George Kamucha

High Resolution Hip-joint Socket Imaging Using Pulsed Laser Radar

In this paper, we present a 3D imaging technique for computer-assisted hip-joint replacement surgery. It is based on a high resolution laser radar which uses powerful picosecond laser pulses, generated from low-cost single-heterostructure (SH) semiconductor laser diodes, and a highly sensitive avalanche photodiode. Although the pulse repetition frequency (PRF) is relatively high (20 kHz), class 1 eye-safety laser conditions are fulfilled. A socket bone from a cow was imaged using our laser radar system. The extracted 3D data was found to be within 0.5 mm in relation to the bony surface constructed from computed tomography (CT) data of the same bone.

A fast procedure for acquisition and reconstruction of magnetic resonance images using compressive sampling

This paper proposes a fast and robust procedure for sensing and reconstruction of sparse or compressible magnetic resonance images based on the compressive sampling theory. The algorithm starts with incoherent undersampling of the k-space data of the image using a random matrix. The undersampled data is sparsified using Haar transformation. The Haar transform coefficients of the k-space data are then reconstructed using the orthogonal matching Pursuit algorithm. The reconstructed coefficients are inverse transformed into k-space data and then into the image in spatial domain. Finally, a median filter is used to suppress the recovery noise artifacts. Experimental results show that the proposed procedure greatly reduces the image data acquisition time without significantly reducing the image quality. The results also show that the error in the reconstructed image is reduced by median filtering.

A robust magnetic resonance imaging method based on compressive sampling and clustering of sparsifying coefficients

This paper presents a novel and robust method for medical Magnetic Resonance Imaging (MRI). The proposed method utilizes the sparsity as well as clustering of the image coefficients in the wavelet transform sparsifying domain. The method shows better immunity to reconstruction noise than other Compressive Sampling (CS) based techniques. The algorithm starts with undersampling of the k-space data of the image using a random matrix followed by reconstruction of the Haar transform coefficients of the k-space data using the Orthogonal Matching Pursuit (OMP) algorithm. The transform coefficients are then modulated by a raised-cosine shaping vector that suppresses noisy artifacts in the coefficients to restore the clustering. The shaped coefficients are then transformed into k-space data. The k-space data is finally transformed into the image in spatial domain. Experimental results show that the proposed procedure gives better results than other conventional methods in terms of terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).

A hybrid MRI method based on denoised compressive sampling and detection of dominant coefficients

In this paper, a hybrid method for acquisition and reconstruction of sparse magnetic resonance images is presented. The method uses conventional spin echo Magnetic Resonance Imaging (MRI) with only a few Phase-encoding steps to obtain the dominant k-space data coefficients. The rest of the k-space data coefficients are estimated using Compressive Sampling (CS). The compressive sampling part of the algorithm uses a random matrix to sample the vectorized k-space data of the image at a sub-Nyquist rate followed by reconstruction of the Discrete Wavelet Transform (DWT) coefficients of the k-space data using Orthogonal Matching Pursuit (OMP). The DWT coefficients are then transformed into the Discrete Fourier Transform (DFT) domain and denoised prior to combination with the dominant DFT coefficients obtained using conventional MRI to yield the whole k-space of the reconstructed image. The reconstructed k-space data is finally transformed into the reconstructed image using inverse DFT. Computer simulation results show that the proposed procedure yields better results than other conventional CS-MRI methods in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) index.

Effect of OFDM signal structure and subcarrier modulation on the reduction of the signal peak power

The problem of high ratio of peak-to-average power experienced in orthogonal-frequency-division-multiplexed signals remains a major challenge when it comes to practical realization of transmitters for such signals. This is mainly due to the requirement on high power amplifiers to operate with large input power back-offs and thus at undesirable low power efficiency region in order to avoid clipping of such signals and the subsequent degradation of bit error rate and spectral interference to adjacent channels. In this paper, an efficient peak power reducing technique is first proposed, its performance verified, and then used to investigate the influence of OFDM signal structure and subcarrier modulation on the peak power reduction capability of peak power reduction techniques. The proposed technique involves carefully selecting some subcarrier signals that when added to information-modulated subcarriers yield a combined signal with an acceptable lower ratio of peak-to-average power. This technique is based on the application of second order cone convex programming to the minimization of maximum of norms. Simulation results show that the proposed technique reduces peak-to-average power ratio (PAPR) by 4.5 dB and 7.8 dB while utilizing 5% and 20% of subcarriers for peak power reduction. Real OFDM signals are found to exhibit a PAPR that is higher than that of complex signals by 2.4 dB. In addition, the OFDM signal structure and the type of subcarrier modulation are found to have minimal impact on the peak power reduction capability since the variation in PAPR reductions for the modulation schemes used in the research is within ±0.75 dB, and the difference in PAPR reduction for both real and complex signals is on average within ±0.22 dB.