Measuring the mutual effects between a CZT detector and MRI for the development of a simultaneous MBI/MRI insert
Bibliographic record
Abstract
While mammography is the gold standard for breast cancer screening, it suffers from poor sensitivity in women with dense breast tissue. Both breast MRI and molecular breast imaging (MBI) have been used as secondary imaging techniques. However, breast MRI suffers from low specificity and low sensitivity in MBI. A CZT based detector system has been developed with the goal of simultaneous MBI/MRI imaging to address the shortcomings of each modality. The performance of each modality needs to be addressed separately and together. The CZT system is comprised of four Redlen CZT modules tiled in a 2x2 array. Each module consists of 256 pixels and feature a builtin on-board ASIC and FPGA. A custom digital readout circuit board was designed to interface the four modules with a microcontroller to a PC. MR images were acquired with a 3T GE Discovery MR750 and Hologic breast coils. A gradient echo imaging sequence was used for all image acquisitions. A tissue mimicking phantom with a plastic grid insert (1 cm spacing) was used to evaluate geometric accuracy with the CZT detectors in the MRI bore. The average distance between the grid markers was 1A}0.2cm indicating negligible geometric distortion. Field maps were generated with a uniform phantom to quantify the effect on magnetic field homogeneity. Early results indicate a significant distortion (~10ppm) in the magnetic field closest to the coil. Further analysis of the MR images will determine the extent of image quality degradation. A flood map of Tc-99m was acquired to evaluate and implement an energy correction map and a uniformity map. In the absence of a magnetic field, the mean energy resolution at 140keV was 6.3%. After fully characterizing the uniformity, geometric accuracy and sensitivity, the same metrics will be evaluated in the MRI bore.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".