A dedicated small-animal x-ray micro computed tomography (micro-CT) system has been developed to screen laboratory small animals such as mice and rats. The micro-CT system consists of an indirect-detection flat-panel x-ray detector with a field-of-view of 120 x 120 mm2, a microfocus x-ray source, a rotational subject holder and a parallel data processing system. The flat-panel detector is based on a matrix-addressed photodiode array fabricated by a CMOS (complementary metal-oxide semiconductor) process coupled to a CsI:T1 (thallium-doped caesium iodide) scintillator as an x-ray-to-light converter. Principal imaging performances of the micro-CT system have been evaluated in terms of image uniformity, voxel noise and spatial resolution. It has been found that the image non-uniformity mainly comes from the structural non-uniform sensitivity pattern of the flat-panel detector and the voxel noise is about 48 CT numbers at the voxel size of 100 x 100 x 200 microm3 and the air kerma of 286 mGy. When the magnification ratio is 2, the spatial resolution of the micro-CT system is about 14 1p/mm (line pairs per millimetre) that is almost determined by the flat-panel detector showing about 7 1p/mm resolving power. Through low-contrast phantom imaging studies, the minimum resolvable contrast has been found to be less than 36 CT numbers at the air kerma of 95 mGy. Some laboratory rat imaging results are presented.

Magnetic resonance electrical impedance tomography (MREIT) is a recently developed imaging technique that combines MRI and electrical impedance tomography (EIT). In MREIT, cross-sectional electrical conductivity images are reconstructed from the internal magnetic field density data produced inside an electrically conducting object when an electrical current is injected into the object. In this work we present the results of electrical conductivity imaging experiments, and performance evaluations of MREIT in terms of noise characteristics and spatial resolution. The MREIT experiment was performed with a 3.0 Tesla MRI system on a phantom with an inhomogeneous conductivity distribution. We reconstructed the conductivity images in a 128 × 128 matrix format by applying the harmonic B_z algorithm to the z-component of the internal magnetic field density data. Since the harmonic B_z algorithm uses only a single component of the internal magnetic field data, it was not necessary to rotate the object in the MRI scan. The root mean squared (RMS) errors of the reconstructed images were between 11% and 35% when the injection current was 24 mA. Magn Reson Med 51:1292–1296, 2004.

We introduce an efficient ring artifact correction method for a cone-beam computed tomography (CT). In the first step, we correct the defective pixels whose values are close to zero or saturated in the projection domain. In the second step, we compute the mean value at each detector element along the view angle in the sinogram to obtain the one-dimensional (1D) mean vector, and we then compute the 1D correction vector by taking inverse of the mean vector. We multiply the correction vector with the sinogram row by row over all view angles. In the third step, we apply a Gaussian filter on the difference image between the original CT image and the corrected CT image obtained in the previous step. The filtered difference image is added to the corrected CT image to compensate the possible contrast anomaly that may appear due to the contrast change in the sinogram after removing stripe artifacts. We applied the proposed method to the projection data acquired by two flat-panel detectors (FPDs) and a silicon-based photon-counting X-ray detector (PCXD). Micro-CT imaging experiments of phantoms and a small animal have shown that the proposed method can greatly reduce ring artifacts regardless of detector types. Despite the great reduction of ring artifacts, the proposed method does not compromise the original spatial resolution and contrast.

Background: Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most serious cardiac arrhythmias that require quick and accurate detection to save lives. Automated external defibrillators (AEDs) have been developed to recognize these severe cardiac arrhythmias using complex algorithms inside it and determine if an electric shock should in fact be delivered to reset the cardiac rhythm and restore spontaneous circulation. Improving AED safety and efficacy by devising new algorithms which can more accurately distinguish shockable from non-shockable rhythms is a requirement of the present-day because of their uses in public places. Method: In this paper, we propose a sequential detection algorithm to separate these severe cardiac pathologies from other arrhythmias based on the mean absolute value of the signal, certain low-order intrinsic mode functions (IMFs) of the Empirical Mode Decomposition (EMD) analysis of the signal and a heart rate determination technique. First, we propose a direct waveform quantification based approach to separate VT plus VF from other arrhythmias. The quantification of the electrocardiographic waveforms is made by calculating the mean absolute value of the signal, called the mean signal strength. Then we use the IMFs, which have higher degree of similarity with the VF in comparison to VT, to separate VF from VTVF signals. At the last stage, a simple rate determination technique is used to calculate the heart rate of VT signals and the amplitude of the VF signals is measured to separate the coarse VF from VF. After these three stages of sequential detection procedure, we recognize the two components of shockable rhythms separately. Results: The efficacy of the proposed algorithm has been verified and compared with other existing algorithms, e.g., HILB [1], PSR [2], SPEC [3], TCI [4], Count [5], using the MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database. Four quality parameters (e.g., sensitivity, specificity, positive predictivity, and accuracy) were calculated to ascertain the quality of the proposed and other comparing algorithms. Comparative results have been presented on the identification of VTVF, VF and shockable rhythms (VF + VT above 180 bpm). Conclusions: The results show significantly improved performance of the proposed EMD-based novel method as compared to other reported techniques in detecting the life threatening cardiac arrhythmias from a set of large databases.