Publications by Jonathan Yuelin Dong

Point-spread-function engineering in MINFLUX: optimality of donut and half-moon excitation patterns

Localization microscopy enables imaging with resolutions that surpass the conventional optical diffraction limit. Notably, the Maximally INFormative LUminescence eXcitation (MINFLUX) method achieves super-resolution by shaping the excitation point spread f ...

2025

The Hôtel-Dieu MRI Classification of Uterosacral Ligament Involvement in Endometriosis: A Pictorial Guide to Clinical Use

Jonathan Yuelin Dong

Objectives: Endometriosis is a common gynecologic condition characterized by the presence of endometrial-like tissue outside the uterus, often leading to pelvic pain and infertility. Diagnosis is frequently delayed, with prolonged diagnostic wandering that ...

MDPI AG2025

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

Jonathan Yuelin Dong

Reservoir Computing (RC) has become popular in recent years thanks to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive power ...

2025

A Silicon-Pixel Paradigm for PET

Michaël Unser, Pol del Aguila Pla, Jonathan Yuelin Dong

Positron emission tomography (PET) scanners use scintillation crystals to stop high-energy photons. The ensuing lower-energy photons are then detected via photomultipliers. We study the performance of a stack of monolithic siliconpixel detectors as an alte ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Model-based temporal unmixing towards quantitative photo-switching optoacoustic tomography

Michaël Unser, Jonathan Yuelin Dong

Optoacoustic (OA) imaging combined with reversibly photoswitchable proteins has emerged as a promising technology for the high-sensitivity and multiplexed imaging of cells in live tissues in preclinical research. Through carefully designed illumination sch ...

2025

Structured Random Model for Fast and Robust Phase Retrieval

Michaël Unser, Jonathan Yuelin Dong

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computational ...

Institute of Electrical and Electronics Engineers Inc.2025

Single-pixel transmission matrix recovery via two-photon fluorescence

Jonathan Yuelin Dong

Imaging at depth in opaque materials has long been a challenge. Recently, wavefront shaping has enabled notable advance for deep imaging. Nevertheless, most noninvasive wavefront-shaping methods require cameras, lack the sensitivity for deep imaging under ...

Amer Assoc Advancement Science2024

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Michaël Unser, Thanh-An Michel Pham, Jonathan Yuelin Dong

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniqu ...

2023

From information theory to novel imaging techniques

Jonathan Yuelin Dong

In imaging, we retrieve information about a specimen by encoding it into a probe beam. We quantitatively analyze the Fisher information provided by phase and scattering microscopy techniques and discuss implications for microscope design. ...

Optical Society of America2023

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Michaël Unser, Thanh-An Michel Pham, Pakshal Narendra Bohra, Jonathan Yuelin Dong

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

2022

Physics-based neural network for non-invasive control of coherent light in scattering media

Antoine Vincent Boniface, Jonathan Yuelin Dong

Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the prop ...

2022

Asymptotic Stability in Reservoir Computing

Michaël Unser, Jonathan Yuelin Dong

Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random. Stability relates to the sensitivity of the network state to perturbations. It is an important property in Reservoir Computing as it directly impacts perform ...

IEEE2022

Artifacts in optical projection tomography due to refractive-index mismatch: model and correction

Michaël Unser, Jonathan Yuelin Dong

Optical projection tomography (OPT) is a powerful tool for three-dimensional (3D) imaging of mesoscopic samples. While it is able to achieve resolution of a few tens of microns over a sample volume of several cubic centimeters, the reconstructed images oft ...

2022

Mechanical artifacts in optical projection tomography: classification and automatic calibration

Michaël Unser, Thanh-An Michel Pham, François Thierry M Marelli, Jonathan Yuelin Dong

Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts d ...

Optica Publishing Group2022

Fundamental bounds on the precision of iSCAT, COBRI and dark-field microscopy for 3D localization and mass photometry

Jonathan Yuelin Dong

Interferometric imaging is an emerging technique for particle tracking and mass photometry. Mass or position are estimated from weak signals, coherently scattered from nanoparticles or single molecules, and interfered with a co-propagating reference. In th ...

2021

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Florent Gérard Krzakala, Jonathan Yuelin Dong, Ruben Elie Ohana

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...

Curran Associates, Inc.2020