Maria Monzon^{1,2}, Seung Su Yoon^{1,2}, Carola Fischer^{2}, Andreas Maier^{1}, Jens Wetzl^{2}, and Daniel Giese^{2}

^{1}Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, ^{2}Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany

The analysis of mitral valve motion is known to be relevant in the diagnosis of cardiac dysfunction. Dynamic motion parameters can be extracted from Cardiac Magnetic Resonance (CMR) images. We propose two chained Convolutional Neural Networks for automatic tracking of mitral valve-annulus landmarks on time-resolved 2-chamber and 4-chamber CMR images. The first network is trained to detect the region of interest and the second to track the landmarks along the cardiac cycle. We successfully extracted several motion-related parameters with high accuracy as well as analyzed unlabeled datasets, thereby overcoming time-consuming annotation and allowing statistical analysis over large number of datasets.

We propose a robust, fully automated algorithm that tracks the MVA insertion points on 2-chamber (2CHV) and/or 4-chamber (4CHV), CINE CMR series. The network system initially detects the mitral valves’ region of interest (ROI) before extracting the time-resolved MVA landmarks. This information is then used to extract motion-related parameters including velocities (e’-waves) and diameters. The system performance is analyzed based on annotated data. Thereafter, motion parameters are extracted retrospectively on N=1468 unlabeled datasets

In recent work, atrioventricular plane tracking was shown feasible using direct coordinate regression, however, without temporal feature extraction

- MVA plane displacement (
**MVAPD**) curve was defined as the time-resolved perpendicular distance of the MVA plane relative to the first frame. Peak displacement (**MVAPD-PD**) was also extracted^{4,12}. - MVA plane velocity (
**MVAPV**) was derived as the MVAPD time-resolved discrete temporal derivate^{4,12}. Early diastolic velocity (**MVAPV-e´**) was then defined as the central maximum of the MVAPV. - The total motion of the annulus
^{}(**VAD**) was quantified as the total displacement sum over all timeframes in mm. - The septal and lateral MVA landmark velocity curves (
**SMVAV, LMVAV**) were computed as the temporal derivative of each landmark displacement^{ 13}. The central maximum of each curve represents early annular diastolic velocity (**MAVL-e’**). -
The time-resolved diameter evolution throughout the cardiac cycle was
derived as the Euclidean distance between landmarks in mm and the maximum
diameter
^{14 }(**MAMD**) as well as the difference between maximum and minimum diameter (**MADC**) were extracted^{6}.

Bland-Altman analysis revealed following mean agreement values (Fig. 4) : MVAPD-PD: $$$0.51\pm2.42 $$$ mm, MVAPV-e´: $$$0.08\pm2.44 $$$ cm/s, VAD: $$$15.39\pm54.62$$$ mm and for MAVL-e’ $$$0.08 \pm3.73$$$ cm/s; MAMD: $$$0.31\pm3.66$$$ mm and MADC $$$0.28\pm3.12$$$ mm.

The localization network fails to locate the ROI in less than $$$0.5\%$$$ of unlabeled datasets and at least one time-frame was not smoothly tracked in $$$16.53\%$$$ of unlabeled series.

Future work will include on-line integration of the approach and analysis of motion parameters on larger numbers of categorized patient datasets. Furthermore, a prospective slice-tracking CMR acquisition is planned for improved morphological and/or flow measurements of the mitral valve

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**Figure 2: a)** Feature extraction 2D Residual and 3D convolution
blocks. Each residual block consists of a spatial convolution(CONV)(3x3), Batch Normalization (BN) and Leaky Rectified Linear Units (LReLU)
activation layers. The 3D block consist of double spatial and temporal CONV(3x3x3)-BN-LReLU operations. **b) **Localization CNN architecture based on 2-D UNet with 3 encoder-decoder blocks. **c) **Landmark tracking Fully CNN architecture details based on 3-D UNet.For down-sampling asymmetrical max-pooling layers were applied into temporal and spatial dimensions.

**Figure 3: a) **Example of network output for a 2CHV and a 4CHV dataset. **b)**
Representation of the MVA plane in systole end diastole with representation of different
MVA motion parameters (MAMD, SMVAV, LMVAV, MVAPD). **c-e)** Mean and standard
deviation of derived parameters from ground truth annotations (black) and
predicted landmarks (red) for test subset (N=12) along the cardiac cycle. The error bars represent the
standard deviation in each plot:** c) **MAVPD and corresponding MAVPV, **d)** SMVAV and
LMVAV, **e)** annulus diameter.