Digital Subtraction Angiography (DSA) aims at selectively displaying vessels by subtracting an unenhanced mask image from a contrast-enhanced fluoroscopic image. This strategy requires the data to be static, i.e. to be acquired without patient or C-arm motion. Thus, conventional DSA cannot be applied to dynamic acquisition protocols such as bolus injection chases, which are particularly useful for the diagnosis of peripheral arterial disease (PAD). Preliminary studies have shown that convolutional neural networks (CNNs) are capable of overcoming this drawback, by predicting DSA-like images directly from their corresponding fluoroscopic x-ray images without the need for the acquisition of a mask image. Here, we demonstrate the potential of this approach for fluoroscopic acquisitions of the lower extremities. We apply the network to twelve different patient exams of which nine are without C-arm motion and the remaining three are bolus chase studies with C-arm motion. For cases where a conventional DSA is feasible we examine very small deviations and observe predictions for the bolus chase studies of similar visual impression as with conventional DSA. The results indicate that Deep DSA has the potential to improve the diagnosis of PAD by generating DSA-equivalent images from bolus chase studies of the lower extremities.