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Erschienen in:
04.08.2018 | Review Article
Model checking for trigger loss detection during Doppler ultrasound-guided fetal cardiovascular MRI
Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2018
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Purpose
Ultrasound (US) is the state of the art in prenatal diagnosis to depict fetal heart diseases. Cardiovascular magnetic resonance imaging (CMRI) has been proposed as a complementary diagnostic tool. Currently, only trigger-based methods allow the temporal and spatial resolutions necessary to depict the heart over time. Of these methods, only Doppler US (DUS)-based triggering is usable with higher field strengths. DUS is sensitive to motion. This may lead to signal and, ultimately, trigger loss. If too many triggers are lost, the image acquisition is stopped, resulting in a failed imaging sequence. Moreover, losing triggers may prolong image acquisition. Hence, if no actual trigger can be found, injected triggers are added to the signal based on the trigger history.
Method
We use model checking, a technique originating from the computer science domain that formally checks if a model satisfies given requirements, to simultaneously model heart and respiratory motion and to decide whether respiration has a prominent effect on the signal. Using bounds on the physiological parameters and their variability, the method detects when changes in the signal are due to respiration. We use this to decide when to inject a trigger.
Results
In a real-world scenario, we can reduce the number of falsely injected triggers by 94% from more than 87% to less than 5%. On a subset of motion that would allow CMRI, the number can be further reduced to below 0.2%. In a study using simulations with a robot, we show that our method works for different types of motions, motion ranges, starting positions and heartbeat traces.
Conclusion
While DUS is a promising approach for fetal CMRI, correct trigger injection is critical. Our model checking method can reduce the number of wrongly injected triggers substantially, providing a key prerequisite for fast and artifact free CMRI.