Using surface markers for MRI guided breast conserving surgery: a feasibility survey
Bibliographic record
Abstract
Breast MRI is frequently performed prior to breast conserving surgery in order to assess the location and extent of the lesion. Ideally, the surgeon should also be able to use the image information during surgery to guide the excision and this requires that the MR image is co-registered to conform to the patient's position on the operating table. Recent progress in MR imaging techniques has made it possible to obtain high quality images of the patient in the supine position which significantly reduces the complexity of the registration task. Surface markers placed on the breast during imaging can be located during surgery using an external tracking device and this information can be used to co-register the images to the patient. There remains the problem that in most clinical MR scanners the arm of the patient has to be placed parallel to the body whereas the arm is placed perpendicular to the patient during surgery. The aim of this study is to determine the accuracy of co-registration based on a surface marker approach and, in particular, to determine what effect the difference in a patient's arm position makes on the accuracy of tumour localization. Obtaining a second MRI of the patient where the patient's arm is perpendicular to body axes (operating room position) is not possible. Instead we obtain a secondary MRI scan where the patient's arm is above the patient's head to validate the registration. Five patients with enhancing lesions ranging from 1.5 to 80 cm(3) in size were imaged using contrast enhanced MRI with their arms in two positions. A thin-plate spline registration scheme was used to match these two configurations. The registration algorithm uses the surface markers only and does not employ the image intensities. Tumour outlines were segmented and centre of mass (COM) displacement and Dice measures of lesion overlap were calculated. The relationship between the number of markers used and the COM-displacement was also studied. The lesion COM-displacements ranged from 0.9 to 9.3 mm and the Dice overlap score ranged from 20% to 80%. The registration procedure took less than 1 min to run on a standard PC. Alignment of pre-surgical supine MR images to the patient using surface markers on the breast for co-registration therefore appears to be feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".