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Record W2079356550 · doi:10.1118/1.4773043

A system for MRI‐guided transperineal delivery of needles to the prostate for focal therapy

2013· article· en· W2079356550 on OpenAlexafffund
Jeremy Cepek, Blaine A. Chronik, Uri Lindner, John Trachtenberg, Sean M. Davidson, Jeffrey Bax, Aaron Fenster

Bibliographic record

VenueMedical Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsOntario Institute for Cancer ResearchCentre for Imaging Technology CommercializationUniversity Health NetworkRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsScannerMagnetic resonance imagingInterventional magnetic resonance imagingAblationMedicineProstateVisualizationRadiologyBiomedical engineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: To demonstrate the capabilities of a new magnetic resonance imaging (MRI)-guided system for delivering needles to the prostate for focal therapy. Included is a presentation of the design of the system and its user interface, evaluation of MR-compatibility, and quantitative evaluation of guidance accuracy and repeatability within the bore of a clinical MRI scanner. METHODS: The use of MRI for visualization of tumors, intraoperative visualization of interventional tools, and thermometry for controlled ablation of lesions is becoming increasingly prevalent. In this work, the authors present a prototype system for guiding needles to prostate tumors within the bore of an MRI scanner for use in focal laser thermal ablation of prostate tumors. The system consists of a manually actuated trajectory alignment device that allows a physician to precisely align a set of needle guides with an intended target in the prostate within the bore of a clinical closed-bore MRI scanner. Needle insertion is then performed transperineally, with the patient in the bore of the MRI, and custom software provides monitoring of thermal ablative procedures. RESULTS: The system is shown to have a minimal effect on image distortion, and only a 6% decrease in image signal-to-noise ratio. Through needle insertion tests in tissue-mimicking phantoms, the system's potential for reliably guiding needles to intra-MR targets within 2.64 mm has been demonstrated. Use of the system to deliver focal laser ablation therapy to two patients showed that it can be used to deliver needles with minimal disruption of workflow, and in less time than when insertions are performed freehand or with a fixed grid template. CONCLUSIONS: A system for delivering needles to a patient's prostate for focal therapy within the bore of an MRI scanner has been developed. Results from needle insertion tests in phantoms suggest that the system has the potential to provide accurate delivery of focal therapy to prostate tumors of the smallest clinically significant size. Initial tests in two patients showed that needle deflection was larger than in phantoms, but methods of manually compensating for this effect were employed and needles were delivered to treatment sites with sufficient accuracy to deliver effective treatment. In addition, the treatment was delivered in less time than with a fixed grid template or freehand insertions. Despite this success, methods of reducing needle deflection are needed in order to fully utilize the potential of this system, and further reduce total procedure time.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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 designBench or experimental
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

Citations42
Published2013
Admission routes2
Has abstractyes

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