Java-based remote viewing and processing of nuclear medicine images: toward "the imaging department without walls".
Bibliographic record
Abstract
UNLABELLED: In nuclear medicine practice, images often need to be reviewed and reports prepared from locations outside the department, usually in the form of hard copy. Although hard-copy images are simple and portable, they do not offer electronic data search and image manipulation capabilities. On the other hand, picture archiving and communication systems or dedicated workstations cannot be easily deployed at numerous locations. To solve this problem, we propose a Java-based remote viewing station (JaRViS) for the reading and reporting of nuclear medicine images using Internet browser technology. METHODS: JaRViS interfaces to the clinical patient database of a nuclear medicine workstation. All JaRViS software resides on a nuclear medicine department server. The contents of the clinical database can be searched by a browser interface after providing a password. Compressed images with the Java applet and color lookup tables are downloaded on the client side. This paradigm does not require nuclear medicine software to reside on remote computers, which simplifies support and deployment of such a system. To enable versatile reporting of the images, color tables and thresholds can be interactively manipulated and images can be displayed in a variety of layouts. Image filtering, frame grouping (adding frames), and movie display are available. Tomographic mode displays are supported, including gated SPECT. RESULTS: The time to display 14 lung perfusion images in 128 x 128 matrix together with the Java applet and color lookup tables over a V.90 modem is <1 min. SPECT and PET slice reorientation is interactive (<1 s). JaRViS could run on a Windows 95/98/NT or a Macintosh platform with Netscape Communicator or Microsoft Intemet Explorer. The performance of Java code for bilinear interpolation, cine display, and filtering approaches that of a standard imaging workstation. CONCLUSION: It is feasible to set up a remote nuclear medicine viewing station using Java and an Internet or intranet browser. Images can be made easily and cost-effectively available to referring physicians and ambulatory clinics within and outside of the hospital, providing a convenient alternative to film media. We also find this system useful in home reporting of emergency procedures such as lung ventilation-perfusion scans or dynamic studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".