Signal-to-noise Ratio Calculation Research Articles

Magnetic resonance imaging of the cerebellopontine angle: comparison between constructive interference steady-state and small field-of-view technique turbo spin echo sequences.

The aim of this work was to optimize athree-dimensional (3D) turbo-spin-echo (TSE) sequence using asmall field-of-view (FOV) technique for the study of the cerebellopontine angle and to compare it with aconstructive interference steady-state (CISS) sequence. A total of 30consecutive patients underwent magnetic resonance imaging with a3Tesla (T) scanner, including 3D CISS and the optimized 3D small FOV technique turbo spin echo (3D SFT-TSE) T2-weightedsequences for the study of the cerebellopontine angle. The 3D SFT-TSE sequence was optimized after three different steps, and aquantitative evaluation of the signal-to-noise ratio (SNR) was obtained according to the National Electrical Manufacturers Association (NEMA) method. Three neuroradiologists made ablind comparative qualitative evaluation of the images between the 3D CISS and the 3D SFT-TSE obtained after the third optimization step, based on spatial resolution, contrast resolution, and presence of artifacts and noise. The calculation of SNR using the NEMA method confirmed the superiority of the third optimization step over the others. For both spatial and contrast resolution, the optimized SFT-TSE was considered better (p < 0.001) than the CISS, while image artifacts and noise were considered worse in the CISS sequence (p < 0.001). Intraobserver analysis showed that all neuroradiologists preferred the 3D SFT-TSE sequence in terms of both spatial resolution and contrast resolution and found more noise and artifact disruption in the CISS sequence. The use of the 2D radiofrequency pulse technique with a3D SFT-TSE T2 sequence was significantly more efficient than the 3D CISS sequence for the study of the cerebellopontine angle and inner ear structures.

Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

Time-efficient adaptive modulation scheme for LACO-OFDM in VLC systems

In this paper, an adaptively layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) scheme is proposed and experimentally demonstrated in the visible light communication (VLC) system. Aided by the measured signal-to-noise ratio (SNR) as channel state information (CSI), adaptive modulation at the transmitter side can balance the bit-error ratio (BER) of subcarriers, and the inter-layer interference (ISI) of LACO-OFDM is accordingly mitigated. However, the sequential demodulation of LACO-OFDM determines that the SNR calculation of subsequent layers is in high latency compared to that of the first layer. Therefore, we reuse the loading information of the first layer to relax the feedback on subsequent layers. Compared to the traditional 16-quadrature amplitude modulation (QAM) with same spectral efficiency, a 0.68dB signal-to-noise ratio (SNR) improvement can be achieved by the proposed adaptive modulation scheme at BER of 3.8x10−3 for LACO-OFDM in VLC systems.

A bridged loop gap resonator (BLGR) for small animal imaging by 1.5 T MRI systems.

A bridged loop gap resonator (BLGR) was developed as a transmit and receive coil for a mobile insert to be used for small animal proton imaging by 1.5 T MRI devices. The insert system has its own gradient system, radio frequency (RF) transmit and receive coil, and control and signal processing unit. The reflection S11 and transmission S21 parameters, quality factor (Q), sensitivity, signal to noise ratio (SNR), and maps of the static (B0) and RF (B1) magnetic flux densities were measured. The RF coil was developed starting from a loop gap resonator (LGR) for a balanced LGR and a shielded balanced LGR for a shielded bridged balanced LGR. The purpose of developing this device is to minimize the influence of the sample and surroundings on the RF coil parameters. The final design of the BLGR does not require retuning after a sample change. A 3D image of a mouse in formalin was acquired with a fast low angle shot (FLASH) MRI sequence. The SNR was calculated from one FLASH image. The signal for SNR calculation was acquired from a gadolinium-doped water sample and the noise from the air outside of the sample. This article verifies that the BLGR is viable for small animal nuclear magnetic resonance imaging at 1.5 T and is independent of sample size and material.

