Feature-space assessment of electrical impedance tomography coregistered with computed tomography in detecting multiple contrast targets
Bibliographic record
Abstract
PURPOSE: Fusion of electrical impedance tomography (EIT) with computed tomography (CT) can be useful as a clinical tool for providing additional physiological information about tissues, but requires suitable fusion algorithms and validation procedures. This work explores the feasibility of fusing EIT and CT images using an algorithm for coregistration. The imaging performance is validated through feature space assessment on phantom contrast targets. METHODS: EIT data were acquired by scanning a phantom using a circuit, configured for injecting current through 16 electrodes, placed around the phantom. A conductivity image of the phantom was obtained from the data using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). A CT image of the phantom was also acquired. The EIT and CT images were fused using a region of interest (ROI) coregistration fusion algorithm. Phantom imaging experiments were carried out on objects of different contrasts, sizes, and positions. The conductive medium of the phantoms was made of a tissue-mimicking bolus material that is routinely used in clinical radiation therapy settings. To validate the imaging performance in detecting different contrasts, the ROI of the phantom was filled with distilled water and normal saline. Spatially separated cylindrical objects of different sizes were used for validating the imaging performance in multiple target detection. Analyses of the CT, EIT and the EIT/CT phantom images were carried out based on the variations of contrast, correlation, energy, and homogeneity, using a gray level co-occurrence matrix (GLCM). A reference image of the phantom was simulated using EIDORS, and the performances of the CT and EIT imaging systems were evaluated and compared against the performance of the EIT/CT system using various feature metrics, detectability, and structural similarity index measures. RESULTS: In detecting distilled and normal saline water in bolus medium, EIT as a stand-alone imaging system showed contrast discrimination of 47%, while the CT imaging system showed a discrimination of only 1.5%. The structural similarity index measure showed a drop of 24% with EIT imaging compared to CT imaging. The average detectability measure for CT imaging was found to be 2.375 ± 0.19 before fusion. After complementing with EIT information, the detectability measure increased to 11.06 ± 2.04. Based on the feature metrics, the functional imaging quality of CT and EIT were found to be 2.29% and 86%, respectively, before fusion. Structural imaging quality was found to be 66% for CT and 16% for EIT. After fusion, functional imaging quality improved in CT imaging from 2.29% to 42% and the structural imaging quality of EIT imaging changed from 16% to 66%. The improvement in image quality was also observed in detecting objects of different sizes. CONCLUSIONS: The authors found a significant improvement in the contrast detectability performance of CT imaging when complemented with functional imaging information from EIT. Along with the feature assessment metrics, the concept of complementing CT with EIT imaging can lead to an EIT/CT imaging modality which might fully utilize the functional imaging abilities of EIT imaging, thereby enhancing the quality of care in the areas of cancer diagnosis and radiotherapy treatment planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".