Comparison of Diffusion Kurtosis Imaging to Diffusion Basis Spectrum Imaging in Healthy Young Adults
Bibliographic record
Abstract
Diffusion Tensor Imaging is sensitive to changes in microstructure, but in a way that can be non-specific to the underlying microstructural cause. For example, neurite dispersion and demyelination both lead to decreased FA. These ambiguities in interpretation, as well as advances in image acquisition, have motivated the development of more detailed models of diffusion. Two such models are Diffusion Kurtosis Imaging (DKI) [Jensen 2005] and Diffusion Basis Spectrum Imaging (DBSI) [Ramirez Manzanares 2007]. DKI is a mathematical model of the higher-order properties of the diffusion profile, while DBSI is a biophysically informed model of the underlying tissue microstructure. Three commonly used measures from DKI are: Mean Kurtosis (MK) Axial Kurtosis (AK), Radial Kurtosis(RK). These parameters represent the degree of nongaussianity in the diffusion profile generally for MK, parallel to the principal diffusion direction for AK, and perpendicular for RK. For DBSI, there are four main measures: Water Ratio (WR), Fiber Ratio (FR), Hindered Ratio (HR) and Restricted Ratio (RR). These measures represent the proportion of signal assigned to various compartments by the DBSI model, and they sum to unity. These models have not been directly compared in humans. In this study, we examined the relationship between the parameters calculated using both of these models fitted to the same diffusion data in healthy young adults. This is an exploratory study of the relationship between DKI and DBSI parameters across multiple human subjects. This poster was presented at the 2017 meeting of the Organization for Human Brain Mapping in Vancouver, BC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".