Convolutional neural network (CNN)-based deep learning techniques have enjoyed many
successful applications in the field of medical imaging. However, the complicated between …

Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies
as they allow significant dose reduction during CT scans while maintaining the diagnostic …

Fully supervised deep learning (DL) methods have been widely used in low-dose CT
(LDCT) imaging field and can usually achieve high accuracy results. These methods require …

Low-dose CT imaging requires reconstruction from noisy indirect measurements which can
be defined as an ill-posed linear inverse problem. In addition to conventional FBP method in …

To suppress the streak artifacts in images reconstructed from sparse-view projections in
computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep …

As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the
theme of deep tomographic reconstruction, the second special issue is assembled to reflect …

Fast and low-dose reconstructions of medical images are highly desired in clinical routines.
We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework …

For conventional computed tomography (CT) image reconstruction tasks, the most popular
method is the so-called filtered-back-projection (FBP) algorithm. In it, the acquired Radon …

Reducing the radiation dose is always an important topic in modern computed tomography
(CT) imaging. As the dose level reduces, the conventional analytical filtered backprojection …

Purpose. Although deep learning (DL) technique has been successfully used for computed
tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) …