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Perfusion CT (PCT) examinations are getting more frequently used for diagnosis of acute brain diseases such as
hemorrhage and infarction, because the functional map images it produces such as regional cerebral blood flow (rCBF),
regional cerebral blood volume (rCBV), and mean transit time (MTT) may provide critical information in the emergency
work-up of patient care. However, a typical PCT scans the same slices several tens of times after injection of contrast
agent, which leads to much increased radiation dose and is inevitability of growing concern for radiation-induced cancer
risk. Reducing the number of views in projection in combination of TV minimization reconstruction technique is being
regarded as an option for radiation reduction. However, reconstruction artifacts due to insufficient number of X-ray
projections become problematic especially when high contrast enhancement signals are present or patient's motion
occurred.
In this study, we present a novel reconstruction technique using contrast-adaptive TpV minimization that can reduce
reconstruction artifacts effectively by using different p-norms in high contrast and low contrast objects. In the proposed
method, high contrast components are first reconstructed using thresholded projection data and low p-norm total
variation to reflect sparseness in both projection and reconstruction spaces. Next, projection data are modified to contain
only low contrast objects by creating projection data of reconstructed high contrast components and subtracting them
from original projection data. Then, the low contrast projection data are reconstructed by using relatively high p-norm
TV minimization technique, and are combined with the reconstructed high contrast component images to produce final
reconstructed images.
The proposed algorithm was applied to numerical phantom and a clinical data set of brain PCT exam, and the resultant
images were compared with those using filtered back projection (FBP) and conventional TV reconstruction algorithm.
Our results show the potential of the proposed algorithm for image quality improvement, which in turn may lead to dose
reduction.
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