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Record W2071633708 · doi:10.1118/1.4897382

Spatiotemporal filtering of MR‐temperature artifacts arising from bowel motion during transurethral MR‐HIFU

2014· article· en· W2071633708 on OpenAlexafffund
Alain Schmitt, Charles Mougenot, Rajiv Chopra

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsPhilips (Canada)Sunnybrook Health Science Centre
FundersNational Cancer InstituteFedDev OntarioNational Institutes of Health
KeywordsArtifact (error)Magnetic resonance imagingProstateNoise (video)Filter (signal processing)Imaging phantomUltrasoundBiomedical engineeringMaterials scienceComputer scienceNuclear medicineComputer visionRadiologyPhysicsAcousticsMedicineCancer

Abstract

fetched live from OpenAlex

PURPOSE: Transurethral MR-HIFU is a minimally invasive image-guided treatment for localized prostate cancer that enables precise targeting of tissue within the gland. The treatment is performed within a clinical MRI to obtain real-time MR thermometry used as an active feedback to control the spatial heating pattern in the prostate and to monitor for potential damage to surrounding tissues. This requires that the MR thermometry measurements are an accurate representation of the true tissue temperature. The proton resonance frequency shift thermometry method used is sensitive to tissue motion and changes in the local magnetic susceptibility that can be caused by the motion of air bubbles in the rectum, which can impact the performance of transurethral MR-HIFU in these regions of the gland. METHODS: A method is proposed for filtering of temperature artifacts based on the temporal variance of the temperature, using empirical and dynamic positional knowledge of the ultrasonic heating beam, and an estimation of the measurement noise. A two-step correction strategy is introduced which eliminates artifact-detected temperature variations while keeping the noise level low through spatial averaging. RESULTS: The filter has been evaluated by postprocessing data from five human transurethral ultrasound treatments. The two-step correction process led to reduced final temperature standard deviation in the prostate and rectum areas where the artifact was located, without negatively affecting areas distal to the artifact. The performance of the filter was also found to be consistent across all six of the data sets evaluated. The evaluation of the detection criterion parameter M determined that a value of M = 3 achieves a conservative filter with minimal loss of spatial resolution during the process. CONCLUSIONS: The filter was able to remove most artifacts due to the presence of moving air bubbles in the rectum during transurethral MR-HIFU. A quantitative estimation of the filter capabilities shows a systematic improvement in the standard deviation of the corrected temperature maps in the rectum zone as well as in the entire acquired slice.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.594

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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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 designBench or experimental
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

Citations16
Published2014
Admission routes2
Has abstractyes

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