Simulator of a Full Fetal Electrocardiogram Measurement Chain by Multichannel Capacitive Sensing

Long-term ambulatory monitoring of the fetal heart rate (fHR) can greatly increase insight into fetal well-being and reduce pregnancy risks. Unfortunately, the existing solutions using wet or dry electrodes are unsuitable for long-term fHR monitoring due to the use of a gel or direct skin contact. Capacitive electrodes can measure an electrocardiographic (ECG) signal through clothes and, therefore, are perfectly suitable for long-term monitoring of the fHR. However, capacitive fetal ECG (fECG) measurements are challenging due to the high sensitivity of capacitive sensors to motion artifacts (MAs) and the low amplitude of the fECG. This article aims at investigating the feasibility of fECG measurements using capacitive electrodes with dedicated postprocessing algorithms for signal-to-noise ratio (SNR) improvement and MA reduction. To this end, a novel simulator of the full measurement chain is realized that generates multichannel capacitive fECG data with artificial MAs and system noise. A dedicated blind source separation (BSS) algorithm is then employed for MA removal and fECG extraction. The extracted fECG is evaluated by SNR calculation and R-peak detection. Our results show that the BSS algorithm may extract the fECG signal from noisy capacitive data. In addition, lower system noise or higher number of channels may lead to better fECG extraction. A maximum increase of 0.5 dB in the SNR and decrease of 80.7 % in the R-peak detection error are observed with increased electrode number from 8 to 20. Our findings provide useful insights for the hardware design of a capacitive fECG measurement system.

Signal-to-noise ratio assessment of muscle diffusion tensor imaging using single image set and validation by the difference image method.

Signal-to-noise ratio (SNR) assessment is essential for accurate quantification of diffusion tensor imaging (DTI) metrics and usually requires the use of a difference image method using duplicate images. We aimed to estimate the SNR of DTI of thigh muscles using a single image set without duplicate images. DTI of one thigh were acquired on a 3 T scanner from 15 healthy adults, and scans with number of signal averages (NSA) = 4 and 8 were repeatedly acquired. SNR were evaluated for six thigh muscles. For SNR calculation from a single image set, diffusion-weighted images with similar diffusion encoding directions were grouped into pairs. The difference image of each pair was high-pass filtered in k-space to yield noise images. Noise images were also calculated with a difference method using two image sets as a reference. Subjects were divided into two groups for filter optimization and validation, respectively. The coefficient of repeatability (CR) of the SNR obtained from the two methods was also evaluated separately. Bland-Altman analysis comparing the single image set method and the reference showed 95% limits of agreement of -9.2 to 9.2% for the optimization group and -12.5 to 12.6% for the validation group. The SNR measurement had a CR of 21.1% using the reference method, and 13.8% using the single image set method. The single image method can be used for DTI SNR assessment and offers better repeatability. SNR of skeletal muscle DTI can be assessed for any data set without duplicate images.

Automatic passive data selection in time domain for imaging near-surface surface waves

The passive surface wave method, as a valid supplement to the active survey method for the deeper imaging, has gained growing attentions from geophysicists and civil engineering communities, particularly in urban applications. The multi-channel analysis of passive surface waves (MAPS), as a newly developed method, combines seismic interferometry and multi-channel analysis of surface waves and shows high potential in extracting high-frequency surface-wave energy in urban environment. The unknown source-distribution of ambient noise and the complicated anthropogenic environment, however, make it difficult to obtain high-quality dispersion images of passive surface waves. We proposed a data selection technique in time domain for selective stacking of cross-correlations and applied it to improve the MAPS method. The proposed technique sorts the time segments with the defined acausal-to-causal ratio factor, and automatically detects the optimum time segments with the highest signal-to-noise ratio (SNR). Three real-world applications have demonstrated the feasibility of the proposed data selection technique. The results indicated that our method is able to preserve the time segments with coherent signals, and reject the time segments polluted by non-stationary noise sources and/or spatial aliasing. Compared with the conventional MAPS method, the improved one can broaden interested frequency band of surface waves and deblur the measured image of dispersion energy. Finally, we discussed the influence of velocity window parameter for SNR calculation on the performance of the proposed technique, and suggested that the velocity window should be appropriately wide to guarantee the accuracy of surface-wave imaging.

Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes.

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz).

