Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics

TL;DR

This paper introduces a hypothesis-based particle counting algorithm for nanoparticle imaging assays that offers robust, interpretable, and training-free detection, improving accuracy in digital diagnostics for early disease detection.

Contribution

The paper presents a novel statistical particle counting method based on explicit image-formation models, eliminating the need for training data and enhancing interpretability in nanoparticle detection.

Findings

Demonstrates robust count accuracy across various imaging conditions

Reveals characteristic error modes at small particle separations

Validates utility with experimental SARS-CoV-2 biomarker detection images

Abstract

Digital assays represent a shift from traditional diagnostics and enable the precise detection of low-abundance analytes, critical for early disease diagnosis and personalized medicine, through discrete counting of biomolecular reporters. Within this paradigm, we present a particle counting algorithm for nanoparticle based imaging assays, formulated as a multiple-hypothesis statistical test under an explicit image-formation model and evaluated using a penalized likelihood rule. In contrast to thresholding or machine learning methods, this approach requires no training data or empirical parameter tuning, and its outputs remain interpretable through direct links to imaging physics and statistical decision theory. Through numerical simulations we demonstrate robust count accuracy across weak signals, variable backgrounds, magnification changes and moderate PSF mismatch. Particle resolvability tests further reveal characteristic error modes, including under-counting at very small separations and localized over-counting near the resolution limit. Practically, we also confirm the algorithm's utility, through application to experimental dark-field images comprising a nanoparticle-based assay for detection of DNA biomarkers derived from SARS-CoV-2. Statistically significant differences in particle count distributions are observed between control and positive samples. Full count statistics obtained further exhibit consistent over-dispersion, and provide insight into non-specific and target-induced particle aggregation. These results establish our method as a reliable framework for nanoparticle-based detection assays in digital molecular diagnostics.