MatMRI and MatHIFU: Matlab{trade mark, serif} toolboxes for real-time monitoring and control of MR-HIFU
Bibliographic record
Abstract
Background. Availability of open tools is a key feature to facilitate the development of pre-clinical research of Magnetic Resonance-guided High Intensity Focused Ultrasound (MR-HIFU). MatMRI is a toolbox that allows direct communication with a Philips{trade mark, serif} MRI scanner in a Matlab{trade mark, serif} environment, which is well-known in many laboratories. MatMRI performs real-time acquisition of magnitude and phase images that can be processed to estimate changes of temperature. Available functionality of MatMRI includes acquisition of individual slices and volumetric data. Analogously to MatMRI, MatHIFU is a toolbox for the control of the Philips Sonalleve MR-HIFU system. MatHIFU allows the execution of user-defined treatment protocols such as thermal ablation, hyperthermia or drug delivery. MatMRI and MatHIFU can be used independently or in combination. Methods. MatMRI was based on the official tool for MRI data-dumping made by Philips Healthcare. Multi-threading capabilities were added to maximize real-time processing performance. Basic use of MatMRI involves four basic steps: initiate communication, subscribe to MRI data, query for new images and unsubscribe. If required, MatMRI can also pause/resume the scanning and update on real-time the location and orientation of the images. MatHIFU performs the execution of sonication protocols and allows real-time monitoring. Basic use of MatHIFU requires also four steps: preparation of sonication protocol, initiate communication, execute sonication protocol and monitor the state of execution. Results. MatMRI was integrated into existing software used to control a table designed for animal experimentation (FUS Instruments, Canada). The integration in the existing software was seamless and delivered real-time estimation of changes of temperature in a mouse model. Using MatHIFU and MatMRI, a complete new interface to control the Sonalleve system was developed to perform in vivo experiments allowing adapted conditions for the experimental model. Conclusions. MatMRI and MatHIFU leverage considerably the existing MRI and MR-HIFU platforms to conduct pre-clinical research. MatMRI simplifies considerably the efforts required to perform real-time measurements of MR data and its possibilities are beyond thermal applications. MatHIFU facilitates the exploration of new therapeutic applications using the Philips MR-HIFU Sonalleve system. Both matMRI and matHIFU are aimed to be freely available to other research groups under coordination of Philips Healthcare.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.142 | 0.099 |
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".