Radio Frequency Modeling of Receive Coil Arrays for Magnetic Resonance Imaging

The numerical calculation of the signal-to-noise ratio (SNR) of magnetic resonance imaging (MRI) coil arrays is a powerful tool in the development of new coil arrays. The proposed method describes a complete model that allows the calculation of the absolute SNR values of arbitrary coil arrays, including receiver chain components. A new method for the SNR calculation of radio frequency receive coil arrays for MRI is presented, making use of their magnetic B 1 − transmit pattern and the S-parameters of the network. The S-parameters and B 1 − fields are extracted from an electromagnetic field solver and are post-processed using our developed model to provide absolute SNR values. The model includes a theory for describing the noise of all components in the receiver chain and the noise figure of a pre-amplifier by a simple passive two-port network. To validate the model, two- and four-element receive coil arrays are investigated. The SNR of the examined arrays is calculated and compared to measurement results using imaging of a saline water phantom in a 3 T scanner. The predicted values of the model are in good agreement with the measured values. The proposed method can be used to predict the absolute SNR for any receive coil array by calculating the transmit B 1 − pattern and the S-parameters of the network. Knowledge of the components of the receiver chain including pre-amplifiers leads to satisfactory results compared to measured values, which proves the method to be a useful tool in the development process of MRI receive coil arrays.

Signal-to-noise analysis of a birefringent spectral zooming imaging spectrometer

Study of signal-to-noise ratio (SNR) of a novel spectral zooming imaging spectrometer (SZIS) based on two identical Wollaston prisms is conducted. According to the theory of radiometry and Fourier transform spectroscopy, we deduce the theoretical equations of SNR of SZIS in spectral domain with consideration of the incident wavelength and the adjustable spectral resolution. An example calculation of SNR of SZIS is performed over 400–1000 nm. The calculation results indicate that SNR with different spectral resolutions of SZIS can be optionally selected by changing the spacing between the two identical Wollaston prisms. This will provide theoretical basis for the design, development and engineering of the developed imaging spectrometer for broad spectrum and SNR requirements.

Discrete Representation of Holograms of Halftone Objects

A comparative analysis of methods for calculating digital holograms based on discrete transformations for various types of objects is presented. Qualitative results of synthesis and reconstruction of holograms are obtained, as well as examples of the realization of some of the types of holograms considered. The quality of digital hologram recovery is estimated by calculation of signal-tonoise ratio (SNR) and root-mean-square error (RMSE).

A fast symbolic SNR computation method and its Verilog-A implementation for Sigma-Delta modulator design optimization

Sigma-Delta modulator (SDM) is a type of medium-speed but high-accuracy data converter. Signal-to-noise ratio (SNR) is one of its most critical design metrics. To enable automatic synthesis, fast SNR evaluation is of uttermost importance to accelerate the synthesis loop. In this paper a fast symbolic SNR computation method is proposed, which can automatically generate analytical SNR calculation formulas given a loop filter circuit design. Furthermore, Verilog-A implementation of this method is introduced, which demonstrates the feasibility of using the fast SNR computation method in a standard mixed-signal design automation environment. Application of the Verilog-A modules to SDM circuit optimization is demonstrated by invoking the build-in quasi-Newton optimizer in a Verilog-A tool and compared to the simulated annealing (SA) optimization. This work is an attempt of demonstration that a symbolic computation method can be integrated into a commercial mixed-signal design tool with good performance in behavioral synthesis. Utilization of the proposed method for behavioral Monte Carlo simulation in the Verilog-A environment was also found to be encouraging.

Application of NDT methods to improve the detection of underground linear objects

Identification of underground objects and rectilinear discontinuities such as tunnels, tubes etc. is quite complicated. This is due to the fact that their width is quite small compared to the cross-section of ground penetrating radar (GPR) beams. This implies low reflectivity and hence very low signal-to-noise ratio (SNR) per scan. In this paper, we propose a new method for improving the SNR of remote sensing devices such as GPR, that use electromagnetic (EM) waves for detection of underground objects. The proposed method for improving the detectability of small-width objects is based on measuring the anisotropy of soils, in a similar fashion as in non-destructive testing methods. The proposed method performs ‘orthogonal’ scans of a parcel of the tested soil, with subsequent processing of the received signals using a special algorithm. No ‘progressive scan’ is required. Mathematical expressions for calculation of SNR are derived in a general form. The performance of the proposed method is investigated by simulations of real cases, when the reflected signal from the object is ‘drowned in the noise’. Numerical simulations of the investigated cases show that SNR can be increased from 10 dB up to several 10's dB, compared to the traditional method of progressive scanning of the soil under test. It is shown that the presented version of the orthogonal scanning method can yield simultaneous improvement of both SNR and selectivity. The horizontal resolution is improved to about 1 m, in contrast to 5–6 m in conventional GPR.

