Volume 1 Issue 1 - 2014
Review Article
Hao Gong, Miao Chuang, Hengyong Yu, and Guohua Cao*
A number of reconstruction algorithms have been developed to reduce radiation dose from x-ray computed tomography (CT). However, the performance of these algorithms has not been reproducibly evaluated using realistic datasets that are accessible to the public. In this data paper, we present a group of five datasets acquired from our in-house developed cone-beam micro-tomography (micro-CT) scanner and a commercial micro-CT scanner. Such datasets can be used to evaluate 2D or 3D reconstruction algorithms in terms of noise level, contrast-to-noise ratio (CNR), uniformity, spatial resolution, image artifacts, and pixel value accuracy. As an example study, the recently developed improved-distance-driven-model (IDDM) is evaluated based the well-known simultaneous algebraic reconstruction technique (SART) framework using these datasets, and the results confirm the usefulness of the presented datasets.

Baodong Liu, Akiva Mintz, and Hengyong Yu*
To significantly improve the temporal resolution for dynamic cardiac imaging, multiple cameras have been proposed to integrate in a single-photon emission computed tomography (SPECT) system focusing on a heart-size region-of interest (ROI). However, the truncated projections will cause interior SPECT problem, which doesn't have a unique solution. Inspired by the recent results on interior tomography in the x-ray computed tomography (CT) field, the compressive sensing (CS)-based interior SPECT theories have been established. However, those theoretical results have not been evaluated using a realistic dataset acquired from a clinical SPECT scanner. In this data paper, we present a phantom dataset acquired from a clinical SPECT scanner and use it to evaluate the CS-based iterative reconstruction algorithm for interior SPECT.
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