Traditional iterative tomographic reconstruction methods resort to gradient decent methods and require significant computation due to slow convergence. We divide the iterative reconstruction into two stages in the image and Radon spaces, respectively. First, we refine the reconstruction result by image space adaptive filtering. This finds a feasible update direction based on signal modeling. Second, we minimize the discrepancy between the sinograms along the update direction in Radon space and guarantee convergence. Reconstruction from clinical data using the proposed algorithm converges extremely fast and provides satisfactory reconstruction results in far fewer iterations than traditional methods.

Accurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.

Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.

Image restoration has been extensively studied in the past. But multi-image based restoration/compounding is still surprisingly primitive. It usually starts with weighted averaging of the multiple images followed by single-image based restoration methods, which discards the abundant information hinted in the multiple images that can help the restoration process. In this paper, we utilize the fact that the images are corrupted by independent noise and design a new independence measurement based on the properties of independent random variables. The new independence measurement can be efficiently evaluated and imposed as an energy term into the traditional maximum a posteriori (MAP) framework, compensating to the generative models of signal and noise. It can effectively prevent the signal from being smoothed out as noise and hence dramatically improve the restoration quality and robustness, especially when accurate noise/signal models are difficult to obtain. Experiments on real medical images show very promising results.

Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.