Signal-to-noise ratio evaluation of magnetic resonance images in the presence of an ultrasonic motor
Bibliographic record
Abstract
BACKGROUND: Safe robot-assisted intervention using magnetic resonance imaging (MRI) guidance requires the precise control of assistive devices, and most currently available tools are rarely MRI-compatible. To obtain high precision, it is necessary to characterize and develop existing MRI-safe actuators for use in a high magnetic field (≥3 T). Although an ultrasonic motor (USM) is considered to be an MRI-safe actuator, and can be used in the vicinity of a high field scanner, its presence interferes with MR images. Although an MR image provides valuable information regarding the pathology of a patient's body, noise, generally of a granular type, decreases the quality of the image and jeopardizes the true evaluation of any existing pathological issues. An eddy current induced in the conductor material of the motor structure can be a source of noise when the motor is close to the isocenter of the image. We aimed to assess the effects of a USM on the signal-to-noise ratio (SNR) of MR images in a 3-T scanner. The SNR was compared for four image sequences in transverse directions for three orientations of the motor (x, y, and z) when the motor was in the "off" state. The SNR was evaluated to assess three artifact reduction methods used to minimize the motor-induced artifacts. RESULTS: The SNR had a range of 5-10 dB for slices close to the motor in the x and y orientations, and increased to 15-20 dB for slices far from the motor. Averaging the SNR for slices in all cases gave an SNR loss of about 10 dB. The maximum SNR was measured in the z orientation. In this case, the SNR loss was almost the same as that of other motor orientations, approximately 10 dB, but with a higher range, approximately 20-40 dB. CONCLUSIONS: The selection of certain scanning parameters is necessary for reducing motor-generated artifacts. These parameters include slice selection and bandwidth. In developing any MRI-compatible assisted device actuated by a USM, this study recommends the use of an approximately 3-mm slice thickness with minimum bandwidth to achieve optimized SNR values when a USM is operating close to (within approximately 40 mm) the region being imaged. The SNR can be further enhanced by increasing the number of signal averages, but this is achieved only at the cost of increased scan duration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".