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Journal of Nuclear Cardiology

, Volume 25, Issue 6, pp 1948–1957 | Cite as

Development and validation of an automatic method to detect the latest contracting viable left ventricular segments to assist guide CRT therapy from gated SPECT myocardial perfusion imaging

  • Weihua Zhou
  • Ningchao Tao
  • Xiaofeng Hou
  • Yao Wang
  • Russell D. Folks
  • David C. Cooke
  • Valeria M. Moncayo
  • Ernest V. Garcia
  • Jiangang Zou
Original Article

Abstract

Objectives

The purpose of this study is to use ECG-gated SPECT MPI to detect the latest contracting viable left ventricular (LV) segments to help guide the LV probe placement used in CRT therapy and to validate segment selection against the visual integration method by experts.

Methods

For each patient, the resting ECG-gated SPECT MPI short-axis images were sampled in 3D to generate a polar map of the perfusion distribution used to determine LV myocardial viability, and to measure LV synchronicity using our phase analysis tool. In the visual integration method, two experts visually interpreted the LV viability and mechanical dyssynchrony from the short-axis images and polar maps of viability and phase, to determine the latest contracting viable segments using the 17-segment model. In the automatic method, the apical segments, septal segments, and segments with more than 50% scar were excluded as these are not candidates for CRT LV probe placement. Amongst the remaining viable segments, the segments, whose phase angles were within 10° of the latest phase angle (the most delayed contracting segment), were identified for potential CRT LV probe placement and ranked based on the phase angles of the segments. Both methods were tested in 36 pre-CRT patients who underwent ECG-gated SPECT MPI. The accuracy was determined as the percent agreement between the visual integration and automatic methods. The automatic method was performed by a second independent operator to evaluate the inter-operator processing reproducibility.

Results

In all the 36 patients, the LV lead positions of the 1st choices recommended by the automatic and visual integration methods were in the same segments in 35 patients, which achieved an agreement rate of 97.2%. In the inter-operator reproducibility test, the LV lead positions of the 1st choices recommended by the two operators were in the same segments in 25 patients, and were in the adjacent segments in 7 patients, which achieved an overall agreement of 88.8%.

Conclusions

An automatic method has been developed to detect the latest contracting viable LV segments to help guide the LV probe placement used in CRT therapy. The retrospective clinical study with 36 patients suggests that this method has high agreement against the visual integration method by experts and good inter-operator reproducibility. Consequently, this method is promising to be a clinical tool to recommend the CRT LV lead positions.

Keywords

SPECT MPI hear failure CRT left ventricular lead 

Abbreviations

SPECT

Single-photon emission computed tomography

MPI

Myocardial perfusion imaging/images

LV

Left ventricle/ventricular

CRT

Cardiac resynchronization therapy

OSEM

Ordered subset expectation maximization

Notes

Acknowledgements

This research was supported by a grant from the American Heart Association (15POST22690035, PI: Weihua Zhou, Ph.D.) and a New Faculty startup grant from the University of Southern Mississippi (Project Number: DE01791). This research was also partly supported by a grant from National Natural Science Foundation of China (Project Number: 81470457, PI: Jiangang Zou, MD).

Disclosures

Dr. Garcia and Mr. Folks receive royalties from the sale of the Emory Cardiac Toolbox. The terms of this arrangement have been reviewed and approved by Emory University in accordance with its conflict-of-interest practice. All other authors have nothing to declare.

Supplementary material

12350_2017_853_MOESM1_ESM.pptx (609 kb)
Supplementary material 1 (PPTX 609 kb)

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Copyright information

© American Society of Nuclear Cardiology 2017

Authors and Affiliations

  • Weihua Zhou
    • 1
  • Ningchao Tao
    • 2
  • Xiaofeng Hou
    • 2
  • Yao Wang
    • 2
  • Russell D. Folks
    • 3
  • David C. Cooke
    • 3
  • Valeria M. Moncayo
    • 3
  • Ernest V. Garcia
    • 3
  • Jiangang Zou
    • 2
  1. 1.School of ComputingUniversity of Southern MississippiLong BeachUSA
  2. 2.Department of CardiologyNanjing Medical UniversityNanjingChina
  3. 3.Department of RadiologyEmory University School of MedicineAtlantaUSA

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