Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging.

This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.

Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging.

This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3 T MRI.

Adaptive Graph Convolutional Networks for Medical Image Segmentation.

Medical image segmentation is very essential for computer-aided diagnosis in the field of medical imaging. In the last decade, Deep Learning-based frameworks (e.g.

Segmentation of liver and spleen based on computational anatomy models.

Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge.