Sotirios A. Tsaftaris1,2, Erik Offerman3,
Robert R. Edelman3, Ioannis Koktzoglou3
1Electrical Engineering and Computer
Science, Northwestern University, Evanston, IL, United States; 2Radiology,
Northwestern University, Chicago, IL, United States; 3Radiology,
NorthShore University HealthSystem, Evanston, IL, United States
Ghost
magnetic resonance angiography (MRA) has been proposed as an unenhanced and
ungated method for angiography. The method requires manual post-processing to
identify suitable slices in a large stack from which to create an
interpretable angiogram. To maximize the contrast of the final angiogram it
is necessary to eliminate slices located within the body and to carefully
select the slices that contain conspicuous ghost artifacts. This
time-consuming process can also introduce unwanted inter- and intra- observer
variability. The purpose of this work was to completely automate the
reconstruction process during ungated and non-contrast-enhanced Ghost MRA
using image analysis and clustering.