Boosting the SNR by adding a receive-only endorectal monopole to an external antenna array for high-resolution, T2 -weighted imaging of early-stage cervical cancer with 7-T MRI.

The aim of this study was to investigate the signal-to-noise ratio (SNR) gain in early-stage cervical cancer at ultrahigh-field MRI (e.g. 7T) using a combination of multiple external antennas and a single endorectal antenna. In particular, we used an endorectal monopole antenna to increase the SNR in cervical magnetic resonance imaging (MRI). This should allow high-resolution, T2 -weighted imaging and magnetic resonance spectroscopy (MRS) for metabolic staging, which could facilitate the local tumor status assessment. In a prospective feasibility study, five healthy female volunteers and six patients with histologically proven stage IB1-IIB cervical cancer were scanned at 7T. We used seven external fractionated dipole antennas for transmit-receive (transceive) and an endorectally placed monopole antenna for reception only. A region of interest, containing both normal cervix and tumor tissue, was selected for the SNR measurement. Separated signal and noise measurements were obtained in the region of the cervix for each element and in the near field of the monopole antenna (radius<30mm) to calculate the SNR gain of the endorectal antenna in each patient. We obtained high-resolution, T2 -weighted images with a voxel size of 0.7×0.8×3.0mm3 . In four cases with optimal placement of the endorectal antenna (verified on the T2 -weighted images), a mean gain of 2.2 in SNR was obtained at the overall cervix and tumor tissue area. Within a radius of 30mm from the monopole antenna, a mean SNR gain of 3.7 was achieved in the four optimal cases. Overlap between the two different regions of the SNR calculations was around 24%. We have demonstrated that the use of an endorectal monopole antenna substantially increases the SNR of 7-T MRI at the cervical anatomy. Combined with the intrinsically high SNR of ultrahigh-field MRI, this gain may be employed to obtain metabolic information using MRS and to enhance spatial resolutions to assess tumor invasion.

Unobtrusive Estimation of Cardiac Contractility and Stroke Volume Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate.

Unobtrusive and inexpensive technologies for monitoring the cardiovascular health of heart failure (HF) patients outside the clinic can potentially improve their continuity of care by enabling therapies to be adjusted dynamically based on the changing needs of the patients. Specifically, cardiac contractility and stroke volume (SV) are two key aspects of cardiovascular health that change significantly for HF patients as their condition worsens, yet these parameters are typically measured only in hospital/clinical settings, or with implantable sensors. In this work, we demonstrate accurate measurement of cardiac contractility (based on pre-ejection period, PEP, timings) and SV changes in subjects using ballistocardiogram (BCG) signals detected via a high bandwidth force plate. The measurement is unobtrusive, as it simply requires the subject to stand still on the force plate while holding electrodes in the hands for simultaneous electrocardiogram (ECG) detection. Specifically, we aimed to assess whether the high bandwidth force plate can provide accuracy beyond what is achieved using modified weighing scales we have developed in prior studies, based on timing intervals, as well as signal-to-noise ratio (SNR) estimates. Our results indicate that the force plate BCG measurement provides more accurate timing information and allows for better estimation of PEP than the scale BCG (r2 = 0.85 vs. r2 = 0.81) during resting conditions. This correlation is stronger during recovery after exercise due to more significant changes in PEP (r2 = 0.92). The improvement in accuracy can be attributed to the wider bandwidth of the force plate. ∆SV (i.e., changes in stroke volume) estimations from the force plate BCG resulted in an average error percentage of 5.3% with a standard deviation of ±4.2% across all subjects. Finally, SNR calculations showed slightly better SNR in the force plate measurements among all subjects but the small difference confirmed that SNR is limited by motion artifacts rather than instrumentation.

Research on space target detection ability calculation method and spectral filtering technology in sky‐screen's photoelectric system

ABSTRACTSky‐screen is a photoelectric sensor to detection flying target information in shooting range, to improve the detection ability of sky‐screen in strong background luminance, this article establishes the sky background luminance calculation model and the detection ability calculation model of sky‐screen, researches the space targets characteristics and SNR (signal‐to‐noise ratio) calculation model, analyzes the influence factors of strong background luminance and study different spectral filtering methods to improve the detection performance, discusses the influence factor that sky background luminance change and different layouts of sky‐screen to the detection system of sky‐screen. Through the calculation and experiment analysis, the results of the theoretical calculation and experiment testing on the detection ability of calculation model of sky‐screen are consistent, and the spectral filtering technology can improve the signal‐to‐noise ratio and the space target detection ability effectively in sky‐screen. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1035–1041, 2016

