Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation

Benameur, Narjes; Caiani, Enrico Gianluca; Alessandrini, Martino; Arous, Younes; Abdallah, N. Ben; Saâdaoui, Foued; Kraiem, Tarek

doi:10.1016/j.mri.2018.08.004

Cited by 9 publications

(6 citation statements)

References 58 publications

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“…In general, cardiac magnetic resonance imaging (MRI) is the commonly used technique for the assessment of left ventricular function [6]. Additionally, non-invasive cardiac CT is also established to characterize left ventricular function in terms of global strain [7] or reginal wall motion stress [3,8] as well as to increase transcatheter aortic valve implantation [9][10][11][12][13] and to simplify stent positioning on severe coronary artery disease [14].…”

Section: Discussionmentioning

confidence: 99%

Predictors of Correct Angulations to Perform Invasive Retrograde Left Ventriculography - Defined by Cardiac 256 Multi-Sliced Computed Tomography

Anger¹,

Wolff²,

Pabst³

et al. 2019

J Integr Cardiol

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Background: Correct angulation performing invasive retrograde probed left ventriculography in the cath-lab is crucial to define individual patient specific wall motion stress of the left ventricle. Since decades, left ventriculography is assessed in LAO 60° and RAO 30°-this can't be right. This study defines the correct angulations.Methods: We used in total n=1000 cardiac 256 multi-sliced computed tomography 3D heart reconstructions retrospectively and reordered the heart in regard to the invasive left ventriculography angulations -thereby, we got the correct angulation parameters. We assessed additionally LV-hypertrophy, gender, age, body mass index (BMI), electrocardiogram (ECG) and QRS axis to characterize specific predictors. Results:We got a statistically significant recipe of changed angulations: Older patients need LAO angle tipped to 50°, younger patients flatter to 42°. Male patients change the Caudal_LAO angle more to 20°, whereas female held the Caudal_LAO angle to 16°. In general, the ECG itself and pre-described LV-hypertrophy are without any impact. In regard to the QRS axis of the ECG only marked left axis deviation changes the angles: LAO 50°/ Caudal_LAO 15°, RAO 20°/ Caudal_RAO 5°. LVhypertrophy changes Caudal_LAO more to 18°, BMI over 30 reduces Caudal_LAO to 11°. Only minor changes in the angulation Caudal_RAO: 4° when BMI is more than 30. No significant changes on RAO 30°. Summary and Conclusion:We declare a recipe for correct angles performing invasive retrograde left ventriculography in the set-up of cath-lab: age-, LV hypertrophy, gender and BMI-dependent. Invasive cardiologists will be forced to follow the recipe of miscellaneous angulations while performing retrograde probed characterization of LV function.

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Section: Discussionmentioning

confidence: 99%

Predictors of Correct Angulations to Perform Invasive Retrograde Left Ventriculography - Defined by Cardiac 256 Multi-Sliced Computed Tomography

Anger¹,

Wolff²,

Pabst³

et al. 2019

J Integr Cardiol

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“…This 2D generalization is known as the monogenic signal [27]. The monogenic signal tool allows the computation of local features from the image such as the local amplitude that represents the energy, image structural information that usually defined by local phase, and the orientation that informs about the dominant direction in an image [28]. The monogenic signal is defined as [29]:…”

Section: Parametric Imaging Methods From the Monogenic Signalmentioning

confidence: 99%

Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

Benameur¹,

Mohammed²,

Mahmoudi³

et al. 2021

Computers, Materials &Amp; Continua

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Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.

