Filtered back projection (FBP) reconstruction is simple and computationally efficient and is used in many commercial CT (tomography) imaging products. However, higher Poisson noise levels or metal objects in the imaged area can lead to severe artifacts. Iterative reconstruction employs stochastic models for the imaging process and the characteristics of the medical images and can reduce Poisson noise and metal related artifacts. But it is computation-intensive and furthermore, its image models are relatively simple and cannot quite capture the highly complex nature of the medical images, leaving rooms for further improvement. Recent advances in neural networks and deep learning could offer potential solutions to overcome these two problems. Towards that end, most of the neural networks proposed so far for CT image reconstruction are feed-forward networks with CNN (convolutional neural network) and fully connected layers, attempting to learn the mapping from the projections or the FBP output to the reconstructed image. While these networks have demonstrated some promising reconstruction or post-processing results, their architectures are somewhat arbitrary and the question remains as to what would be a more principled way to find a good architecture, thereby further improving reconstruction results. One promising idea is to design the network structure based on signal processing principles such as MAP (maximum a posteriori) estimation and iterative optimization. In this work, we developed a novel RNN (recurrent neural network) based on an accelerated iterative MAP estimation algorithm. This network makes use of, rather than learn, the forward image model such that the learning can be focused on the image or prior model and acceleration. This has led to good reconstruction results where Poisson noise and metal artifacts are greatly reduced.
Stochastic or model-based iterative reconstruction is able to account for the stochastic nature of the CT imaging process and some artifacts and is able to provide better reconstruction quality. It is also, however, computationally expensive. In this work, we investigated the use of some of the neural network training algorithms such as momentum and Adam for iterative CT image reconstruction. Our experimental results indicate that these algorithms provide better results and faster convergence than basic gradient descent. They also provide competitive results to coordinate descent (a leading technique for iterative reconstruction) but, unlike coordinate descent, they can be implemented as parallel computations, hence can potentially accelerate iterative reconstruction in practice.
The detector panel on a typical CT machine today is made of more than 500 detector boards, nicknamed chiclets. Each chiclet contains a number of detectors (i.e., pixels). In the manufacturing process, the chiclets on the panel need to go through an iterative test, swap, and test (TST) process, till some image quality level is achieved. Currently, this process is largely manual and can take hours to several days to complete. This is inefficient and the results can also be inconsistent. In this work, we investigate techniques that can be used to automate the iterative TST process. Specifically, we develop novel prediction techniques that can be used to simulate the iterative TST process. Our results indicate that deep neural networks produce significantly better results than linear regression in the more difficult prediction scenarios.
X-ray machines are widely used for medical imaging and their cost is highly dependent on their image resolution.
Due to economic reasons, lower-resolution (lower-res) machines still have a lot of customers, especially in developing
economies. Software based resolution enhancement can potentially enhance the capabilities of the lower-res
machines without significantly increasing their cost hence, is highly desirable. In this work, we developed an
algorithm for X-ray image resolution enhancement. In this algorithm, the fractal idea and cross-resolution patch
matching are used to identify low-res patches that can be used as samples for high-res patch/pixel estimation.
These samples are then used to generate a prior distribution and used in a Bayesian MAP (maximum a posteriori)
optimization to produce the high-res image estimate. The efficacy of our algorithm is demonstrated by
experimental results.
Compressed sensing can recover a signal that is sparse in some way from a small number of samples. For computed tomography (CT) imaging, this has the potential to obtain good reconstruction from a smaller number of projections or views, thereby reducing the amount of radiation that a patient is exposed to In this work, we applied compressed sensing to fan beam CT image reconstruction, which is a special case of an important 3-D CT problem (cone beam CT). We compared the performance of two compressed sensing algorithms, denoted as the LP and the QP, in simulation. Our results indicate that the LP generally provides smaller reconstruction error and converges faster; therefore, it is preferable.
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