Synopsis

Anomalies of the coronary ostia can have severe consequences. To provide a screening solution, automated ostia detection based on single breath-hold coronary MRA scans is presented. The aorta is segmented in the data sets to serve as an orientation point and vesselness enhancing filters are applied. Searching the aorta surface for high vessel responses by a ray-tracing procedure yields information about the position of the coronary ostia. The proposed approach was successfully validated in 10 volunteers with an average deviation of $$$7.6 \pm 1.0$$$° in angular and $$$1.2 \pm 0.58$$$ mm in superior-inferior direction.

Introduction

Methods

Highly accelerated single-breath-hold 3-D imaging was performed with a prototype sequence and reconstruction in $$$10$$$ healthy volunteers ($$$6$$$ female, $$$42 ± 13$$$ years) on a 1.5T clinical MR scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). Imaging was performed with a FOV in axial slice orientation covering the proximal part of the coronary arteries. For data acquisition, a prototype sequence was used for 3-D volume-selective, T2-prepared, ECG-triggered, fat-saturated bSSFP imaging with the following parameters: TR $$$4.0$$$ ms, TE $$$2.0$$$ ms, FOV $$$310$$$x$$$225$$$x$$$(79$$$-$$$105)$$$ mm³, acquired voxel size $$$1.2$$$x$$$1.5$$$x$$$1.4$$$ mm³ interpolated to $$$1.2$$$x$$$1.2$$$x$$$1.1$$$ mm³. Accelerated data acquisition was performed as described by Forman et al.⁴ with an acceleration factor of $$$10.6$$$, and images were reconstructed using compressed sensing with $$$20$$$ iterations and a regularization factor of $$$0.008$$$.

After compressed sensing reconstruction, the coronary ostia were automatically detected in the resulting 3D volumes with a multi-step procedure (see Figure 1):
In the preprocessing step, similar image characteristics among all data sets were ensured by intensity normalization and thresholding.
Aorta segmentation was performed to obtain the contour of the aorta that later served as initialization for the ostia detection. Starting with an automatically detected landmark point in the aorta from a preceding localizer scan⁵, the aorta was segmented using an active contour algorithm driven by a level set method⁶. Oversegmentation in superior-inferior direction was avoided by bi-directional, slice-wise segmentation and termination of the process if the perimeter between consecutive slices exceeded a certain threshold.
In coronary vessel detection, a vesselness mask T_σ was generated using the methods described in ^7,8, while the vessel size σ corresponds to the diameter of the coronaries. For the final vesselness mask, the responses from larger size structures were removed by subtracting T_3σ from T_σ (see Figure 2).
Finally, ostia detection was performed based on the results of both previous steps and a ray-tracing procedure. Starting from the slice-wise segmented aorta boundary, rays were cast in radial directions. For each ray, we accumulated the filter response from the vesselness mask estimated in the coronary vessel detection step (see Figure 3). The coronary ostia were then selected as the two aorta boundary points with the largest accumulated response assigned to it (see Figure 4).

The automated segmentation was compared to the ground truth segmentation of five experts and the angular deviations, and Euclidean distances in superior-inferior direction between the mean of the annotated positions and the automatically detected ostia were computed.

Results and Discussion

The acquisition time was on average $$$24 ± 5$$$ sec. Fully automated ostia detection was successful in all cases. The average angular deviation between the automatically detected and manually annotated ostia positions was $$$6.6 ± 5.7$$$° for the left and $$$8.7 ± 5.2$$$° for the right coronary ostia and the displacement in superior-inferior direction $$$2.5 ± 1.6$$$ and $$$0.6 ± 0.3$$$ mm. The mean standard deviation of the annotated ostia was $$$3.6 ± 1.1$$$° in angular and $$$1.2 ± 0.58$$$ mm in superior-inferior direction. The increased standard deviations for the left ostium may be caused by lacking visibility, e.g. due to gaps in the coronary arteries. This is also in accordance with an increased standard deviation of the manually annotated ostia positions.

Conclusion

Acknowledgements

References

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