A novel method for removing Rician noise from MRI based on variational mode decomposition

Reverse magnetic resonance tuning nanoplatform with heightened sensitivity for non-invasively multiscale visualizing ferroptosis-based tumor sensitization therapy

Ferroptosis-based therapy has garnered considerable attention for its ability to kill drug-resistant cancer cells. Consequently, it holds great significance to assess the therapeutic outcomes by monitoring ferroptosis-related biomarkers, which enables the provision of real-time pathological insights into disease progression. Nevertheless, conventional imaging technology suffers from limitations including reduced sensitivity and difficulty in achieving real-time precise monitoring. Here, we report a tumor acidic-microenvironment-responsive nanoplatform with "Reverse Magnetic Resonance Tuning (ReMRT)" property and effective combined chemodynamic therapy (CDT) through the loading of chemotherapeutic drugs. This reverse MR mapping change is correlated with iron ion, reactive oxygen species (ROS) generation and drug release, etc., contributing to the precise monitoring of chemo-CDT effectiveness. Furthermore, the ReMRT nanoplatform presents as a highly efficacious combined chemo-CDT agent, and when this nanoplatform is used in conjunction with the "Area Reconstruction" method, it can afford a significant sensitivity (95.1-fold) in multiscale visualization of therapeutic, compared with the conventional MR R1/R2 values. The high-sensitive biological quantitative imaging provides a novel strategy for MR-guided multiscale dynamic tumor-related ferroptosis therapy.

Combining variational mode decomposition with regularisation techniques to denoise MRI data

In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.

A spatially variant high-order variational model for Rician noise removal

Rician noise removal is an important problem in magnetic resonance (MR) imaging. Among the existing approaches, the variational method is an essential mathematical technique for Rician noise reduction. The previous variational methods mainly employ the total variation (TV) regularizer, which is a first-order term. Although the TV regularizer is able to remove noise while preserving object edges, it suffers the staircase effect. Besides, the adaptability has received little research attention. To this end, we propose a spatially variant high-order variational model (SVHOVM) for Rician noise reduction. We introduce a spatially variant TV regularizer, which can adjust the smoothing strength for each pixel depending on its characteristics. Furthermore, SVHOVM utilizes the bounded Hessian (BH) regularizer to diminish the staircase effect generated by the TV term. We develop a split Bregman algorithm to solve the proposed minimization problem. Extensive experiments are performed to demonstrate the superiority of SVHOVM over some existing variational models for Rician noise removal.