Fusion of real-time transrectal ultrasound with preacquired MRI for multimodality prostate imaging
Bibliographic record
Abstract
A system for fusion of realtime transrectal ultrasound (TRUS) with pre-acquired 3D images of the prostate was designed and demonstrated in phantoms and volunteer patients. Biopsy guides for endocavity ultrasound transducers were equipped with customized 6 degree-of-freedom (DoF) electromagnetic (EM) tracking sensors, compatible with the Aurora EM tracking system (Northern Digital Inc, NDI, Waterloo, ON, Canada). The biopsy guides were attached to an ultrasound probe and calibrated to map tracking coordinates with ultrasound image coordinates. Six cylindrical gold seeds were placed in a prostate phantom to serve as fiducial markers. The fiducials were first identified manually in 3T magnetic resonance (MR) images collected with an endorectal coil. The phantom was then imaged with tracked realtime TRUS and the fiducial markers were identified in the live image using custom software. Rigid registrations between MR and ultrasound image space were computed and evaluated using subsets of the fiducial markers. Twelve patients were scanned with 3T MRI and TRUS for biopsy and seed placement. In ten patients, volumetric ultrasound images were reconstructed from 2D sweeps of the prostate and were manually registered with the MR. The rigid registrations were used to display live TRUS images fused with spatially corresponding realtime multiplanar reconstructions (MPRs) of the MR image volume. Registration accuracy was evaluated by segmenting the prostate in the MR and volumetric ultrasound and computing distance measures between the two segmentations. In the phantom experiments, registration accuracies of 2.2 to 2.3 mm were achieved. In the patient studies, the average root mean square distance between the MR and TRUS segmentations was 3.1 mm, the average Hausdorff distance was 9.8 mm. Deformation of the prostate during MR and TRUS scan was identified as the primary source of error. Realtime MR/TRUS image fusion is feasible and is a promising approach to improved target visualization during TRUS-guided biopsy or therapy procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".