Kompression digitaler Bilddaten in der Radiologie – Ergebnisse einer Konsensuskonferenz
Bibliographic record
Abstract
PURPOSE: Recommendations for lossy compression of digital radiological DICOM images in Germany by means of a consensus conference. The compression of digital radiological images was evaluated in many studies . Even though the results demonstrate full diagnostic image quality of modality-dependent compression between 1:5 and 1:200, there are only a few clinical applications. MATERIALS AND METHODS: A consensus conference with approx. 80 interested participants (radiology, industry, physics, and agencies) without individual invitation was organized by the working groups AGIT and APT of the German Roentgen Society DRG to determine compression factors without loss of diagnostic image quality for different anatomical regions for CT, CR/DR, MR, RF/XA examinations. The consent level was specified as at least 66 %. RESULTS: For individual modalities the following compression factors were recommended: CT (brain) 1:5, CT (all other applications) 1:8, CR/DR (all applications except mammography) 1:10, CR/DR (mammography) 1:15, MR (all applications) 1:7, RF/XA (fluoroscopy, DSA, cardiac angio) 1: 6. The recommended compression ratios are valid for JPEG and JPEG 2000 /Wavelet compressions. CONCLUSION: The results may be understood as recommendations and indicate limits of compression factors with no expected reduction of diagnostic image quality. They are similar to the current national recommendations for Canada and England .
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 | 0.052 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".