The presence of metal-artifacts in CT imaging can obscure relevant anatomy and interfere with disease diagnosis. The cause and occurrence of metal-artifacts are primarily due to beam hardening, scatter, partial volume and photon starvation; however, the contribution to the artifacts from each of them depends on the type of hardware. A comparison of CT images obtained with different metallic hardware in various applications, along with acquisition and reconstruction parameters, helps understand methods for reducing or overcoming such artifacts. In this work, a metal beam hardening correction (BHC) and a projection-completion based metal artifact reduction (MAR) algorithms were developed, and applied on phantom and clinical CT scans with various metallic implants. Stainless-steel and Titanium were used to model and correct for metal beam hardening effect. In the MAR algorithm, the corrupted projection samples are replaced by the combination of original projections and in-painted data obtained by forward projecting a prior image. The data included spine fixation screws, hip-implants, dental-filling, and body extremity fixations, covering range of clinically used metal implants. Comparison of BHC and MAR on different metallic implants was used to characterize dominant source of the artifacts, and conceivable methods to overcome those. Results of the study indicate that beam hardening could be a dominant source of artifact in many spine and extremity fixations, whereas dental and hip implants could be dominant source of photon starvation. The BHC algorithm could significantly improve image quality in CT scans with metallic screws, whereas MAR algorithm could alleviate artifacts in hip-implants and dentalfillings.
Non-linear iterative reconstruction (IR) algorithms have shown promising improvements in image quality at reduced
dose levels. However, IR images sometimes may be perceived as having different image noise texture than traditional
filtered back projection (FBP) reconstruction. Standard linear-systems-based image quality evaluation metrics are
limited in characterizing such textural differences and non-linear image-quality vs. dose trade-off behavior, hence
limited in predicting potential impact of such texture differences in diagnostic task. In an attempt to objectively
characterize and measure dose dependent image noise texture and statistical properties of IR and FBP images, we have
investigated higher order moments and Haralicks Gray Level Co-occurrence Matrices (GLCM) based texture features on
phantom images reconstructed by an iterative and a traditional FBP method. In this study, the first 4 central order
moments, and multiple texture features from Haralick GLCM in 4 directions at 6 different ROI sizes and four dose levels
were computed. For resolution, noise and texture trade-off analysis, spatial frequency domain NPS and contrastdependent
MTF were also computed. Preliminary results of the study indicate that higher order moments, along with
spatial domain measures of energy, contrast, correlation, homogeneity, and entropy consistently capture the textural
differences between FBP and IR as dose changes. These metrics may be useful in describing the perceptual differences
in randomness, coarseness, contrast, and smoothness of images reconstructed by non-linear algorithms.
Breast cancer continues to be the most common malignancy of women in the United States. Nuclear imaging techniques such as positron emission tomography (PET) have been widely used for the staging of cancer. The primary limitations of PET for breast cancer diagnosis are the lack of a highly specific radiotracer and the limited
resolution of imaging systems. The sensitivity for detecting small lesions is very low. Many groups are developing positron emission mammography (PEM) systems dedicated for breast imaging using high resolution detectors. Although image resolution is significantly improved compared to whole-body PET systems, the clinical value of
a PEM system is yet to be proven,3.4 Most PET systems have limitations in imaging tissues near the chest walls and lymph nodes. The proposed system addresses the sampling requirements specific to breast imaging and achieves high resolution in PET images of breast and thorax.