Lower Normalized Mean Square Error Research Articles

Two novel memory polynomial models for modeling of RF power amplifiers

Two novel memory polynomial models are derived based on physical knowledge of a general power amplifier (PA). The derivations are given in detail to facilitate derivations of other model structures. The model error in terms of normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR) of the novel model structures are compared to that of established models based on the number of parameters using data measured on two different amplifiers, one high-power base-station PA and one low-power general purpose amplifier. The novel models show both lower NMSE and ACEPR for any chosen number of parameters compared to the established models. The low model errors make the novel models suitable candidates for both modeling and digital predistortion.

Optimization of iterative reconstruction parameters with attenuation correction, scatter correction and resolution recovery in myocardial perfusion SPECT/CT

The aim of this study was to characterize the optimal reconstruction parameters for ordered-subset expectation maximization (OSEM) with attenuation correction, scatter correction, and depth-dependent resolution recovery (OSEMACSCRR). We assessed the optimal parameters for OSEMACSCRR in an anthropomorphic torso phantom study, and evaluated the validity of the reconstruction parameters in the groups of normal volunteers and patients with abnormal perfusion. Images of the anthropomorphic torso phantom, 9 normal volunteers and 7 patients undergoing myocardial perfusion SPECT were acquired with a SPECT/CT scanner. SPECT data comprised a 64×64 matrix with an acquisition pixel size of 6.6 mm. A normalized mean square error (NMSE) of the phantom image was calculated to determine both optimal OSEM update and a full width at half maximum (FWHM) of Gaussian filter. We validated the myocardial count, contrast and noise characteristic for clinical subjects derived from OSEMACSCRR processing. OSEM with depth-dependent resolution recovery (OSEMRR) and filtered back projection (FBP) were simultaneously performed to compare OSEMACSCRR. The combination of OSEMACSCRR with 90-120 OSEM updates and Gaussian filter with 13.2-14.85 mm FWHM yielded low NMSE value in the phantom study. When we used OSEMACSCRR with 120 updates and Gaussian filter with 13.2 mm FWHM in the normal volunteers, myocardial contrast showed significantly higher value than that derived from 120 updates and 14.85 mm FWHM. OSEMACSCRR with the combination of 90-120 OSEM updates and 14.85 mm FWHM produced lowest % root mean square (RMS) noise. Regarding the defect contrast of patients with abnormal perfusion, OSEMACSCRR with the combination of 90-120 OSEM updates and 13.2 mm FWHM produced significantly higher value than that derived from 90-120 OSEM updates and 14.85 mm FWHM. OSEMACSCRR was superior to FBP for the % RMS noise (8.52±1.08 vs. 9.55±1.71, p=0.02) and defect contrast (0.368±0.061 vs. 0.327±0.052, p=0.01), respectively. Clinically optimized the number of OSEM updates and FWHM of Gaussian filter were (1) 120 updates and 13.2 mm, and (2) 90-120 updates and 14.85 mm on the OSEMACSCRR processing, respectively. Further assessment may be required to determine the optimal iterative reconstruction parameters in a larger patient population.

Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm

The robust algorithm OPED for the reconstruction of images from Radon data has been recently developed. This reconstructs an image from parallel data within a special scanning geometry that does not need rebinning but only a simple re-ordering, so that the acquired fan data can be used directly for the reconstruction. However, if the number of rays per fan view is increased, there appear empty cells in the sinogram. These cells need to be filled by interpolation before the reconstruction can be carried out. The present paper analyzes linear interpolation, cubic splines and parametric (or “damped”) splines for the interpolation task. The reconstruction accuracy in the resulting images was measured by the Normalized Mean Square Error (NMSE), the Hilbert Angle, and the Mean Relative Error. The spatial resolution was measured by the Modulation Transfer Function (MTF). Cubic splines were confirmed to be the most recommendable method. The reconstructed images resulting from cubic spline interpolation show a significantly lower NMSE than the ones from linear interpolation and have the largest MTF for all frequencies. Parametric splines proved to be advantageous only for small sinograms (below 50 fan views).