Nearly automated motion artifacts correction between multi breath-hold short-axis and long-axis cine CMR images

Carminati, Chiara; Maffessanti, Francesco; Caiani, Enrico G.

doi:10.1016/j.compbiomed.2013.12.013

Cited by 16 publications

(17 citation statements)

References 33 publications

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“…First, the gap between slices (vertical dimension) in most routine acquisitions is relatively large (around 7-8 mm) and the pixel intensities between the slices cannot be reliably estimated (Petitjean and Dacher, 2011;Tavakoli and Amini, 2013;Petitjean et al, 2015;Queiros et al, 2014). Second, due to motion artifacts in cardiac MRI, misalignment between slices is common (Barajas et al, 2006;Chandler et al, 2008;Lötjönen et al, 2004;Zakkaroff et al;Carminati et al, 2014;Liew et al, 2015). This means that the cavity center is not at the same position in different slices.…”

Section: Discussionmentioning

confidence: 99%

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Avendi

Kheradvar

Jafarkhani

2016

Medical Image Analysis

525

286

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Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2% − 95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.

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Section: Discussionmentioning

confidence: 99%

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Avendi

Kheradvar

Jafarkhani

2016

Medical Image Analysis

525

286

View full text Add to dashboard Cite

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“…Previous registration‐based methods for segmentation correction in cine‐bSSFP images have often used LAX views as the reference . Using this approach in our application would have improved the cine‐bSSFP images, but would not have solved misalignment between the cine‐bSSFP and 4D flow MRI data.…”

Section: Discussionmentioning

confidence: 99%

Improving left ventricular segmentation in four‐dimensional flow MRI using intramodality image registration for cardiac blood flow analysis

Gupta

Bustamante

Fredriksson

et al. 2017

Magnetic Resonance in Med

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Purpose: Assessment of blood flow in the left ventricle using four-dimensional flow MRI requires accurate left ventricle segmentation that is often hampered by the low contrast between blood and the myocardium. The purpose of this work is to improve left-ventricular segmentation in four-dimensional flow MRI for reliable blood flow analysis. Method: The left ventricle segmentations are first obtained using morphological cine-MRI with better in-plane resolution and contrast, and then aligned to four-dimensional flow MRI data. This alignment is, however, not trivial due to inter-slice misalignment errors caused by patient motion and respiratory drift during breath-hold based cine-MRI acquisition. A robust image registration based framework is proposed to mitigate such errors automatically. Data from 20 subjects, including healthy volunteers and patients, was used to evaluate its geometric accuracy and impact on blood flow analysis. Results: High spatial correspondence was observed between manually and automatically aligned segmentations, and the improvements in alignment compared to uncorrected segmentations were significant (P < 0.01). Blood flow analysis from manual and automatically corrected segmentations did not differ significantly (P > 0.05).Conclusion: Our results demonstrate the efficacy of the proposed approach in improving left-ventricular segmentation in four-dimensional flow MRI, and its potential for reliable blood flow analysis. Magn Reson Med 79:554-560,

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“…Since the proposed SSM approach works on the whole 3D stack, in-plane correction for spatial misalignment was performed [5].…”

Section: Pre-processing and User Initializationmentioning

confidence: 99%

Right Ventricular Endocardial Segmentation in CMR Images using a Novel Inter:Modality Statistical Shape Modelling Approach

Piazzese

Carminati

Krause

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Nearly automated motion artifacts correction between multi breath-hold short-axis and long-axis cine CMR images

Cited by 16 publications

References 33 publications

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Improving left ventricular segmentation in four‐dimensional flow MRI using intramodality image registration for cardiac blood flow analysis

Right Ventricular Endocardial Segmentation in CMR Images using a Novel Inter:Modality Statistical Shape Modelling Approach

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