Evidence map›Paper›PMID 38598407›Full record

ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2024

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High-Intensity Focused Ultrasound Therapy.

Minho Song, Oleg A Sapozhnikov, Vera A Khokhlova, Tatiana D Khokhlova

Abstract read
In one paragraph

Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Minho Song
Oleg A Sapozhnikov
Vera A Khokhlova
Tatiana D Khokhlova

Funding

Ultrasound enhanced penetration for treatment of pancreatic cancerR01CA154451 · NCI · UNIVERSITY OF WASHINGTON · PI HWANG, JOO HA · 2011 to 2021
$4.3M
Pulsed Focused Ultrasound (pFUS) exposures and devices for tissue permeabilization without contrast agentsR01EB023910 · NIBIB · UNIVERSITY OF WASHINGTON · PI KHOKHLOVA, TATIANA · 2017 to 2021
$2.3M
NCI NIH HHS R01 CA154451NIBIB NIH HHS R01 EB023910
6 · The paper itself

Abstract

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated with tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and its optimization for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filter-singular value decomposition (SVD) and infinite impulse response (IIR) high-pass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high-pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

Indexed as

High-Intensity Focused Ultrasound AblationMicrobubblesAlgorithmsAnimalsImage Processing, Computer-AssistedPhantoms, ImagingSignal Processing, Computer-AssistedSwineUltrasonography, Doppler

Identifiers

PMID38598407
PMCPMC11141145

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.