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Dustin Ragan

Publications and source records attributed to Dustin Ragan.

2 recordsLinked to original sources

A fast spin echo two-point Dixon technique and its combination with sensitivity encoding for efficient T2-weighted imaging.

A fast spin echo two-point Dixon (fast 2PD) technique was developed for efficient T2-weighted imaging with uniform water and fat separation. The technique acquires two interleaved fast spin echo images with water and fat in-phase and 180 degrees out-of-phase, respectively, and generates automatically separate water and fat images for each slice. The image reconstruction algorithm uses an improved and robust region-growing scheme for phase correction and achieves consistency in water and fat identification between different slices by exploiting the intrinsic correlation between the complex images from two neighboring slices. To further lower the acquisition time to that of a regular fast spin echo acquisition with a single signal average, we combined the fast 2PD technique with sensitivity encoding (SENSE). Phantom experiments show that the fast 2PD and SENSE are complementary in scan efficiency and signal-to-noise ratio (SNR). In vivo data from scanning of clinical patients demonstrate that T2-weighted imaging with uniform and consistent fat separation, including breath-hold abdominal examinations, can be readily performed with the fast 2PD technique or its combination with SENSE.

Abdomen↗

Semiautomated four-dimensional computed tomography segmentation using deformable models.

The purpose of this work is to demonstrate a proof of feasibility of the application of a commercial prototype deformable model algorithm to the problem of delineation of anatomic structures on four-dimensional (4D) computed tomography (CT) image data sets. We acquired a 4D CT image data set of a patient's thorax that consisted of three-dimensional (3D) image data sets from eight phases in the respiratory cycle. The contours of the right and left lungs, cord, heart, and esophagus were manually delineated on the end inspiration data set. An interactive deformable model algorithm, originally intended for deforming an atlas-based model surface to a 3D CT image data set, was applied in an automated fashion. Triangulations based on the contours generated on each phase were deformed to the CT data set on the succeeding phase to generate the contours on that phase. Deformation was propagated through the eight phases, and the contours obtained on the end inspiration data set were compared with the original manually delineated contours. Structures defined by high-density gradients, such as lungs, cord, and heart, were accurately reproduced, except in regions where other gradient boundaries may have confused the algorithm, such as near bronchi. The algorithm failed to accurately contour the esophagus, a soft-tissue structure completely surrounded by tissue of similar density, without manual interaction. This technique has the potential to facilitate contour delineation in 4D CT image data sets; and future evolution of the software is expected to improve the process.

Algorithms↗