Laboratory coherent-scatter analysis of intact urinary stones with crystalline composition: a tomographic approach
Bibliographic record
Abstract
Knowledge of urinary stone composition and structure provides important insights in guiding treatment and preventing recurrence. No current method can successfully provide information relating structure and composition of intact stones. We are developing a tomographic technique that uses measures of coherently scattered diagnostic x-rays to yield stone composition. Coherent-scatter (CS) properties depend on molecular structure and are, therefore, sensitive to material composition. Powdered, amorphous or polycrystalline materials with no significant orientation produce circularly symmetric CS patterns. However, in materials with preferred crystallite orientation, like urinary stones, bright spots in CS patterns are observed. This compromises a composition analysis based on comparing CS measurements from calculi to a library of CS signatures from powdered chemicals. We show that a computed tomographic reconstruction of CS measurements using filtered backprojection (CSCT) effectively eliminates bright spots and yields CS patterns equivalent to powdered materials. This allows for direct comparison with a powdered chemical reference library to establish composition. Validation is achieved through a tomographic CS analysis of an aluminium (Al) rod phantom. Much like calculi, CS patterns from a solid polycrystalline Al rod exhibit diffraction spots, absent in the ring-like Al powder CS pattern. We show that the reconstructed Al CS cross-section is equivalent to its powdered counterpart and results in clearly defined composition images. The potential of CSCT to identify stone composition is demonstrated through images of intact stones deemed chemically pure by infrared spectroscopy. Computed tomographic reconstruction of CS signals allowed the generation of composition maps, showing the distribution of stone components. These images provide strong evidence that current laboratory techniques risk missing critical stone components due to inadequate sampling. This is of particular importance since follow-up treatments are based on these composition analyses. CS analysis can distinguish common stone components and can provide topographic composition maps of intact stones. Such details offer invaluable clinical information regarding stone formation, treatment and follow-up, and thus support the development of CS analysis as a laboratory stone analysis technique.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".