Qi Duan

A review of imaging techniques for systems biology

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

Real-time segmentation by Active Geometric Functions

Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

Dynamic Cardiac Information From Optical Flow Using Four Dimensional Ultrasound

Quantitative analysis of cardiac motion is of great clinical interest in assessing ventricular function. Real-time 3-D (RT3D) ultrasound transducers provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analysis requires segmentation and visual tracking of the left ventricular endocardial border. We present results based on correlation of four-dimensional optical flow motion for temporal tracking of ventricular borders in three dimensional ultrasound data. A displacement field is computed from the optical flow output, and a framework for the computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with physiological knowledge as well as experimental results

Tracking of LV endocardial surface on real-time three-dimensional ultrasound with optical flow

Matrix-phased array transducers for real-time three-dimensional ultrasound enable fast, non-invasive visualization of cardiac ventricles. Segmentation of 3D ultrasound is typically performed at end diastole and end systole with challenges for automation of the process and propagation of segmentation in time. In this context, given the position of the endocardial surface at certain instants in the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of volume ultrasound data. In this paper, we applied optical flow to track the endocardial surface between frames of reference, segmented via manual tracing or manual editing of the output from a deformable model. To evaluate optical-flow tracking of the endocardium, quantitative comparison of ventricular geometry and dynamic cardiac function are reported on two open-chest dog data sets and a clinical data set. Results showed excellent agreement between optical flow tracking and segmented surfaces at reference frames, suggesting that optical flow can provide dynamic “interpolation” of a segmented endocardial surface.

Assessment of visual quality and spatial accuracy of fast anisotropic diffusion and scan conversion algorithms for real-time three-dimensional spherical ultrasound

Three-dimensional ultrasound machines based on matrix phased-array transducers are gaining predominance for real-time dynamic screening in cardiac and obstetric practice. These transducers array acquire three-dimensional data in spherical coordinates along lines tiled in azimuth and elevation angles at incremental depth. This study aims at evaluating fast filtering and scan conversion algorithms applied in the spherical domain prior to visualization into Cartesian coordinates for visual quality and spatial measurement accuracy. Fast 3d scan conversion algorithms were implemented and with different order interpolation kernels. Downsizing and smoothing of sampling artifacts were integrated in the scan conversion process. In addition, a denoising scheme for spherical coordinate data with 3d anisotropic diffusion was implemented and applied prior to scan conversion to improve image quality. Reconstruction results under different parameter settings, such as different interpolation kernels, scaling factor, smoothing options, and denoising, are reported. Image quality was evaluated on several data sets via visual inspections and measurements of cylinder objects dimensions. Error measurements of the cylinders radius, reported in this paper, show that the proposed fast scan conversion algorithm can correctly reconstruct three-dimensional ultrasound in Cartesian coordinates under tuned parameter settings. Denoising via three-dimensional anisotropic diffusion was able to greatly improve the quality of resampled data without affecting the accuracy of spatial information after the modification of the introduction of a variable gradient threshold parameter.

Comparing optical-flow based methods for quantification of myocardial deformations on RT3D ultrasound

Quantitative analysis of cardiac motion is of great clinical interest in assessing ventricular function. Real-time 3-D (RT3D) ultrasound transducers provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analysis requires segmentation and visual tracking of the left ventricular endocardial border. Previously, we presented a method based on four-dimensional optical flow motion estimation for temporal tracking of ventricular borders in RT3D ultrasound. A myocardial displacement field and dynamic cardiac metrics were computed by interpolating the boundary tracking results. In this paper, we propose three additional methods for deriving dynamic cardiac information from tracking ventricular surfaces and demonstrate these methods on a clinical dataset

Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography

Adaptive filtering of temporally varying X-ray image sequences acquired during endovascular interventions can improve the visual tracking of catheters by radiologists. Existing techniques blur the important parts of image sequences, such as catheter tips, anatomical structures and organs; and they may introduce trailing artifacts. To address this concern, an adaptive filtering process is presented to apply temporal filtering in regions without motion and spatial filtering in regions with motion. The adaptive filtering process is a multi-step procedure. First a normalized motion mask that describes the differences between two successive frames is generated. Secondly each frame is spatially filtered using the specific motion mask to specify different types of filtering in each region. Third an IIR filter is then used to combine the spatially filtered image with the previous output image; the motion mask thus serves as a weighted input mask to determine how much spatial and temporal filtering should be applied. This method results in improving both the stationary and moving fields. The visibility of static anatomical structures and organs increases, while the motion of the catheter tip and motion of anatomical structures and organs remain unblurred and visible during interventional procedures.

Surface Function Actives

Deformable models have been widely used in image segmentation since the introduction of the snakes. Later the introduction of level set frameworks to solve the energy minimization problem associated with the deformable model overcame some limitations of the parametric active contours with respect to topological changes by embedding surface representations into higher dimensional functions. However, this may also bring in more computational load so that recent advances in spatio-temporal resolutions of 3D/4D imaging raised some challenges for real-time segmentation, especially for interventional imaging. In this context, a novel segmentation framework, Surface Function Actives (SFA), is proposed for real-time segmentation purpose. SFA has great advantages in terms of potential efficiency, based on its dimensionality reduction for the surface representation. Utilizing implicit representations with variational framework also provides flexibility and benefits currently shared by level set frameworks. An application for minimally-invasive intervention is shown to illustrate the potential applications of this framework.

Quantitative validation of optical flow based myocardial strain measures using sonomicrometry

Dynamic cardiac metrics, including myocardial strains and displacements, provide a quantitative approach to evaluate cardiac function. However, in current clinical diagnosis, largely 2D strain measures are used despite that cardiac motions are complex 3D volumes over time. Recent advances in 4D ultrasound enable the capability to capture such complex motion in a single image data set. In our previous work, a 4D optical flow based motion tracking algorithm was developed to extract full 4D dynamic cardiac metrics from such 4D ultrasound data. In order to quantitatively evaluate this tracking method, in-vivo coronary artery occlusion experiments at various locations were performed on three canine hearts. Each dog was screened with 4D ultrasound and sonomicrometry data was acquired during each occlusion study. The 4D ultrasound data from these experiments was then analyzed with the tracking method and estimated principal strain measures were directly compared to those recorded by sonomicrometry. Strong agreement was observed independently for the three canine hearts. This is the first validation study of optical flow based strain estimation for 4D ultrasound with a direct comparison with sonomicrometry using in-vivo data.

Coronary Occlusion Detection with 4D Optical Flow Based Strain Estimation on 4D Ultrasound

Real-time three-dimensional echocardiography (RT3DE) offers an efficient way to obtain complete 3D images of the heart over an entire cardiac cycle in just a few seconds. The complex 3D wall motion and temporal information contained in these 4D data sequences has the potential to enhance and supplement other imaging modalities for clinical diagnoses based on cardiac motion analysis. In our previous work, a 4D optical flow based method was developed to estimate dynamic cardiac metrics, including strains anddisplacements, from 4D ultrasound. In this study, in order to evaluate the ability of our method in detecting ischemic regions, coronary artery occlusion experiments at various locations were performed on five dogs. 4D ultrasound data acquired during these experiments were analyzed with our proposed method. Ischemic regions predicted by the outcome of strain measurements were compared to predictions from cardiac physiology with strong agreement. This is the first direct validation study of our image analysis method for biomechanical prediction and in vivo experimental outcome.