Direct reconstruction of positron emission tomography (PET) data using deep neural
networks is a growing field of research. Initial results are promising, but often the networks are …

Iterative X-ray computed tomography (CT) algorithms have the potential for producing high-quality
images but are computationally very demanding, especially when applied to high-…

High spatial resolution ( ∼13.7 mm/pixel ) neutron tomography was performed on partially
water-saturated compacted silica sand specimens with two different grain morphologies (…

The complexity and throughput of computer networks are rapidly increasing as a result of
the proliferation of interconnected devices, data-driven applications, and remote working. …

Purpose: Neural network image reconstruction directly from measurement data is a
relatively new field of research, which until now has been limited to producing small single-slice …

This article presents and validates a newly developed GATE model of the Siemens Inveon
trimodal imaging platform. Fully incorporating the positron emission tomography (PET), single-…

Tomographic image reconstruction is often formulated as a regularized weighted least squares
(RWLS) problem optimized by iterative algorithms that are either inherently algebraic or …

Positron emission tomography (PET) scanners continue to increase sensitivity and axial
coverage by adding an ever expanding array of block detectors. As they age, one or more block …

String alignment by dynamic programming is generalized to include cyclic shift and
corresponding optimal alignment cost for strings representing cyclic patterns. A guided search …

Currently, there are no available means in the United States to document objectively the
location and extent of amyloid deposits in patients with systemic forms of amyloidosis. To …