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Record W2062190821 · doi:10.1145/1836845.1836849

A framework for GPU accelerated needle insertion simulation using meshfree methods

2010· article· en· W2062190821 on OpenAlexaff
Aria Shahingohar, Roy Eagleson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Analysis Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceFinite element methodMeshfree methodsBrachytherapyCUDAComputational scienceParallel computingRadiologyEngineeringStructural engineeringRadiation therapyMedicine

Abstract

fetched live from OpenAlex

The simulation of needle insertion is an important research area that has many applications in robotic and image guided brachytherapy cancer treatment, biopsies, and neurosurgery. Modeling of soft tissue plays an important role in the needle insertion simulation, but the use of Finite Element Method is complicated due to the need for remeshing in the neighbourhood of the needle tip. We are proposing to use a meshfree method for the tissue deformation modeling, in which new tissue nodes are added on the needle shaft as the needle is inserted into the tissue. In addition, we have utilized Nvidia's CUDA technology to accelerate the methods used in our framework.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.387
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.075
GPT teacher head0.428
Teacher spread0.353 · 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 teacher head, not a consensus.

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

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

Citations1
Published2010
Admission routes1
Has abstractyes

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