Chun Jiao

Multiscale noise reduction on low-dose CT sinogram by stationary wavelet transform

Low-dose protocol for computed tomography (CT) scans has been gradually used in clinics to lower the radiation exposure for mass screening. However, the high noise during data acquisition (and therefore degraded image quality) impairs diagnostic accuracy. This work explores a multiscale approach to reduce non-stationary Gaussian noise in low-dose CT sinograms by wavelet analysis. To explore the noise property in wavelet domain, statistical analysis on the distribution of wavelet coefficients was performed with different basic functions, using computer simulation. A stationary wavelet transform was chosen to alleviate the Gibbs ringing effect caused by thresholding process with orthogonal basic functions. A Bayesian analysis was applied to estimate the local variance at each decomposed scale so that noise reduction on the wavelet coefficients becomes adaptive to each scale. Both computer simulations and phantom experiments were performed to show the potential of the presented local-adaptive stationary wavelet transform for low-dose CT. Comparing with traditional smoothing filters and wavelet-based thresholding denoising methods, the proposed method demonstrated noticeable improvement on noise reduction and edge preservation of low-dose CT images.

Prior image based anisotropic edge guided TV minimization for few-view CT reconstruction

To improve the spatial resolution of the image reconstructed by the conventional total variation (TV) algorithm, we propose a prior image based anisotropic edge guided TV minimization (PIEGTV) algorithm for few-view CT reconstruction. In this study, an anisotropic edge of the prior image is detected using the proposed edge detector. Then the weights of the TV discretization term for the to-be-estimated image are updated by the anisotropic edge information. To solve the minimization problem of the PIEGTV reconstruction, a similar TV-based minimization implementation is developed to deal with the raw data fidelity and other constraints. The results with computer simulations for the Shepp-Logan phantom and experimental data from a physical phantom demonstrate that the proposed algorithm can yield images with noticeable gains in edge preserving and shape preserving for small structures, compared to the conventional and a few modified TV algorithms.

A ROC-based feature selection method for computer-aided detection and diagnosis

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier’s suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

3D finite element model for treatment of cleft lip

Cleft lip is a congenital facial deformity with high occurrence rate in China. Surgical procedure involving Millard or Tennison methods is usually employed for treatment of cleft lip. However, due to the elasticity of the soft tissues and the mechanical interaction between skin and maxillary, the occurrence rate of facial abnormality or dehisce is still high after the surgery, leading to multiple operations of the patient. In this study, a framework of constructing a realistic 3D finite element model (FEM) for the treatment of cleft lip has been established. It consists of two major steps. The first one is the reconstruction of a 3D geometrical model of the cleft lip from scanning CT data. The second step is the build-up of a FEM for cleft lip using the geometric model, where the material property of all the tetrahedrons was calculated from the CT densities directly using an empirical curve. The simulation results demonstrated (1) the deformation procedure of the model step-by-step when forces were applied, (2) the stress distribution inside the model, and (3) the displacement of all elements in the model. With the computer simulation, the minimal force of having the cleft be repaired is predicted, as well as whether a given force sufficient for the treatment of a specific individual. It indicates that the proposed framework could integrate the treatment planning with stress analysis based on a realistic patient model.

Mathematical Modeling of the Push-Pull Effect: Effect of Acceleration Profiles

The push-pull effect (PPE) remains a critical problem for modern fighter aircrafts. The present study is part of an ongoing effort that uses mathematical models to investigate the effects of push-pull maneuver on the cardiovascular system and its possible countermeasures. A distributed hemodynamic model with carotid baroreflex has been employed to reflect the transient response of cardiovascular system to PPE. With simulation studies, the effect of different factors of push-push maneuver such as baseline, duration and onset rate of push phase, and onset rate of pull phase is explored. Comparison with centrifuge experimental data published validates the results and indicates that mathematical modeling is a promising tool for the study on PPE mechanism and its countermeasures.

Fully 4D cardiac gated SPECT reconstruction with simultaneous compensation in KL domain

Recent researches have shown promise in applying KL transform to 4D gated sinogram for pre-reconstruction temporal smoothing and quasi-4D inversion of attenuated Radon transform. To achieve quantitative 4D reconstruction, this work aims to compensate for the major degradation factors, including distance-dependent collimator resolution variation and object-specific photon scatter, simultaneously within the KL framework. To alleviate the influence of cardiac motion on reconstruction, heart motion was classified into several groups based on inter-frame similarities and each group underwent a corresponding KL transform. In the KL domain, non-stationary Poisson noise was stabilized by Anscombe transform and treated by adaptive Wiener filtration. Scatter contribution to the primary energy window was then estimated and removed based on photon detection energy spectrum and the triple-energy-window acquisition formula after noise treatment. The scatter-corrected data was further subject to a depth-dependent deconvolution, based on the distance frequency relationship, with measured detector response kernel in the KL domain. The deconvoluted sinograms were reconstructed by inverting the attenuated Radon transform for each KL component and the 4D SPECT images were obtained by a corresponding inverse KL transform for each group. The simultaneous compensation strategy in the KL domain was tested by computer simulations from digital phantoms of 128 cubic array and clinical data from a patient. The adaptive KL transform for different groups consisting of frames with similar activity dynamics showed noticeable improvement over our previous work of using a single KL transform for all frames. Improvement was also seen by the adaptive noise treatment of all the KL components over previous work of discarding the higher-order components. Further improvement by considering the scatter and resolution variation was demonstrated.