ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2022
Ultrasound Lesion Detectability as a Distance Between Probability Measures.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Labeled Numerical Phantom of Abdominal Wall for Wave-Physics-Based Ultrasound Imaging: Applications to Image Reconstruction.IEEE transactions on ultrasonics · 2026Article
- Resolution Comparison of a Standoff Gel Pad Versus a Liquid Gel Barrier for Nasal Bone Fracture Sonography: A Standardized Crossover Study.Diagnostics (Basel, Switzerland) · 2025Article
- High-Resolution Power Doppler Using Null Subtraction Imaging.IEEE transactions on medical imaging · 2024Article
- Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- Methods for Enhancing the Robustness of the Generalized Contrast-to-Noise Ratio.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Lesion detectability (LD) quantifies how easily a lesion or target can be distinguished from the background. LD is commonly used to assess the performance of new ultrasound imaging methods. The contrast-to-noise ratio (CNR) is the most popular measure of LD; however, recent work has exposed its vulnerability to manipulations of dynamic range. The generalized CNR (gCNR) has been proposed as a robust histogram-based alternative that is invariant to such manipulations. Here, we identify key shortcomings of CNR and strengths of gCNR as LD metrics for modern beamformers. Using the measure theory, we pose LD as a distance between empirical probability measures (i.e., histograms) and prove that: 1) gCNR is equal to the total variation distance between probability measures and 2) gCNR is one minus the error rate of the ideal observer. We then explore several consequences of measure-theoretic LD in simulation studies. We find that histogram distances depend on bin selection that LD must be considered in the context of spatial resolution and that many histogram distances are invariant under measure-preserving isomorphisms of the sample space (e.g., dynamic range transformations). Finally, we provide a mathematical interpretation for why quantitative values such as contrast ratio (CR), CNR, and signal-to-noise ratio should not be compared between images with different dynamic ranges or underlying units and demonstrate how histogram matching can be used to reenable such quantitative comparisons.
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