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Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease.

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Title: Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking—a cardiovascular MR study in health and disease.
Authors: Gröschel, Jan1,2,3 (AUTHOR) jan.groeschel@charite.de, Kuhnt, Johanna1,2,3 (AUTHOR), Viezzer, Darian1,2,3 (AUTHOR), Hadler, Thomas1,2,3 (AUTHOR), Hormes, Sophie1,2 (AUTHOR), Barckow, Phillip4 (AUTHOR), Schulz-Menger, Jeanette1,2,3 (AUTHOR), Blaszczyk, Edyta1,2,3 (AUTHOR) edyta.blaszczyk@charite.de
Source: European Radiology. Feb2024, Vol. 34 Issue 2, p1003-1015. 13p.
Subjects: Artificial intelligence, Left ventricular hypertrophy, Papillary muscles, Tracking algorithms, Magnetic resonance
Abstract: Objectives: The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (AI)–based contours with manually derived strain values in healthy volunteers and patients with cardiac pathologies. Materials and methods: A cohort of 136 subjects (60 healthy volunteers and 76 patients; of those including 46 cases with left ventricular hypertrophy (LVH) of varying etiology and 30 cases with chronic myocardial infarction) was analyzed. Comparisons were based on quantitative strain analysis and on a geometric level by the Dice similarity coefficient (DSC) of the segmentations. Strain quantification was performed in 3 long-axis slices and short-axis (SAX) stack with epi- and endocardial contours in end-diastole. AI contours were checked for plausibility and potential errors in the tracking algorithm. Results: AI-derived strain values overestimated radial strain (+ 1.8 ± 1.7% (mean difference ± standard deviation); p = 0.03) and underestimated circumferential (− 0.8 ± 0.8%; p = 0.02) and longitudinal strain (− 0.1 ± 0.8%; p = 0.54). Pairwise group comparisons revealed no significant differences for global strain. The DSC showed good agreement for healthy volunteers (85.3 ± 10.3% for SAX) and patients (80.8 ± 9.6% for SAX). In 27 cases (27/76; 35.5%), a tracking error was found, predominantly (24/27; 88.9%) in the LVH group and 22 of those (22/27; 81.5%) at the insertion of the papillary muscle in lateral segments. Conclusions: Strain analysis based on AI-segmented images shows good results in healthy volunteers and in most of the patient groups. Hypertrophied ventricles remain a challenge for contouring and feature tracking. Clinical relevance statement: AI-based segmentations can help to streamline and standardize strain analysis by feature tracking. Key Points: • Assessment of strain in cardiovascular magnetic resonance by feature tracking can generate global and segmental strain values. • Commercially available artificial intelligence algorithms provide segmentation for strain analysis comparable to manual segmentation. • Hypertrophied ventricles are challenging in regards of strain analysis by feature tracking. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Feb2024, Vol. 34 Issue 2, p1003-1015. 13p.
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– Name: Abstract
  Label: Abstract
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  Data: Objectives: The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (AI)–based contours with manually derived strain values in healthy volunteers and patients with cardiac pathologies. Materials and methods: A cohort of 136 subjects (60 healthy volunteers and 76 patients; of those including 46 cases with left ventricular hypertrophy (LVH) of varying etiology and 30 cases with chronic myocardial infarction) was analyzed. Comparisons were based on quantitative strain analysis and on a geometric level by the Dice similarity coefficient (DSC) of the segmentations. Strain quantification was performed in 3 long-axis slices and short-axis (SAX) stack with epi- and endocardial contours in end-diastole. AI contours were checked for plausibility and potential errors in the tracking algorithm. Results: AI-derived strain values overestimated radial strain (+ 1.8 ± 1.7% (mean difference ± standard deviation); p = 0.03) and underestimated circumferential (− 0.8 ± 0.8%; p = 0.02) and longitudinal strain (− 0.1 ± 0.8%; p = 0.54). Pairwise group comparisons revealed no significant differences for global strain. The DSC showed good agreement for healthy volunteers (85.3 ± 10.3% for SAX) and patients (80.8 ± 9.6% for SAX). In 27 cases (27/76; 35.5%), a tracking error was found, predominantly (24/27; 88.9%) in the LVH group and 22 of those (22/27; 81.5%) at the insertion of the papillary muscle in lateral segments. Conclusions: Strain analysis based on AI-segmented images shows good results in healthy volunteers and in most of the patient groups. Hypertrophied ventricles remain a challenge for contouring and feature tracking. Clinical relevance statement: AI-based segmentations can help to streamline and standardize strain analysis by feature tracking. Key Points: • Assessment of strain in cardiovascular magnetic resonance by feature tracking can generate global and segmental strain values. • Commercially available artificial intelligence algorithms provide segmentation for strain analysis comparable to manual segmentation. • Hypertrophied ventricles are challenging in regards of strain analysis by feature tracking. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Papillary muscles
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      – SubjectFull: Tracking algorithms
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              Text: Feb2024
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