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Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images

Edlund, Jonathan LU orcid ; Haris, Kostas LU ; Ostenfeld, Ellen LU orcid ; Carlsson, Marcus LU ; Heiberg, Einar LU ; Johansson, Sebastian LU ; Östenson, Björn LU orcid ; Jin, Ning ; Aletras, Anthony H. LU orcid and Steding-Ehrenborg, Katarina LU (2022) In Scientific Reports 12(1).
Abstract

Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum... (More)

Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum heart rate. The algorithm was validated for exercise by comparing LV mass during exercise to rest. Respiratory sorting to end expiration and end inspiration (processing time 20 to 40 min) succeeded in all research participants. Bias ± SD for LV mass was 0 ± 5 g when comparing real-time CMR at rest, and 0 ± 7 g when comparing real-time CMR during exercise to ECG-gated at rest. This study presents a semi-automatic algorithm to retrospectively perform respiratory sorting in free breathing real-time CMR. This can facilitate implementation of exercise CMR with non-ECG-gated free breathing real-time imaging, without any additional physiological input.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
12
issue
1
article number
5611
publisher
Nature Publishing Group
external identifiers
  • scopus:85127487743
  • pmid:35379859
ISSN
2045-2322
DOI
10.1038/s41598-022-09366-8
language
English
LU publication?
yes
id
e97b4e4b-bcd5-455c-9394-598c5363ca68
date added to LUP
2022-06-03 14:24:37
date last changed
2022-07-31 04:31:54
@article{e97b4e4b-bcd5-455c-9394-598c5363ca68,
  abstract     = {{<p>Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum heart rate. The algorithm was validated for exercise by comparing LV mass during exercise to rest. Respiratory sorting to end expiration and end inspiration (processing time 20 to 40 min) succeeded in all research participants. Bias ± SD for LV mass was 0 ± 5 g when comparing real-time CMR at rest, and 0 ± 7 g when comparing real-time CMR during exercise to ECG-gated at rest. This study presents a semi-automatic algorithm to retrospectively perform respiratory sorting in free breathing real-time CMR. This can facilitate implementation of exercise CMR with non-ECG-gated free breathing real-time imaging, without any additional physiological input.</p>}},
  author       = {{Edlund, Jonathan and Haris, Kostas and Ostenfeld, Ellen and Carlsson, Marcus and Heiberg, Einar and Johansson, Sebastian and Östenson, Björn and Jin, Ning and Aletras, Anthony H. and Steding-Ehrenborg, Katarina}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images}},
  url          = {{http://dx.doi.org/10.1038/s41598-022-09366-8}},
  doi          = {{10.1038/s41598-022-09366-8}},
  volume       = {{12}},
  year         = {{2022}},
}