ArticleUltrasound in medicine & biology2019
Validation of Normalized Singular Spectrum Area as a Classifier for Molecularly Targeted Microbubble Adherence.
Article in Ultrasound in medicine & biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Improved Nondestructive Ultrasound Molecular Imaging With Lightweight Convolutional Neural Network.IEEE transactions on medical imaging · 2026Article
- Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles.IEEE transactions on ultrasonics · 2025Article
- Quantifying Molecular Changes in the Preeclamptic Rat Placenta with Targeted Contrast-Enhanced Ultrasound Imaging.Molecular imaging and biology · 2025Article
- Theranostic Microbubbles with Homogeneous Ligand Distribution for Higher Binding Efficacy.Pharmaceutics · 2022Article
- Acoustic Molecular Imaging Beyond the Diffraction Limit In Vivo.IEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2022Article
- Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques.Ultrasound in medicine & biology · 2021Article
- Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.IEEE transactions on medical imaging · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Ultrasound molecular imaging is a diagnostic technique wherein molecularly targeted microbubble contrast agents are imaged to reveal disease markers on the blood vessel endothelium. Currently, microbubble adhesion to affected tissue can be quantified using differential targeted enhancement (dTE), which measures the late enhancement of adherent microbubbles through administration of destructive ultrasound pressures. In this study, we investigated a statistical parameter called the normalized singular spectrum area (NSSA) as a means to detect microbubble adhesion without microbubble destruction. We compared the signal differentiation capability of NSSA with matched dTE measurements in a mouse hindlimb tumor model. Results indicated that NSSA-based signal classification performance matches dTE when differentiating adherent microbubble from non-adherent microbubble signals (receiver operating characteristic area under the curve = 0.95), and improves classification performance when differentiating microbubble from tissue signals (p < 0.005). NSSA-based signal classification eliminates the need for destruction of contrast, and may offer better sensitivity, specificity and the opportunity for real-time microbubble detection and classification.
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Registered trials
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.