A fast co-registration scheme between camera and MRI for MRI-guided surgery.

The co-registration between an optical tracker and Magnetic Resonance Imaging (MRI) space is an indispensable step for MRI-guided surgery. In this study, with a focus on RGB cameras as the tracker, we introduce an innovative co-registration scheme for tracker-to-MRI integration. Firstly, we design a cube-shaped registration model that is equipped with an ArUco marker on its exterior for RGB camera detection and houses four water blobs inside for MRI calibration.

Automated brain atrophy quantification and evaluation using spatial resolution enhancement.

Brain atrophy is one of the most common features of neurodegenerative diseases and is particularly critical in the early diagnosis of conditions like Alzheimer's and multiple sclerosis. Automated segmentation and quantification are highly desirable in brain atrophy evaluation but existing methods require high-quality MRI scans with isotropic resolution. However in practice, clinicians usually choose to reduce the number of slices to save time, and because of their anisotropic resolution, the morphometric analysis cannot be implemented.

A Modified Reference Scan Method for MR Image Inhomogeneity Correction.

Intensity inhomogeneity remains a pivotal challenge that hampers the diagnostic efficacy of Magnetic Resonance Imaging (MRI). Traditional reference scan methods, while effective in correcting intensity inhomogeneity, often inadvertently introduce noise into the images, thus degrading the Signal-to-Noise Ratio (SNR). In this study, we introduce an innovative modified reference scan methodology.

CSA: A Channel-Separated Attention Module for Enhancing MRI Reconstruction.

Channel attention mechanisms have been proven to effectively enhance network performance in various visual tasks, including the Magnetic Resonance Imaging (MRI) reconstruction task. Channel attention mechanisms typically involve channel dimensionality reduction and cross-channel interaction operations to achieve complexity reduction and generate more effective weights of channels. However, the operations may negatively impact MRI reconstruction performance since it was found that there is no discernible correlation between adjacent channels and the low information value in some feature maps.

Linear fine-tuning: a linear transformation based transfer strategy for deep MRI reconstruction.

Introduction: Fine-tuning (FT) is a generally adopted transfer learning method for deep learning-based magnetic resonance imaging (MRI) reconstruction. In this approach, the reconstruction model is initialized with pre-trained weights derived from a source domain with ample data and subsequently updated with limited data from the target domain. However, the direct full-weight update strategy can pose the risk of "catastrophic forgetting" and overfitting, hindering its effectiveness.

Preparation, characterization, and Cd(II) sorption of/on cysteine-montmorillonite composites synthesized at various pH.

Montmorillonite-cysteine could be used as the immobilizer, detector, and detoxifier of heavy metals. To further the understanding and the application, the interaction between the montmorillonite and cysteine and the adsorption of cysteine on montmorillonite and characterization of the composites need to be studied further. In present work, the effects of pH, contact time and initial concentration of cysteine on the adsorption, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Cd(II) adsorption on the composites were conducted to characterize the composites synthesized at different pH conditions.

Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging.

Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed.