Deep learning in vivo catheter tip locations for photoacoustic-guided cardiac interventions

Interventional cardiac procedures often require ionizing radiation to guide cardiac catheters to the heart. To reduce the associated risks of ionizing radiation, photoacoustic imaging can potentially be combined with robotic visual servoing, with initial demonstrations requiring segmentation of catheter tips. However, typical segmentation algorithms applied to conventional image formation methods are susceptible to problematic reflection artifacts, which compromise the required detectability and localization of the catheter tip.

We describe a convolutional neural network and the associated customizations required to successfully detect and localize in vivo photoacoustic signals from a catheter tip received by a phased array transducer, which is a common transducer for transthoracic cardiac imaging applications.

We trained a network with simulated photoacoustic channel data to identify point sources, which appropriately model photoacoustic signals from the tip of an optical fiber inserted in a cardiac catheter. The network was validated with an independent simulated dataset, then tested on data from the tips of cardiac catheters housing optical fibers and inserted into ex vivo and in vivo swine hearts.

When validated with simulated data, the network achieved an $F1$ score of 98.3% and Euclidean errors (mean ± one standard deviation) of $1.02\pm 0.84\mathrm{mm}$ for target depths of 20 to 100 mm. When tested on ex vivo and in vivo data, the network achieved $F1$ scores as large as 100.0%. In addition, for target depths of 40 to 90 mm in the ex vivo and in vivo data, up to 86.7% of axial and 100.0% of lateral position errors were lower than the axial and lateral resolution, respectively, of the phased array transducer.

These results demonstrate the promise of the proposed method to identify photoacoustic sources in future interventional cardiology and cardiac electrophysiology applications.