Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch.

The presence of a diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidates for recanalization treatment. However, the visual assessment of DWI-FLAIR mismatch is subject to limitations due to variability among raters, which affects accuracy and consistency. To overcome these challenges, we aimed to develop and validate a deep learning-based classifier to categorize the mismatch.

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.

Deep Learning Diffuse Optical Tomography.

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies.

Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks.

Objective: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images.

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered.

Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson's Disease.

Objective: Parkinson's disease (PD) is a neurodegenerative disorder that mainly leads to the impairment of patients' motor function, as well as of cognition, as it progresses. This study tried to investigate the impact of PD on the resting state functional connectivity of the default mode network (DMN), as well as of the entire brain.

Methods: Sixty patients with PD were included and compared to 60 matched normal control (NC) subjects.

Topological persistence vineyard for dynamic functional brain connectivity during resting and gaming stages.

Background: Recent studies have shown the dynamic functional connectivity (FC) of the brain. Accordingly, new challenges have arisen for analyzing and interpreting this rich information.

New Method: We identified the patterns of coherent FC using a novel method in computational topology called the persistence vineyard.

Monitoring of soil bacterial community and some inoculated bacteria after prescribed fire in microcosm.

The soil bacterial community and some inoculated bacteria were monitored to assess the microbial responses to prescribed fire in their microcosm. An acridine orange direct count of the bacteria in the unburned control soil were maintained at a relatively stable level (2.0 approximately 2.

Monitoring of bacterial community in a coniferous forest soil after a wildfire.

Changes in the soil bacterial community of a coniferous forest were analyzed to assess microbial responses to wildfire. Soil samples were collected from three different depths in lightly and severely burned areas, as well as a nearby unburned control area. Direct bacterial counts ranged from 3.