The targeting accuracy of a preclinical MRI‐guided focused ultrasound system
Bibliographic record
Abstract
PURPOSE: Assess the accuracy, precision, and sources of error using a preclinical MR-guided focused ultrasound system. METHODS: A preclinical focused ultrasound system, described previously [Chopra et al., Med. Phys. 36(5), 1867-1874 (2009)], was tested on a benchtop and with 3T GE, 3T Philips, and 7 T Bruker MR scanners for spatial targeting accuracy and precision. Randomly distributed water-filled holes drilled into a polystyrene plate were imaged using MRI and targeted using treatment planning software. The ultrasound focus of a 72 mm, f-number 0.8, 1.16 MHz transducer was aimed at the target locations, and 1-2 s continuous-wave sonications were performed on clear polystyrene plates to create localized spots of melted plastic. The distance between target and observed locations was measured and analyzed. Retrospective analysis of targeting accuracy was performed on preclinical data obtained from other experiments at their institution using the same system. RESULTS: The results suggest that the sources of targeting error under MR guidance can be roughly separated into three components--normally distributed random error; constant shift from inaccuracy in detection of the initial ultrasound focus; and angular misalignment between MR and focused ultrasound (FUS) coordinates. The lower bound on the targeting error was estimated to be 0.25 ± 0.13 mm, while the maximum observed targeting error did not exceed 2 mm. Measures required to reduce errors and improve targeting were developed to reduce the registration and misalignment errors such that maximum error was reduced to 0.36 ± 0.14 mm. Retrospective in vivo analysis indicated that the error was 1.02 ± 0.43 mm, including error extrinsic to the system. CONCLUSIONS: The FUS system, as described, is capable of precise and accurate sonications. The largest source of error--misregistration of the coordinate systems of the scanner and ultrasound system--was addressed which reduced the error to 0.36 ± 0.14 mm, sufficient for many preclinical applications.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.001 |
| 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".