PMID- 31284153
OWN - NLM
STAT- MEDLINE
DCOM- 20200908
LR  - 20200908
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 111
DP  - 2019 Aug
TI  - EVCMR: A tool for the quantitative evaluation and visualization of cardiac MRI 
      data.
PG  - 103334
LID - S0010-4825(19)30203-3 [pii]
LID - 10.1016/j.compbiomed.2019.103334 [doi]
AB  - Quantitative evaluation of diseased myocardium in cardiac magnetic resonance 
      imaging (MRI) plays an important role in the diagnosis and prognosis of 
      cardiovascular disease. The development of a user interface with state-of-the-art 
      techniques would be beneficial for the efficient post-processing and analysis of 
      cardiac images. The aim of this study was to develop a custom user interface tool 
      for the quantitative evaluation of the short-axis left ventricle (LV) and 
      myocardium. Modules for cine, perfusion, late gadolinium enhancement (LGE), and 
      T1 mapping data analyses were developed in Python, and a module for 
      three-dimensional (3D) visualization was implemented using PyQtGraph library. The 
      U-net segmentation and manual contour correction in the user interface were 
      effective in generating reference myocardial segmentation masks, which helped 
      obtain labeled data for deep learning model training. The proposed U-net 
      segmentation resulted in a mean Dice score of 0.87 (+/-0.02) in cine diastolic 
      myocardial segmentation. The LV mass measurement of the proposed method showed 
      good agreement with that of manual segmentation (intraclass correlation 
      coefficient = 0.97, mean difference and 95% Bland-Altman limits of 
      agreement = 4.4 +/- 12.2 g). C++ implementation of voxel-wise T1 mapping and its 
      binding via pybind11 led to a significant computational gain in calculating the 
      T1 maps. The 3D visualization enabled fast user interactions in rotating and 
      zooming-in/out of the 3D myocardium and scar transmurality. The custom tool has 
      the potential to provide a fast and comprehensive analysis of the LV and 
      myocardium from multi-parametric MRI data in clinical settings.
CI  - Copyright (c) 2019 Elsevier Ltd. All rights reserved.
FAU - Kim, Yoon-Chul
AU  - Kim YC
AD  - Clinical Research Institute, Samsung Medical Center, Sungkyunkwan University 
      School of Medicine, Seoul, South Korea.
FAU - Kim, Khu Rai
AU  - Kim KR
AD  - Department of Electronic Engineering, Sogang University, Seoul, South Korea.
FAU - Choi, Kwanghee
AU  - Choi K
AD  - Department of Computer Science and Engineering, Sogang University, Seoul, South 
      Korea.
FAU - Kim, Minwoo
AU  - Kim M
AD  - Department of Computer Science and Engineering, Sogang University, Seoul, South 
      Korea.
FAU - Chung, Younjoon
AU  - Chung Y
AD  - Department of Computer Science and Engineering, Sogang University, Seoul, South 
      Korea.
FAU - Choe, Yeon Hyeon
AU  - Choe YH
AD  - Department of Radiology and HVSI Imaging Center, Heart Vascular Stroke Institute, 
      Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South 
      Korea. Electronic address: yhchoe@skku.edu.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190619
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - Aged
MH  - Algorithms
MH  - Deep Learning
MH  - Female
MH  - Heart/*diagnostic imaging
MH  - Humans
MH  - Image Interpretation, Computer-Assisted/*methods
MH  - Magnetic Resonance Imaging/*methods
MH  - Male
MH  - Middle Aged
MH  - *Software
OTO - NOTNLM
OT  - Deep learning
OT  - Heart
OT  - Image segmentation
OT  - MRI
OT  - Python
OT  - Visualization
EDAT- 2019/07/10 06:00
MHDA- 2020/09/09 06:00
CRDT- 2019/07/09 06:00
PHST- 2019/03/15 00:00 [received]
PHST- 2019/05/24 00:00 [revised]
PHST- 2019/06/17 00:00 [accepted]
PHST- 2019/07/10 06:00 [pubmed]
PHST- 2020/09/09 06:00 [medline]
PHST- 2019/07/09 06:00 [entrez]
AID - S0010-4825(19)30203-3 [pii]
AID - 10.1016/j.compbiomed.2019.103334 [doi]
PST - ppublish
SO  - Comput Biol Med. 2019 Aug;111:103334. doi: 10.1016/j.compbiomed.2019.103334. Epub 
      2019 Jun 19.