Unsupervised denoising of photoacoustic images based on the Noise2Noise network.

In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available.

Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging.

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module.

Imaging-based intelligent spectrometer on a plasmonic rainbow chip.

Compact, lightweight, and on-chip spectrometers are required to develop portable and handheld sensing and analysis applications. However, the performance of these miniaturized systems is usually much lower than their benchtop laboratory counterparts due to oversimplified optical architectures. Here, we develop a compact plasmonic "rainbow" chip for rapid, accurate dual-functional spectroscopic sensing that can surpass conventional portable spectrometers under selected conditions.

Hairpin RF resonators for MR imaging transceiver arrays with high inter-channel isolation and B efficiency at ultrahigh field 7 T.

Electromagnetic decoupling among a close-fitting or high-density transceiver RF array elements is required to maintain the integrity of the magnetic flux density from individual channel for enhanced performance in detection sensitivity and parallel imaging. High-impedance RF coils have demonstrated to be a prominent design method to circumvent these coupling issues. Yet, inherent characteristics of these coils have ramification on the B field efficiency and SNR.

Minimizing Molecular Misidentification in Imaging Low-Abundance Protein Interactions Using Spectroscopic Single-Molecule Localization Microscopy.

Super-resolution microscopy can capture spatiotemporal organizations of protein interactions with resolution down to 10 nm; however, the analyses of more than two proteins involving low-abundance protein are challenging because spectral crosstalk and heterogeneities of individual fluorescent labels result in molecular misidentification. Here we developed a deep learning-based imaging analysis method for spectroscopic single-molecule localization microscopy to minimize molecular misidentification in three-color super-resolution imaging. We characterized the 3-fold reduction of molecular misidentification in the new imaging method using pure samples of different photoswitchable fluorophores and visualized three distinct subcellular proteins in U2-OS cell lines.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

Purpose: To develop an ultrafast and robust MR parameter mapping network using deep learning.

Theory And Methods: We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan.

Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging.

In this study, we present a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilize a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each image at a specific time point is assumed to belong to a low-dimensional manifold which is learned from training images created based on the parametric model.