Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings.

Recording neuronal activity using multiple electrodes has been widely used to understand the functional mechanisms of the brain. Increasing the number of electrodes allows us to decode more variety of functionalities. However, handling massive amounts of multichannel electrophysiological data is still challenging due to the limited hardware resources and unavoidable thermal tissue damage.

One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation.

One-shot federated learning (FL) has emerged as a promising solution in scenarios where multiple communication rounds are not practical. Notably, as feature distributions in medical data are less discriminative than those of natural images, robust global model training with FL is non-trivial and can lead to overfitting. To address this issue, we propose a novel one-shot FL framework leveraging Image Synthesis and Client model Adaptation (FedISCA) with knowledge distillation (KD).

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.

Motivation: Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures.

Results: Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields.

Deep 3D reconstruction of synchrotron X-ray computed tomography for intact lungs.

Synchrotron X-rays can be used to obtain highly detailed images of parts of the lung. However, micro-motion artifacts induced by such as cardiac motion impede quantitative visualization of the alveoli in the lungs. This paper proposes a method that applies a neural network for synchrotron X-ray Computed Tomography (CT) data to reconstruct the high-quality 3D structure of alveoli in intact mouse lungs at expiration, without needing ground-truth data.

Time-Dependent Deep Image Prior for Dynamic MRI.

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of the deep-image-prior approach, which optimizes the weights of a reconstruction network to fit a sequence of sparsely acquired dynamic MRI measurements.

Grid-Free Localization Algorithm Using Low-rank Hankel Matrix for Super-Resolution Microscopy.

Localization microscopy, such as STORM / PALM, can reconstruct super-resolution images with a nanometer resolution through the iterative localization of fluorescence molecules. Recent studies in this area have focused mainly on the localization of densely activated molecules to improve temporal resolutions. However, higher density imaging requires an advanced algorithm that can resolve closely spaced molecules.

Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal.

Recently, the annihilating filter-based low-rank Hankel matrix (ALOHA) approach was proposed as a powerful image inpainting method. Based on the observation that smoothness or textures within an image patch correspond to sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in an image domain to estimate any missing pixels. By extending this idea, we propose a novel impulse-noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix.

CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction.

We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements.

Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging.

For effective treatment of Alzheimer's disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. We aimed to develop an automatic image interpretation system based on a deep convolutional neural network (CNN) which can accurately predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). PET images of 139 patients with AD, 171 patients with MCI and 182 normal subjects obtained from Alzheimer's Disease Neuroimaging Initiative database were used.

Deep Convolutional Neural Network for Inverse Problems in Imaging.

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection.

Fast and robust segmentation of the striatum using deep convolutional neural networks.

Background: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN).

New Method: T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation.

MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based hankel matrix.

Purpose: Magnetic resonance imaging (MRI) artifacts are originated from various sources including instability of an magnetic resonance (MR) system, patient motion, inhomogeneities of gradient fields, and so on. Such MRI artifacts are usually considered as irreversible, so additional artifact-free scan or navigator scan is necessary. To overcome these limitations, this article proposes a novel compressed sensing-based approach for removal of various MRI artifacts.

Reference-free single-pass EPI Nyquist ghost correction using annihilating filter-based low rank Hankel matrix (ALOHA).

Purpose: MR measurements from an echo-planar imaging (EPI) sequence produce Nyquist ghost artifacts that originate from inconsistencies between odd and even echoes. Several reconstruction algorithms have been proposed to reduce such artifacts, but most of these methods require either additional reference scans or multipass EPI acquisition. This article proposes a novel and accurate single-pass EPI ghost artifact correction method that does not require any additional reference data.

Acceleration of MR parameter mapping using annihilating filter-based low rank hankel matrix (ALOHA).

Purpose: MR parameter mapping is one of clinically valuable MR imaging techniques. However, increased scan time makes it difficult for routine clinical use. This article aims at developing an accelerated MR parameter mapping technique using annihilating filter based low-rank Hankel matrix approach (ALOHA).

Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography.

In optical tomography, there exist certain spatial frequency components that cannot be measured due to the limited projection angles imposed by the numerical aperture of objective lenses. This limitation, often called as the missing cone problem, causes the under-estimation of refractive index (RI) values in tomograms and results in severe elongations of RI distributions along the optical axis. To address this missing cone problem, several iterative reconstruction algorithms have been introduced exploiting prior knowledge such as positivity in RI differences or edges of samples.

Annihilating Filter-Based Low-Rank Hankel Matrix Approach for Image Inpainting.

In this paper, we propose a patch-based image inpainting method using a low-rank Hankel structured matrix completion approach. The proposed method exploits the annihilation property between a shift-invariant filter and image data observed in many existing inpainting algorithms. In particular, by exploiting the commutative property of the convolution, the annihilation property results in a low-rank block Hankel structure data matrix, and the image inpainting problem becomes a low-rank structured matrix completion problem.

High-speed terahertz reflection three-dimensional imaging using beam steering.

High-speed terahertz (THz) reflection three-dimensional (3D) imaging is demonstrated using electronically-controlled optical sampling (ECOPS) and beam steering. ECOPS measurement is used for scanning an axial range of 7.8 mm in free space at 1 kHz scan rate while a transverse range of 100 × 100 mm(2) is scanned using beam steering instead of moving an imaging target.

High-speed terahertz reflection three-dimensional imaging for nondestructive evaluation.

We demonstrate high-speed terahertz (THz) reflection three-dimensional (3D) imaging based on electronically controlled optical sampling (ECOPS). ECOPS enables scanning of an axial range of 9 mm in free space at 1 kHz. It takes 80 s to scan a transverse range of 100 mm × 100 mm along a zigzag trajectory that consists of 200 lines using translation stages.

Terahertz substance imaging by waveform shaping.

Terahertz pulse shaping technique is used to adaptively design terahertz waveforms of enhanced spectral correlation to particular materials among a given set of materials. In a proof-of-principle experiment performed with a two-dimensional image target consisted of meta-materials of distinctive resonance frequencies, the as-designed waveforms are used to demonstrate terahertz substance imaging. It is hoped that this material-specific terahertz waveforms may enable single- or few-shot terahertz material classification when being used in conjunction with terahertz power measurement.

Enhancement of terahertz pulse emission by optical nanoantenna.

Bridging the gap between ultrashort pulsed optical waves and terahertz (THz) waves, the THz photoconductive antenna (PCA) is a major constituent for the emission or detection of THz waves by diverse optical and electrical methods. However, THz PCA still lacks employment of advanced breakthrough technologies for high-power THz emission. Here, we report the enhancement of THz emission power by incorporating optical nanoantennas with a THz photoconductive antenna.