Intelligent Database Systems Lab
國立雲林科技大學
National Yunlin University of Science and Technology
1
A clustering-based approach forprediction of cardiacresynchronization therapy (CRT;心臟再同步治療)
Advisor
：
Dr. Hsu
Reporter
：
Wen-Hsiang Hu
Author
：
Heng Huang et al.
2005 ACM Symposium on Applied Computing
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
2
Outline
Motivation
Objective
Introduction
Methods
Image Acquisition、Surface Tracking 、SimilarityMeasure 、Hierarchical Clustering 、Cross Correlation 、Pacing Sites Filtering
Results
Conclusions
Personal Opinion
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
3
Motivation
In a traditional CRT device deployment, pacing sites(心律部位) are selected without quantitativeprediction. That runs the risk of suboptimal benefits.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
4
Objective
achieve maximized CRT benefit.
─Estimate the most effective places for implanting thepacemaker (節律器).
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
5
Methods
MRI (核磁共振影像) is used to capture 3D images of a heart
Each MRI sequence holds 17 to 20 temporal phases per heartbeat.
{Phase1Slice6Radius8, Phase2Slice6Radius8,…, Phase20Slice6Radius8}represents a sample radial motion series for the 8-th radius in Slice6.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
6
Methods
Hierarchical Clustering: bottom-up clustering &average-link approach
Cross Correlation : obtain timing delay value
Pacing Sites Filtering: Using the contraction timing delay between pacingsite candidates and main cluster, we filter out the site candidates withoutcontraction delay.
sweep line
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
7
Results
We implement and test our approaches on 20 patients‘ cardiacMRI data.
{93,94} =>candidate site
{82,83} =>candidate site
filter out {1,2,3}
move sweep-linefrom top to bottom tillextracting two clusters
sweep line
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
8
Results (cont.)
abnormal heart
normal heart
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
9
Results (cont.)
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
10
CONCLUSIONS
In this paper
─identify the prior optimal left ventricle pacing sites
─help the diagnosis of heart failure in clinic.
─Blinded analysis of clinical MRI data also show that ourapproach can correctly distinguish the failing hearts fromnormal hearts.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
11
Personal Opinion
Disadvantage
─unclear content
Application
─the prediction of bi-ventricular pacing sites