This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in
combination with other methods. The planning and delivery of radiotherapy treatment is a …

Purpose Recently, several attempts were conducted to transfer deep learning to medical
image reconstruction. An increasingly number of publications follow the concept of …

Deep learning-based image processing is capable of creating highly appealing results.
However, it is still widely considered as a “blackbox” transformation. In medical imaging, this …

With the massive influx of multimodal data in the last decade, the role of data analytics in
health informatics has grown rapidly. Deep learning (DL) is defined as a technology based …

Despite the visually appealing results, most Deep Learning-based super-resolution
approaches lack the comprehensibility that is required for medical applications. We propose …

Image reconstruction from insufficient data is common in computed tomography (CT), eg,
image reconstruction from truncated data, limited-angle data and sparse-view data. Deep …

Percutaneous coronary intervention (PCI) is an effective treatment for increasing blood flow
in narrowed coronary arteries by implanting stents. When inserting a guidewire during PCI, a …

The potential benefit of hybrid X-ray and MR imaging in the interventional environment is
large due to the combination of fast imaging with high contrast variety. However, a vast …

It is a computational approach, which uses a deep learning model with an architecture
similar to that of biological brain networks, that has been trained using vast amounts of data …