Statistical iterative methods for image reconstruction like maximum likelihood expectation maximization (ML-EM) are more robust and flexible than analytical inversion methods and allow for accurately modeling the counting statistics and the photon transport during acquisition. They are rapidly becoming the standard for image reconstruction in emission computed tomography. The maximum likelihood...

PURPOSE To prospectively compare traditional filtered back projection (FBP) and iterative image reconstruction for the evaluation of heavily calcified arteries with coronary computed tomography (CT) angiography. MATERIALS AND METHODS The study had institutional review board approval and was HIPAA compliant. Written informed consent was obtained from all patients. Fifty-five consecutive patien...

The increased use of computed tomography (CT) and its high radiation dose have led to great concerns about its potential for radiation induced cancer risks. Breast is a radiosensitive tissue based on tissue weighting factors assigned by the International Commission on Radiological Protection (ICRP). Moreover, the dose is maximal on the surface of the patient. Therefore, strategies should be tak...

Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing the sampling rate in the projection view angle in computed tomography (CT). Most of the image reconstruction algorithms, developed for this purpose, solve a nonsmooth convex optimization problem involving the image total variation (TV). The TV seminorm is the `1 norm of the image gradient magnit...

The paper introduces a novel method for image reconstruction in 3D tomography, called Searchlight Computed Tomography, which reduces the overall radiation exposure when primarily the reconstruction of a specified region of interest is required. To achieve this goal, the Searchlight CT approach restricts the acquisition essentially to X-rays passing through the region of interest of a 3D object,...

The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network ...