ArticleIEEE transactions on medical imaging2020
Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting.IEEE transactions on ultrasonics · 2026Article
- Intravascular ultrasound wall shear stress imaging in stented coronary arteries with ultrafast Doppler.Scientific reports · 2026Article
- Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque.Ultrasonics · 2024Article
- A Coded Excitation Framework for High SNR Transcranial Ultrasound Imaging.IEEE transactions on medical imaging · 2023Article
- Combining Spatial Registration With Clutter Filtering for Power-Doppler Imaging in Peripheral Perfusion Applications.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2022Article
- Blood Flow Changes Associated with Spinal Cord Injury Assessed by Non-linear Doppler Contrast-Enhanced Ultrasound.Ultrasound in medicine & biology · 2022Article
- Spatiotemporal processing in photoplethysmography for skin microcirculatory perfusion imaging.Biomedical optics express · 2022Article
- Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor.
Indexed as
Identifiers
What OpenQuestion holds
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.