1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A clustering-based approach for prediction of cardiac resynchronization 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

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Introduction Methods Image Acquisition 、 Surface Tracking 、 Similarity Measure 、 Hierarchical Clustering 、 Cross Correlation 、 Pacing Sites Filtering Results Conclusions Personal Opinion

3. 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 quantitative prediction. That runs the risk of suboptimal benefits.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  achieve maximized CRT benefit. ─ Estimate the most effective places for implanting the pacemaker ( 節律器 ).

5. 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.  {Phase 1 Slice 6 Radius 8, Phase 2 Slice 6 Radius 8,…, Phase 20 Slice 6 Radius 8 } represents a sample radial motion series for the 8-th radius in Slice6.

6. 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 pacing site candidates and main cluster, we filter out the site candidates without contraction delay. sweep line

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Results  We implement and test our approaches on 20 patients‘ cardiac MRI data.  {93,94} => candidate site  {82,83} => candidate site  filter out {1,2,3}  move sweep-line from top to bottom till extracting two clusters sweep line

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Results (cont.) abnormal heart normal heart

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Results (cont.)

10. 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 our approach can correctly distinguish the failing hearts from normal hearts.

11. 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