Four-Dimensional Similarity Filter for Cerebral CT Perfusion Maps in Acute Stroke.

Background Brain CT perfusion is used for diagnosing ischemic stroke and treatment planning. Purpose To optimize the four-dimensional similarity filter (4DSF), a noise-reducing filter, for CT perfusion in patients with acute stroke. Materials and Methods To reduce the noise of dynamic perfusion images, the 4DSF averages voxels with the most similar time-intensity curves.

Empirical motion-artifact reduction for non-rigid motion in dedicated breast CT.

Objective: The goal of this work is to develop a data-driven empirical motion-artifact reduction algorithm for non-rigid motion in dedicated breast CT.

Methods: Breast CT is a novel imaging modality that offers fully 3D images at good spatial resolution without breast compression and tissue overlap. However, the slow rotation speed of the gantry in such systems increases the likelihood of motion artifacts.

Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

Background: Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Background: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful.

Development, validation, and simplification of a scanner-specific CT simulator.

Background: Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results.