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Record W1997541925 · doi:10.1118/1.4736333

TH‐C‐217BCD‐04: 3D Ultrasound Reconstruction from Freehand Scans Using an Optical Tracking System

2012· article· en· W1997541925 on OpenAlexaff
Navid Samavati, Raluca Maria Vlad, Hadi Tadayyon, Joanne Moseley, Sara Iradji, Gregory J. Czarnota, KK Brock

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsUltrasound3D ultrasoundComputer visionTracking (education)Iterative reconstructionInterpolation (computer graphics)Artificial intelligenceTransducerVolume (thermodynamics)Computer scienceBrachytherapyOrientation (vector space)Biomedical engineeringRadiologyMathematicsMedicinePhysicsAcousticsMotion (physics)Radiation therapyGeometry

Abstract

fetched live from OpenAlex

Purpose: To develop a 3D‐ultrasound image reconstruction method from scans collected with a freehand 2D‐ultrasound probe and a tracking system affixed to the probe for coordinate determination. A freehand ultrasound probe can be used to obtain arbitrary volumes since the motion of the probe is unconstrained. However the 2D images are limited to a thin plane at an arbitrary angle in the volume of interest. The 3D‐ultrasound image reconstruction overcomes this limitation and allows accurate correlation with other 3D imaging modalities. Methods: A tracking system (Polaris Vicra system) which measures the 3D positions of markers affixed to the freehand ultrasound probe was used to determine the coordinates of the transducer during the scanning procedure. The tracking tool was calibrated to determine the location of each ultrasound imaging frame. This allowed the relative position of each ultrasound image plane to its neighboring images to be determined. The image intensities of the new 3D ultrasound volume were calculated by linear interpolation as the weighted average of the pixel values of nearest neighbors among the embedded 2D image intensities. Results: 3D ultrasound reconstructed volumes from freehand scanning of breast tumors clearly demonstrates the location and size of the tumor. The reconstruction accuracy was evaluated by comparing known inclusion volumes in a dedicated brachytherapy and breast ultrasound phantoms with the volumes obtained from the ultrasound reconstruction. The inclusions were contoured on each 2D slices obtained from the ultrasound reconstruction. The known volumes versus the measured volumes were (9.0/8.6 cc; 4.0/4.1cc; 0.27/0.25 cc) with an overall relative error of 5%. Conclusions: Accurate 3D ultrasound image reconstruction is possible using a tracker tool attached to the ultrasound freehand probe. The method offers the freedom of scanning large volumes of interest in breast and allows registration with other 3D imaging modalities. Terry Fox New Frontiers Program Project in Ultrasound for Cancer Therapy Dr. Brock has financial interest in deformable registration technology through the licensing of Morfeus to RaySearch Laboratories.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.022
GPT teacher head0.290
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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