Sparse Correlated Diffusion Imaging: A New Computational Diffusion MRI Modality for Prostate Cancer Detection
Bibliographic record
Abstract
Diffusion weighted imaging (DWI) is a promising magnetic resonanceimaging (MRI) modality with wide applications in diagnosisof different types of diseases such as prostate cancer. DWI providesa large amount of imaging data which often makes it difficultto interpret accurately, mainly due to the fact that much of informationin diffusion imaging cannot be deciphered by human expertsalone. Computational diffusion MRI (CD-MRI) aims to leveragecomputational means to generate imagery from diffusion signalswhich are easier to interpret by human experts. Recently, anew CD-MRI modality called correlated diffusion imaging (CDI) hasbeen proposed which takes advantage of the joint correlation of diffusionsignal attenuation across multiple gradient pulse strengthsand timings to improve the separability of cancerous and healthytissues. In this paper, we propose a new CD-MRI modality calledSparse CDI (sCDI) where an optimally sparse subset of diffusionsignals contributes to the formation of the final diffusion signal leadingto further separation of cancerous and healthy tissue in prostategland compared to CDI and conventional DWI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".