Background Although the breath-hold cine balanced steady state free precession (bSSFP)
imaging is well established for assessment of biventricular volumes and function, shorter …

Background Cardiac MRI feature‐tracking (FT) with breath‐holding (BH) cine balanced
steady state free precession (bSSFP) imaging is well established. It is unclear whether FT …

Objective: To investigate the feasibility of 3D left ventricular global and regional strain by
using one breath-hold (BH) compressed sensing cine (CSC) protocol and determine the …

Background Breath‐holding (BH) for cine balanced steady state free precession (bSSFP)
imaging is challenging for patients with impaired BH capacity. Deep learning‐based …

Purpose: To overcome the difficulty in building a large data set with a high-quality in medical
imaging, a concept of'blended-transfer learning'(BTL) using a combination of both source …

We propose the “Tile-net” method based on dividing an image into smaller tiles. Using the
tile as the input to the neural network, the network is simplified substantially. The Tile-net …

Compressed-sensing cardiovascular CINE MRI was performed using deep artificial neural
network and transfer learning. Transfer learning is a method to use weights obtained from …

Deep artificial neural network with transfer learning is applied to compressed sensing
cardiovascular MRI. Transfer learning is a method that utilizes structure, filter kernels, and …

We build a deep neural network in time-phase encoding plane (ty) for compressed sensing
cardiovascular CINE MRI. Previously neural networks were developed in cross-sectional …

전이학습을 수행한 심층 인공신경망을 압축센싱 심혈관 자기공명영상에 적용하였다.
전이학습은 선행학습 신경망의 구조나 필터 커널, 가중치를 현재의 학습이나 응용에 활용하는 …