A Fast CT Reconstruction Scheme for a General Multi-Core PC

Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors

Controlled Cardiac Computed Tomography

Cardiac computed tomography (CT) has been a hot topic for years because of the clinical importance of cardiac diseases and the rapid evolution of CT systems. In this paper, we propose a novel strategy for controlled cardiac CT that may effectively reduce image artifacts due to cardiac and respiratory motions. Our approach is radically different from existing ones and is based on controlling the X-ray source rotation velocity and powering status in reference to the cardiac motion. We theoretically show that by such a control-based intervention the data acquisition process can be optimized for cardiac CT in the cases of periodic and quasiperiodic cardiac motions. Specifically, we formulate the corresponding coordination/control schemes for either exact or approximate matches between the ideal and actual source positions, and report representative simulation results that support our analytic findings

MicroRNA100 Inhibits Self-Renewal of Breast Cancer Stem–like Cells and Breast Tumor Development

miRNAs are essential for self-renewal and differentiation of normal and malignant stem cells by regulating the expression of key stem cell regulatory genes. Here, we report evidence implicating the miR100 in self-renewal of cancer stem-like cells (CSC). We found that miR100 expression levels relate to the cellular differentiation state, with lowest expression in cells displaying stem cell markers. Utilizing a tetracycline-inducible lentivirus to elevate expression of miR100 in human cells, we found that increasing miR100 levels decreased the production of breast CSCs. This effect was correlated with an inhibition of cancer cell proliferation in vitro and in mouse tumor xenografts due to attenuated expression of the CSC regulatory genes SMARCA5, SMARCD1, and BMPR2. Furthermore, miR100 induction in breast CSCs immediately upon their orthotopic implantation or intracardiac injection completely blocked tumor growth and metastasis formation. Clinically, we observed a significant association between miR100 expression in breast cancer specimens and patient survival. Our results suggest that miR100 is required to direct CSC self-renewal and differentiation

Blind System Identification and Channel Equalization of IIR Systems without Statistical Information

A novel approach is proposed to blindly identify an unknown IIR system. The approach is based on faster sampling at the system output and requires neither a priori statistical information on the unknown input nor training signals. The methods presented are linear in the parameters of the unknown system so that many standard recursive algorithms can be readily applied. It is also shown in the paper that under a generic condition, any finite order IIR system is identifiable provided the over-sampling ratio is appropriately chosen. EDICS Number: S.P. 2.5.4 Keywords: Blind system identification, equalization, system identification, oversampling, wireless communications, multirate systems. This work was supported in part by grants of NSF(USA) 1 and ARC(Australia) 2 . 1 Introduction The blind system identification (BSI) and blind channel equalization (BCE) problems addressed in this paper can be formulated as follows: A sequence of input signal u[kh i ] is transmitted at sampling rate..

Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method

Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm

An automated treatment plan alert system to safeguard cancer treatments in radiation therapy

In radiation oncology, the intricate process of delivering radiation to a patient is detailed by the patient’s treatment plan, which is data describing the geometry, construction and strength of the radiation machine and the radiation beam it emits. The patient’s life depends upon the accuracy of the treatment plan, which is left in the hands of the vendor-specific software automatically generating the plan after an initial patient consultation and planning with a medical professional. However, corrupted and erroneous treatment plan data have previously resulted in severe patient harm when errors go undetected and radiation proceeds. The aim of this paper is to develop an automatic error-checking system to prevent the accidental delivery of radiation treatment to an area of the human body (i.e., the treatment site) that differs from the plan’s documented intended site. To this end, we develop a method for structuring treatment plan data in order to feed machine-learning (ML) classifiers and predict a plan’s treatment site. In practice, a warning may be raised if the prediction disagrees with the documented intended site.The contribution of this paper is in the strategic structuring of the complex, intricate, and nonuniform data of modern treatment planning and from multiple vendors in order to easily train ML algorithms. A three-step process utilizing up- and down-sampling and dimension reduction, the method we develop in this paper reduces the thousands of parameters comprising a single treatment plan to a single two-dimensional heat map that is independent of the specific vendor or construction of the machine used for treatment. Our heat-map structure lends itself well to feed well-established ML algorithms, and we train–test random forest, softmax, k-nearest neighbors, shallow neural network, and support vector machine using real clinical treatment plans from several hospitals in the United States. The paper demonstrates that the proposed method characterizes treatment sites so well that ML classifiers may predict head-neck, breast, and prostate treatment sites with an accuracy of about 94%. The proposed method is the first step towards a thorough, fully automated error-checking system in radiation therapy