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  • Open Access

An automatic segmentation for improved visualization of atrial ablation lesions using magnetic resonance imaging

  • 1,
  • 2,
  • 1,
  • 2,
  • 2,
  • 1,
  • 3,
  • 1 and
  • 1
Journal of Cardiovascular Magnetic Resonance201113(Suppl 1):P251

https://doi.org/10.1186/1532-429X-13-S1-P251

Published: 2 February 2011

Keywords

  • Atrial Fibrillation
  • Leave Atrial
  • Paroxysmal Atrial Fibrillation
  • Manual Interaction
  • Atrial Wall

Background

Delayed-enhancement (DE) MRI is an effective technique for imaging left atrial (LA) ablation lesions following radio-frequency ablation for atrial fibrillation (AF). Existing techniques for segmentation/visualization of lesions require manual interaction of an expert user making them prone to high observer variability. Oakes et al.[1] applied thresholding on the intensity histogram of the manually-outlined atrial wall. Peters et al.[2] used maximum intensity projections (MIPs) to generate volume lesion visualizations for expert-user interpretation. Knowles et al.[3] employed MIP followed by user-interactive thresholding for lesion surface visualization.

Purpose

The aim of our work was to develop a fully-automated approach for LA lesion segmentation/visualization.

Methods

Five patients with paroxysmal AF (2 male, average age 55 years, LA size 4.2±0.5cm) underwent pulmonary vein (PV) isolation achieving successful electrical isolation of all PVs. Imaging was performed 6-months post-ablation on a Philips 1.5T Achieva with scans including respiratory-navigated and cardiac-gated whole-heart 3D-SSFP and inversion recovery-prepared MRI for visualization of Gd-DTPA DE (complete LA coverage, resolution of 1.3×1.3×2mm3).

The endocardial cavity of the LA was segmented from the whole heart SSFP-MRI scan. The segmented LA was registered to the DE-MRI scan using DICOM header data. The atrial wall was approximated by a ±3mm thick region from the endocardial surface. For each voxel in the wall, the probability of it being labelled as scar or healthy was derived from trained classifiers. For the healthy-tissue class, a mixture of Gaussian distributions was used to model the observed atrial wall tissue, with parameters computed using the Expectation-Maximization algorithm. For the scar-tissue class, the classifier was trained on prior segmentations. The DE-MRI images were segmented using the proposed algorithm and by an expert using the approach in [3].

Results

The algorithm segmented scars in less than 30 seconds on a 2.2GHz PC with no user interaction. The total surface area of scar was computed and represented as a percentage of the atrial surface area. Table 1 and Figure 1 show that there was good agreement between the results from the novel approach and the expert’s semi-automatic segmentation.
Table 1

The number of cardiac surface vertices classified into the healthy and scar tissue categories based on semi-automated and automatic segmentation. There was no statistical difference in the methods (p=0.36 paired t-test).

 

Automated algorithm (# surface vertices)

Expert-operated semi-automated (# surface vertices)

% scar using automated algorithm

% scar using expert-operated semi-automated

% difference

Patient

Healthy

Scar

Healthy

Scar

   

Pt. 1

4937

1167

5196

13.38

18.24

13.38

+4.86

Pt. 2

7953

260

7954

2.87

2.77

2.87

-0.10

Pt. 3

6555

629

6540

8.31

8.67

8.31

+0.36

Pt. 4

6354

1584

6220

25.08

23.00

25.08

-1.92

Pt. 5

4522

602

4500

12.00

15.50

12.00

-3.50

Figure 1
Figure 1

(left image) Surface visualization of 3 cases (patients 1, 3 and 5 from Table 1) with red regions indicating detected areas of scar. (right image) 3D voxel-wise segmentation of scar as seen on two selected slices from patient 1.

Conclusions

This study demonstrates a fully-automatic and rapid segmentation/visualization of post-ablation LA DE-MRI that will minimise the observer variability that is seen with existing approaches that require expert manual interactions.

Authors’ Affiliations

(1)
King's College London, London, UK
(2)
Guy's and St Thomas' NHS Foundation Trust, London, UK
(3)
Imperial College London, London, UK

References

  1. Oakes, et al: Circulation. 2009, 119 (13): 1758-67. 10.1161/CIRCULATIONAHA.108.811877.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Peters, et al: Radiology. 2007, 243 (3): 690-5. 10.1148/radiol.2433060417.View ArticlePubMedGoogle Scholar
  3. Knowles, et al: IEEE Trans.Biomed.Eng. 2010, 57 (6): 1467-75. 10.1109/TBME.2009.2038791.View ArticlePubMedGoogle Scholar

Copyright

© Karim et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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