Computed tomography (CT) has been well established as a diagnostic tool through hardware optimization and
sophisticated data calibration. For screening purposes, the associated X-ray exposure risk must be minimized. An
effective way to minimize the risk is to deliver fewer X-rays to the subject or lower the mAs parameter in data
acquisition. This will increase the data noise. This work aims to study the noise property of the calibrated or
preprocessed sinogram data in Radon space as the mAs level decreases. An anthropomorphic torso phantom was
scanned repeatedly by a commercial CT imager at five different mAs levels from 100 down to 17 (the lowest value
provided by the scanner). The preprocessed sinogram datasets were extracted from the CT scanner to a laboratory
computer for noise analysis. The repeated measurements at each mAs level were used to test the normality of the
repeatedly measured samples for each data channel using the Shapiro-Wilk statistical test merit. We further studied the
probability distribution of the repeated measures. Most importantly, we validated a theoretical relationship between the
sample mean and variance at each channel. It is our intention that the statistical test and particularly the relationship
between the first and second statistical moments will improve
low-dose CT image reconstruction for screening
Virtual colonoscopy has been developed as a non-invasive, safe, and low-cost method to evaluate colon polyps.
Implementation and efficiency of virtual colonoscopy requires rigorous cleansing of colon prior to the examination.
Electronic colon cleansing is a new technology that virtually clean stool residues tagged with contrast agents from the
obtained computed tomography (CT) images. From our previous studies on electronic colon cleansing, we found that
residual stool and fluid are often problematic for optimal viewing of colon. In this paper, we focus on developing a
model-based approach to correct both non-uniformity and partial volume effects appearing in regions of bone and tagged
stool residues. A statistical method for maximum a posterior probability (MAP) was developed to identify and virtually
clean the tagged stool residuals. In calculating the solution, the well-known expectation maximization (EM) algorithm is
employed. Experimental results of electronic colon cleansing are promising.
This work investigates a new partial volume (PV) image segmentation framework with comparison to a previous PV approach. The new framework utilizes an expectation-maximization (EM) algorithm to estimate simultaneously (1) tissue fractions in each image voxel and (2) statistical model parameters of the image data under the principle of maximum a posteriori probability (MAP). The previous EM approach models the PV effect by down-sampling a voxel and then labels each sub-voxel as a pure tissue type, where the number of sub-voxels labeled by a given tissue type over the total number of sub-voxels reflects the fraction of that tissue type inside the original voxel. The tissue fractions in each voxel in this discrete PV model are represented by a limited number of percentage values. In the new MAP-EM approach, the PV effect is modeled in a continuous space and estimated directly as the fraction of each tissue type in the original voxel. The previous discrete PV model would converge to our continuous PV tissue-mixture model if there is an infinite number of sub-voxels within a voxel. However, in practice a voxel is usually down-sampled once or twice for computational reasons. A comparison study between this limited down-sampling approach and our continuous PV model reveals, by computer simulations, that our continuous PV model is computationally more effective and thus improves the PV segmentation over the discrete PV model.