  Automatic detection of contrast inflow in X-ray angiographic sequences can
facilitate image guidance in computer-assisted cardiac interventions. In this
paper, we propose two different approaches for prospective contrast inflow
detection. The methods were developed and evaluated to detect contrast frames
from X-ray sequences. The first approach trains a convolutional neural network
(CNN) to distinguish whether a frame has contrast agent or not. The second
method extracts contrast features from images with enhanced vessel structures;
the contrast frames are then detected based on changes in the feature curve
using long short-term memory (LSTM), a recurrent neural network architecture.
Our experiments show that both approaches achieve good performance on detection
of the beginning contrast frame from X-ray sequences and are more robust than a
state-of-the-art method. As the proposed methods work in prospective settings
and run fast, they have the potential of being used in clinical practice.
