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  • Oral presentation
  • Open Access

Temporal interpolation of real-time cine images for ventricular function assessment

  • 1,
  • 1,
  • 1,
  • 2,
  • 3 and
  • 2
Journal of Cardiovascular Magnetic Resonance201416 (Suppl 1) :O42

https://doi.org/10.1186/1532-429X-16-S1-O42

  • Published:

Keywords

  • Siemens Healthcare
  • Cardiac Phasis
  • Data Acquisition Strategy
  • Interpolation Module
  • Reconstruction Pipeline

Background

In uncooperative patients or in presence of arrhythmia, real-time free-breathing cine MR approaches may offer an alternative to well-established segmented data acquisition strategies [1, 2]. Segmented acquisition strategies collect data throughout a number of heartbeats and typically a fixed number of cardiac phases are reconstructed for each slice representing an average RR interval. In real-time cine imaging, the number of reconstructed phases may be different for each slice due to heart rate variations. However, it is desirable to reconstruct a predefined number of cardiac phases per slice to facilitate functional analysis and processing. In this work, we present an image reconstruction approach that retrospectively interpolates real-time cine images to calculate a predefined number of cardiac phases per heartbeat and slice. Ejection fraction is compared between segmented cine images and interpolated real-time cine images in healthy volunteers.

Methods

Segmented breath-hold and real-time free-breathing cine images were acquired in four healthy volunteers on a clinical 3T MR scanner (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Acquisition parameters for each volunteer are represented in Table 1. FOVs for real-time studies were kept as small as possible to improve spatial resolution. T-PAT factor 3 was used to improve temporal resolution. The linear interpolation module was fully integrated into the reconstruction pipeline of the scanner. During imaging, heart rate was continuously monitored and real-time images were linearly interpolated to a fixed number of cardiac phases for each heartbeat and for each slice (Figure 1). 20 cardiac phases were calculated per heartbeat by interpolation during real-time acquisitions to achieve 50 ms nominal temporal resolution. Subsequently, interpolated real-time images were analyzed to evaluate LV function (syngo Argus 4D ventricular function, Siemens Healthcare, Erlangen, Germany) and compared with results based on segmented images.

Table 1

Data acquisition parameters for healthy volunteers

 

Segmented

Real-Time (RT)

RT Interpolated

 

Temporal

Resolution (ms)

39 to 41

78 to 114

42 to 55

 

Spatial Resolution (mm)

1.5 × 1.5 × 6

2.8 × 2.3 × 6

2.8 × 2.3 × 6

 

Comparison of ejection fraction values between segmented and interpolated real-time data sets (Seg: segmented, RT: real-time).

 

Volunteer 1

Seg./RT

Volunteer 2

Seg./RT

Volunteer 3

Seg./RT

Volunteer 4

Seg./RT

EF (%)

57/57

65/64

65/64

68/65

Figure 1
Figure 1

I. Interpolation of RT cine images. Real-time acquired images during a heartbeat are interpolated to a fixed number of frames. The linear interpolation algorithm uses frames before and after the QRS peak to estimate first and last phase. II. End-systolic (upper images) and end-diastolic (bottom images) cardiac phases for a) segmented, b) non-interpolated real-time, and c) interpolated real-time images. Please note that interpolation introduces a slight blur to the images.

Results

Interpolated real-time images were loaded to Argus and endo-cardial borders were detected prior to analysis. EF measurements (see Table 2) from interpolated real-time and segmented images were found to be in good agreement. End-systolic and end-diastolic phases for segmented, non-interpolated real-time, and interpolated real-time images are given in Figure 2. Interpolation increased the SNR, but introduced slight blurriness. The level of blurring was low and thus it did not affect the functional evaluation.

Conclusions

In this feasibility study we presented initial evidence that interpolated real-time cine images may be used to facilitate cardiac functional analysis using well-established post-processing software. The current implemented algorithm will be extended to take arrhythmia events into account.

Funding

The author is a full-time employee of Siemens Healthcare USA, Inc.

Authors’ Affiliations

(1)
Siemens Healthcare USA, Inc., Chicago, Illinois, USA
(2)
Siemens AG Healthcare Sector, Erlangen, Germany
(3)
National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA

References

  1. Carr JC, et al: Radiology. 2001, 219: 828-834. 10.1148/radiology.219.3.r01jn44828.View ArticlePubMedGoogle Scholar
  2. Hori Y, et al: J Card Magn Reson. 2003, 5: 439-450. 10.1081/JCMR-120022260.View ArticleGoogle Scholar

Copyright

© Saybasili et al.; licensee BioMed Central Ltd. 2014

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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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