Model-based iterative reconstruction (MBIR) methods based on maximum a posteriori (MAP) estimation have been
recently introduced to multi-slice CT scanners. The model-based approach has shown promising image quality
improvement with reduced radiation dose compared to conventional FBP methods, but the associated high computation
cost limits its widespread use in clinical environments. Among the various choices of numerical algorithms to optimize
the MAP cost function, simultaneous update methods such as the conjugate gradient (CG) method have a relatively high
level of parallelism to take full advantage of a new generation of many-core computing hardware. With proper
preconditioning techniques, fast convergence speeds of CG algorithms have been demonstrated in 3D emission and 2D
transmission reconstruction. However, 3D transmission reconstruction using preconditioned conjugate gradient (PCG)
has not been reported. Additional challenges in applying PCG in 3D CT reconstruction include the large size of clinical
CT data, shift-variant and incomplete sampling, and complex regularization schemes to meet the diagnostic standard of
image quality. In this paper, we present a ramp-filter based PCG algorithm for 3D CT MBIR. Convergence speeds of
algorithms with and without using the preconditioner are compared.
Breast cancer is the second leading cause of cancer death in women in the United States. Currently, X-ray
mammography is the method of choice for screening and diagnosing breast cancer. However, this 2D projective
modality is far from perfect; with up to 17% breast cancer going unidentified. Over past several years, there has been an
increasing interest in cone-beam CT for breast imaging. However, previous methods utilizing cone-beam CT only
produce approximate reconstructions. Following Katsevich's recent work, we propose a new scanning mode and
associated exact cone-beam CT method for breast imaging. In our design, cone-beam scans are performed along two
tilting arcs for collection of a sufficient amount of data for exact reconstruction. In our Katsevich-type algorithm, conebeam
data is filtered in a shift-invariant fashion and then backprojected in 3D for the final reconstruction. This approach
has several desirable features. First, it allows data truncation unavoidable in practice. Second, it optimizes image quality
for quantitative analysis. Third, it is efficient for sequential/parallel computation. Furthermore, we analyze the
reconstruction region and the detection window in detail, which are important for numerical implementation.
Tomosynthesis is a technique for reconstructing a 3D object from projection data collected within a limited-angular
scanning range. In this paper, we describe and evaluate a methodology for reconstructing a region of interest (ROI) by
combining a global low-resolution CT scan and a local high-resolution tomosynthetic scan. First, a low-resolution CT
scan is acquired. Then, a high-resolution tomosynthetic scan is performed with respect to the ROI. Finally, the ROI is
reconstructed from these two datasets. Our tomosynthetic algorithm is evaluated on a state-of-the-art flat-panel detector
based CT system using a standard CT performance phantom. The experimental results demonstrate that our modality
fusion approach effectively eliminates the interference from surrounding structures and minimizes the shading problem,
as compared to the tomosynthetic results obtained without utilizing the low-resolution CT scan. In conclusion, our
approach provides better ROI reconstruction than tomosynthesis, and uses lower dose than CT. Hence, it may be used
for temporal bone imaging, etc.
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