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Record W2032679179 · doi:10.1118/1.3469277

TU‐D‐201C‐02: Subharmonic Imaging and Pressure Estimation

2010· article· en· W2032679179 on OpenAlexaboutno aff
Flemming Forsberg

Bibliographic record

VenueMedical Physics · 2010
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMammographyMedicineGrayscaleSecond-harmonic imaging microscopyUltrasoundMicrobubblesBreast cancerNuclear medicineMedical imagingRadiologySubharmonicContrast (vision)CancerInternal medicineOpticsSecond-harmonic generationNonlinear systemLaserPhysics

Abstract

fetched live from OpenAlex

The use of gas filled microbubbles as contrast agents for ultrasound imaging (US) is well established by now. Such contrast agents are used world‐wide to improve the diagnostic capabilities of US especially when employed in combination with novel nonlinear contrast imaging modes such pulse inversion second harmonic and subharmonic imaging (SHI). Our group has investigated the use of SHI in breast imaging and has produced the first ever human SHI images as well as developed a new contrast imaging mode: dynamic cumulative maximum intensity (CMI) SHI. Fourteen patients with 16 breast lesions, who underwent breast biopsies with histopathological assessment, participated in a pilot study of mammography and contrast US. A Logiq 9 scanner (GE Healthcare, Milwaukee, WI) was modified to perform grayscale SHI (transmitting/receiving at 4.4/2.2 MHz). Of the 16 lesions, 4 were malignant. Mammography had a sensitivity of 100 % and a specificity of 20 %. Baseline grayscale US and PDI both achieved a sensitivity of 50 % and a specificity of 92 %, while contrast‐enhanced PDI produced 75 % and 75 %, respectively. SHI had a sensitivity of 75 % and specificity of 83 %. All the ultrasound modes produced higher specificities than mammography (p<0.04). The area under the ROC curve (Az) for the diagnosis of breast cancer was 0.64 for grayscale and PDI, 0.67 with contrast enhanced PDI, 0.76 for mammography and 0.78 for SHI. For dynamic CMI‐SHI mode the Az increased to 0.90 and this was significantly better (p=0.03) than mammography. More recently, the utility of contrast microbubbles for quantitative measurements of hydrostatic pressure (in mmHg) have been explored. Changes in ambient pressure affect the reflectivity of ultrasound contrast microbubbles leading to an excellent correlation between subharmonic signals and hydrostatic pressure (r2>0.90). We have proposed subharmonic aided pressure estimation (SHAPE; U.S. patent 6,302,845) and provided proof of concept of the feasibility of in vivo SHAPE. The heart and aorta of four dogs were scanned with a Sonix RP scanner (Ultrasonic Medical Corporation, Richmond, BC, Canada) modified to perform SHAPE. Simultaneously, the instantaneous pressures within the aorta were measured using a manometer‐tipped catheter. The instantaneous in vivo pressure measurements and those based on SHAPE were in good accordance over a pressure range of 0 to 70 mmHg (r2 from 0.65–0.85). In conclusion, a new contrast specific imaging technique, SHI, has been investigated for in vivo breast imaging and for quantitative measurements. SHI appear to improve the diagnosis of breast cancer relative to conventional ultrasound and mammography; albeit based on a very limited patient population. Some encouraging results in pressure estimation have been achieved. Learning Objectives: 1. Understand the origin of subharmonic bubble signals 2. Understand the pro's and con's of SHI 3. Understand the principle of SHAPE

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)0.003
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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
Published2010
Admission routes1
Has abstractyes

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