MRI retrospective respiratory gating and cardiac sensing by CW Doppler radar: A feasibility study.

Objective: This study investigates the feasibility of non-contact retrospective respiratory gating and cardiac sensing using continuous wave Doppler radar deployed in an MRI system. The proposed technique can complement existing sensors which are difficult to apply for certain patient populations.

Methods: We leverage a software-defined radio for continuous wave radar at 2.

Coil sketching for computationally efficient MR iterative reconstruction.

Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three-dimensional (3D) non-Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non-Cartesian imaging.

Deep Learning-Based Reconstruction for Cardiac MRI: A Review.

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling rates. These fast acquisitions have the potential to considerably impact the diagnosis and treatment of cardiovascular disease.

Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation.

Purpose: To develop and validate a deep learning-based reconstruction framework for highly accelerated two-dimensional (2D) phase contrast (PC-MRI) data with accurate and precise quantitative measurements.

Methods: We propose a modified DL-ESPIRiT reconstruction framework for 2D PC-MRI, comprised of an unrolled neural network architecture with a Complex Difference estimation (CD-DL). CD-DL was trained on 155 fully sampled 2D PC-MRI pediatric clinical datasets.

Automated segmentation and classification of cell nuclei in immunohistochemical breast cancer images with estrogen receptor marker.

Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups: those that express the ER marker and those that do not.