Unsupervised cycle-consistent network using restricted subspace field map for removing susceptibility artifacts in EPI.

Purpose: To design an unsupervised deep neural model for correcting susceptibility artifacts in single-shot Echo Planar Imaging (EPI) and evaluate the model for preclinical and clinical applications.

Methods: This work proposes an unsupervised cycle-consistent model based on the restricted subspace field map to take advantage of both the deep learning (DL) and the reverse polarity-gradient (RPG) method for single-shot EPI. The proposed model consists of three main components: (1) DLRPG neural network (DLRPG-net) to obtain field maps based on a pair of images acquired with reversed phase encoding; (2) spin physical model-based modules to obtain the corrected undistorted images based on the learned field map; and (3) cycle-consistency loss between the input images and back-calculated images from each cycle is explored for network training.

Mutually communicated model based on multi-parametric MRI for automated segmentation and classification of prostate cancer.

Background: Multiparametric magnetic resonance imaging (mp-MRI) is introduced and established as a noninvasive alternative for prostate cancer (PCa) detection and characterization.

Purpose: To develop and evaluate a mutually communicated deep learning segmentation and classification network (MC-DSCN) based on mp-MRI for prostate segmentation and PCa diagnosis.

Methods: The proposed MC-DSCN can transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way.

Frequency Modulation Approach for High Power Density 100 Hz Piezoelectric Vibration Energy Harvester.

Piezoelectric vibration energy harvester (PVEH) is a promising device for sustainable power supply of wireless sensor nodes (WSNs). PVEH is resonant and generates power under constant frequency vibration excitation of mechanical equipment. However, it cannot output high power through off-resonance if it has frequency offset in manufacturing, assembly and use.

Risk, Prognostic Factors and Nomograms for Bone Metastasis in Young Females with Breast Cancer: A Large Cohort Retrospective Study.

Purpose: To determine the incidence of bone metastasis (BM) in young female patients with breast cancer (BC) and develop 2 robust nomograms for BM in young female patients with BC.

Methods: We searched and downloaded the data from young (age ≤40 years) female patients with bone cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Univariate and multivariate analyses were performed to screen the potential diagnostic variables and prognostic factors for BM.

Retrospective motion correction for preclinical/clinical magnetic resonance imaging based on a conditional generative adversarial network with entropy loss.

Multishot scan magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving the image quality in MRI. This work proposes and validates a new end-to-end motion-correction method for the multishot sequence that incorporates a conditional generative adversarial network with minimum entropy (cGANME) of MR images. The cGANME contains an encoder-decoder generator to obtain motion-corrected images and a PatchGAN discriminator to classify the image as either real (motion-free) or fake (motion-corrected).

Risk factors, prognostic factors, and nomograms for bone metastasis in patients with newly diagnosed infiltrating duct carcinoma of the breast: a population-based study.

Background: Breast cancer is the most common malignancy in women, and it is also the leading cause of death in female patients; the most common pathological type of BC is infiltrating duct carcinoma (IDC). Some nomograms have been developed to predict bone metastasis (BM) in patients with breast cancer. However, there are no studies on diagnostic and prognostic nomograms for BM in newly diagnosed IDC patients.

Robust water-fat separation based on deep learning model exploring multi-echo nature of mGRE.

Purpose: To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions.

Methods: A new multi-echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water-fat separation module that processes the feature information extracted from the feature extraction module.

Percutaneous pedicle screw fixation combined with transforaminal endoscopic spinal canal decompression for the treatment of thoracolumbar burst fracture with severe neurologic deficit: A case report.

Rationale: The most common fractures of the spine are associated with the thoracolumbar junction (T10-L2). And burst fractures make up 15% of all traumatic thoracolumbar fractures, which are often accompanied by neurological deficits and require open surgeries. Common surgeries include either anterior, posterior or a combination of these approaches.

A new automatic baseline correction method based on iterative method.

A new automatic baseline correction method for Nuclear Magnetic Resonance (NMR) spectra is presented. It is based on an improved baseline recognition method and a new iterative baseline modeling method. The presented baseline recognition method takes advantages of three baseline recognition algorithms in order to recognize all signals in spectra.