Shouhua Luo

Rapid and multimodal in vivo bioimaging of cancer cells through in situ biosynthesis of Zn&Fe nanoclusters

Early diagnosis remains highly important for efficient cancer treatment, and hence, there is significant interest in the development of effective imaging strategies. This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe2+ and Zn2+ ions into cancer cells (i.e., HeLa, U87, and HepG2 cancer cells). We found that the biocompatible metal ions Fe2+ and Zn2+ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters. These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging. Meanwhile, for normal cells (i.e., L02) and tissues, neither fluorescence nor any other obvious difference could be detected between preand post-injection. This multimodal bioimaging strategy based on the in situ biosynthesized Zn&Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.

A wire scanning based method for geometric calibration of high resolution CT system

This paper is about geometric calibration of the high resolution CT (Computed Tomography) system. Geometric calibration refers to the estimation of a set of parameters that describe the geometry of the CT system. Such parameters are so important that a little error of them will degrade the reconstruction images seriously, so more accurate geometric parameters are needed in the higher-resolution CT systems. But conventional calibration methods are not accurate enough for the current high resolution CT system whose resolution can reach sub-micrometer or even tens of nanometers. In this paper, we propose a new calibration method which has higher accuracy and it is based on the optimization theory. The superiority of this method is that we build a new cost function which sets up a relationship between the geometrical parameters and the binary reconstruction image of a thin wire. When the geometrical parameters are accurate, the cost function reaches its maximum value. In the experiment, we scanned a thin wire as the calibration data and a thin bamboo stick as the validation data to verify the correctness of the proposed method. Comparing with the image reconstructed with the geometric parameters calculated by using the conventional calibration method, the image reconstructed with the parameters calculated by our method has less geometric artifacts, so it can verify that our method can get more accurate geometric calibration parameters. Although we calculated only one geometric parameter in this paper, the geometric artifacts are still eliminated significantly. And this method can be easily generalized to all the geometrical parameters calibration in fan-beam or cone-beam CT systems.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

A fast beam hardening correction method incorporated in a filtered back-projection based MAP algorithm

The beam hardening effect can induce strong artifacts in CT images, which result in severely deteriorated image quality with incorrect intensities (CT numbers). This paper develops an effective and efficient beam hardening correction algorithm incorporated in a filtered back-projection based maximum a posteriori (BHC-FMAP). In the proposed algorithm, the beam hardening effect is modeled and incorporated into the forward-projection of the MAP to suppress beam hardening induced artifacts, and the image update process is performed by Feldkamp-Davis-Kress method based back-projection to speed up the convergence. The proposed BHC-FMAP approach does not require information about the beam spectrum or the material properties, or any additional segmentation operation. The proposed method was qualitatively and quantitatively evaluated using both phantom and animal projection data. The experimental results demonstrate that the BHC-FMAP method can efficiently provide a good correction of beam hardening induced artefacts.

Ring artifacts removal via spatial sparse representation in cone beam CT

This paper is about the ring artifacts removal method in cone beam CT. Cone beam CT images often suffer from disturbance of ring artifacts which caused by the non-uniform responses of the elements in detectors. Conventional ring artifacts removal methods focus on the correlation of the elements and the ring artifacts’ structural characteristics in either sinogram domain or cross-section image. The challenge in the conventional methods is how to distinguish the artifacts from the intrinsic structures; hence they often give rise to the blurred image results due to over processing. In this paper, we investigate the characteristics of the ring artifacts in spatial space, different from the continuous essence of 3D texture feature of the scanned objects, the ring artifacts are displayed discontinuously in spatial space, specifically along z-axis. Thus we can easily recognize the ring artifacts in spatial space than in cross-section. As a result, we choose dictionary representation for ring artifacts removal due to its high sensitivity to structural information. We verified our theory both in spatial space and coronal-section, the experimental results demonstrate that our methods can remove the artifacts efficiently while maintaining image details.

Software for automatic analysis of image and sound data simultaneously acquired from high-speed videoendocopy

High-speed digital videoendoscopy system is emerging as a new clinical tool for voice assessment. The system can acquire images of the vibrating vocal folds with simultaneous recording of voice data from the patient. The laryngeal image-based analysis has been proven valuable for objective and quantitative assessment of voice kinematics in health and disease, and meanwhile, acoustic analysis of voice data could assist in the study of phonatory characteristics and reveal useful information related to laryngeal pathophysiology. Contrast to the hardware acquisition systems, the development of effective software for handling such massive visual/sound data has lagged behind. In this paper, a software system is designed to process the laryngeal image sequences and perform image-based analyses as well as acoustic analyses. Our software contains following modules: (1) Import and view Module - to read AVI video data and sound data (wave file), edit/compile and save selected data, make image montages using DirectShow technology and display the acoustic waveform using DirectSound technology; (2) Image Process Module – to perform frame-by-frame image segmentation to delineate the glottis, to extract the GAW and bilateral vocal fold displacements; (3) Image Analysis Module – to adopt Nyquist plot displays that involves the Hilbert transform based analysis of GAW, and to provide instantaneous frequency and amplitude distributions; (4) Acoustic Analysis Module – to perform Fast Fourier Transform (FFT) and Spectrogram analyses of the imported sound data, to display the plot of the sound data and provide instantaneous frequency and amplitude distributions and Nyqiust plot and (5) Dual GAW and sound wave display module. Upon rigorous testing of this software using clinical data samples we demonstrate the applications of the software to the study of dynamic characteristics of the glottis, which may correlate with voice quality and health condition.