Approaches for improving image quality in magnetic induction tomography
Bibliographic record
Abstract
Magnetic induction tomography (MIT) is a contactless and non-invasive method for imaging the passive electrical properties of objects. Measuring the weak signal produced by eddy currents within biological soft tissues can be challenging in the presence of noise and the large signals resulting from the direct excitation-detection coil coupling. To detect haemorrhagic stroke in the brain, for instance, high measurement accuracy is required to enable images with enough contrast to differentiate between normal and haemorrhaged brain tissues. The reconstructed images are often very sensitive to inevitable measurement noise from the environment, system instabilities and patient-related artefacts such as movement and sweating. We propose methods for mitigating signal noise and improving image reconstruction. We evaluated and compared the use of a range wavelet transforms for signal denoising. Adaptive regularization methods including L-curve, generalized cross validation (GCV) and noise estimation were also compared. We evaluated all these described methods with measurements of in vitro tissues resembling a peripheral haemorrhagic cerebral stroke created by placing a bio-membrane package filled with 10 ml blood in a swine brain of 100 ml. We show that wavelet packet denoising combined with adaptive regularization can improve the quality of reconstructed images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".