imaging (Augmented with Brain Tissue Priors and Random Patterns)
Listed in ScienceDB
This dataset is generated using the bSSFP‑DMI forward signal model to provide simulated training data for metabolite separation under short-readout conditions.
Description
Metabolite spatial maps (HDO, glucose, lactate) are synthesized by linearly combining brain tissue segmentation masks (CSF/GM/WM) from the IXI dataset to mimic tissue-dependent distributions, while B₀ field maps are generated using a multi-scale spline random-field model to ensure spatial smoothness.
To increase diversity, phantom samples with randomized geometric shapes and metabolite patterns are included, and complex circular Gaussian noise is added to the time-domain signals to simulate low-SNR acquisitions. Random T₁/T₂ relaxation perturbations are also applied to each sample.The data have a fixed map size of 32 × 32. The time-domain signal per voxel contains 128 sampling points (bandwidth 4065 Hz, dwell time 0.246 ms).
Read the rest (1 more)
Different readout lengths (M = 128, 72, 48, 36) can be obtained by simply taking the first M points from the 128-point signal.Each sample is stored as two mat files (.mat): the ground-truth file of size [Batch, 4, 32, 32] (channels: HDO, glucose, lactate, and B₀ map) and the signal file of size [Batch, 128, 32, 32] (128 time points per voxel). All data are complete with no missing entries, and signal values are within valid dynamic ranges.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.41685 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Analytical chemistry 73% · Imaging 75% · Simulation 75%
Provenance · 1 source records, 15 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.41685 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3401 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].local:field:chemistry | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:imaging | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |