Machine learning approaches to problem-solving are growing rapidly within healthcare, and
radiation oncology is no exception. With the burgeoning interest in machine learning comes …

Deep learning technologies and applications demonstrate one of the most important
upcoming developments in radiology. The impact and influence of these technologies on …

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which
is highly desirable for numerous clinical applications. This can not only reduce the scanning …

Photoacoustic imaging is an emerging imaging modality that is based upon the
photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure …

In this paper, we propose a novel deep convolutional neural network (CNN)-based
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …

Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural
network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low …

Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …

There is an increasing need to remotely monitor people in daily life using radio-frequency
probe signals. However, conventional systems can hardly be deployed in real-world settings …

X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …

Purpose Due to the potential risk of inducing cancer, radiation exposure by X‐ray CT
devices should be reduced for routine patient scanning. However, in low‐dose X‐ray CT …