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“…A theoretical background on this operator is described in the following. The monogenic signal and the monogenic curvature tensor.According to [25], we can define an intrinsic dimension that expresses the number of degrees of freedom necessary to describe the local structure. The MonoGenic signal (MG) corresponds to the effective instrument for analyzing 1D and 2D signals in case of invariable rotation.…”

Section: The Proposed Systemmentioning

confidence: 99%

“…\right.} \end{array} \end{equation}The MG is presented as a fusion of the image

I(\alpha )

, and its Riesz transform:

\begin{equation} \def\eqcellsep{&}\begin{array}{ccc}I(\alpha )_{M}= (I(\alpha ),RK_{x}\lbrace I\rbrace (\alpha ), RK_{y}\lbrace I\rbrace (\alpha )) \\[3pt] = (I,RK_{x}*I ,RK_{y}*I), \end{array} \end{equation}

where * corresponds to the convolution operator.In [25] and [26], the local orientation is computed as follows:

\begin{equation} \theta = arctan \frac{RK_{y}\lbrace I\rbrace }{RK_{x}\lbrace I\rbrace }, \theta \in [0,\pi ). \end{equation}

Moreover, for any intrinsic 1D signal I(x), we can demonstrate that:

\begin{matrix} \begin{matrix} true \\ right \sqrt{R K_{x}^{2} {I} (0, 0), R K_{y}^{2} {I} (0, 0)} badbreak= | false(h_{1} * I (θ) false) false(0 false) \end{matrix} \end{matrix}

…”

Section: The Proposed Systemmentioning

confidence: 99%

“…In [25] and [26], the local orientation is computed as follows:

\begin{equation} \theta = arctan \frac{RK_{y}\lbrace I\rbrace }{RK_{x}\lbrace I\rbrace }, \theta \in [0,\pi ). \end{equation}

Moreover, for any intrinsic 1D signal I(x), we can demonstrate that:

\begin{equation} \def\eqcellsep{&}\begin{array}{ccc}\begin{split} \sqrt {RK_{x}^{2}\lbrace I\rbrace (0,0), RK_{y}^{2} \lbrace I\rbrace (0,0)}= \vert (h_{1}*I(\theta ))(0) \vert \\[3pt] = \vert \frac{1}{\pi }\int _{t\in \Re } \frac{I(t cos\theta , t sin\theta )}{t}dt \vert .

…”

Section: The Proposed Systemmentioning

confidence: 99%

See 1 more Smart Citation

An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

Gargouri

Mokni

Damak

et al. 2022

IET Image Processing

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Mammography continues to play a central part in the early breast masses diagnosis and has raised several challenges in breast cancer detection. Nevertheless, it remains still difficult to detect abnormalities in a dense breast and some benign lesions may have similar appearance with masses. This study proposes a novel Computer-Aided Diagnosis (CADx) allowing benign and malignant mass classification. Accordingly, relevant textural-shape descriptors are proposed, which is the aggregation of a new Local Binary based feature, namely the Monogenic Gray Level and Local Difference (MGLLD), where both texture characteristics and breast tissue density information, and the Zernike moments are incorporated. A heuristic algorithm is then devised for optimizing the proposed features with respect to inter-class and intra-class distribution, by a convenient ponderation. Then, a set of classifiers are applied for opting to the best decision making solution. The Deep CNN yields the best accuracy, with an Area Under Curve (AUC) of 0.98 on Curated Breast Imaging Subset of DDSM (CBIS-DDSM). In addition, the authors are based on a subjective approval of the extracted Region Of Interest (ROI) by experts in radiology. The proposed scheme proves its effectiveness especially on some challenging breast cancer cases, corresponding to higher breast tissue density. In medical image processing, these results open new perspectives with respect to the use of the deep learning solutions, where the small sample-number is known to be a limitation (because pathologies cannot be intentionally provoked).

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Left ventricular MRI wall motion assessment by monogenic signal amplitude image computation

Cited by 9 publications

References 58 publications

Predictors of Correct Angulations to Perform Invasive Retrograde Left Ventriculography - Defined by Cardiac 256 Multi-Sliced Computed Tomography

Predictors of Correct Angulations to Perform Invasive Retrograde Left Ventriculography - Defined by Cardiac 256 Multi-Sliced Computed Tomography

Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

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