Space Target Optical Characteristics and SNR Calculation Model on Sky Screen

To improve the detection performance and dynamic target coordinate testing accuracy in sky-screen measurement system, this paper proposes the out-of-focus technology to design sky-screen’s optical detection system, studies the target’s spatial coordinates calculation method, and sets up the signal-to-noise ratio (SNR) calculation model of sky screen. According to the illumination distribution function of target’s imaging facula, the background illumination and the photosensitive surface size of the photoelectric detection receiver, the output voltage calculation function, and target optical characteristics calculation function were derived, the change regulation of the output voltage in different background illuminations was analyzed, and the relation of SNR and the target’s imaging facula size in sky-screen optical detection system was discussed. Through calculation and experiment, the results find the SNR of new sky screen has increased by nearly 1.4 times, it show the space target optical characteristics function, and the SNR calculation model of sky-screen is correct and feasible.

An Effective Approach of Noise Analysis on Images

Here it is represented an image analysis technique using both noising & de-noising process. By taking a simple image of different formats we added a noise i.e. Gaussian noise to the particular image and then the calculation of SNR(SIGNALTO-NOISE RATIO) and PSNR(PEAK SIGNAL-TO-NOISE RATIO) is performed based on the image formats such as jpeg, png, etc. Although there are various types of noise but we considered here the Gaussian noise only. The calculation done is also according to the different functions (blocking, nlfilter, etc.) and the related categories applied on the simple images. After then the image de-noising is performed on the obtained noised image and the image without any applied function in order to check the comparative analysis of the image containing noise and the de-noised image. The image observed is not as an exact replica of the previous image without noise. Also, there is a vast difference between the SNR and PSNR values of the noised and de-noised image. An image transferred by the sender, if get some distortion like Gaussian noise, then after de-noising process the image observed on the receiver side may be not real and exact, but have less distortion which is a great advantage. General Terms Noise Analysis, Comparison of image properties like psnr and snr, comparative analysis of different image formats.

Material-specific transfer function model and SNR in CT

This study presents an analytical model for the edge spread function (ESF) of a clinical CT system that allows reliable fits of noisy ESF data. The model was used for the calculation of the material-specific transfer function TF and an estimation of the signal transfer and the signal-to-noise ratio (SNR) in 2D. Images of the Catphan phantom were acquired with a clinical Siemens Somatom Sensation Cardiac 64 CT scanner combining four different x-ray tube outputs (40, 150, 250 and 350 mAs) with four different reconstruction filters, which covered the range from very smooth (B10s) to very sharp (B70s). The images of the high- and mid-contrast cylinders of the phantom’s ‘Geometry and Sensitometry’ module (air, Teflon, Delrin and PMP) were used to sample material-specific ESF curves. The ESF curves were fitted with the analytical model we developed based on a linear combination of Boltzmann and Gaussian functions. The analytical model of the ESF was used to obtain the Fourier-based material-specific transfer function TF, as well as the spatial-domain point spread function (PSF). TF was subsequently used to estimate the signal transfer, which was compared to the actual reconstructed image of a 3.0 mm diameter Teflon pin. The noise power spectrum (NPS) was calculated from images of a uniform water phantom under the same technique parameters. The task-specific SNR was calculated for all technique parameters from the model-based TF, the measured NPS and simulated 3 mm diameter disc signals modeling the aforementioned materials. Bootstrapping was performed to estimate the standard deviation of the TF and the SNR. The analytical model we developed accurately captured the features of the CT ESF data. The coefficient of determination R2, a metric that describes the goodness of the fit, had a median value of 0.9995, and decreased for low tube output, low contrast and the sharp reconstruction filter. Our analysis showed that ESF, PSF and TF depended not only on the reconstruction filter, but also on the tube output and the material of the cylinders. For B40s and B70s, the TF of Delrin was significantly higher than the TF of other materials in the frequency range of 0.4–0.9 mm−1. The estimated signal transfer agreed well with the actual reconstructed image of the Teflon pin. For the technique parameters we used the SNR values ranged between [64, 320], [64, 281], [37, 137] and [33, 117] for air, Teflon, Delrin and PMP respectively. While for high-contrast materials the smoothest reconstruction filter resulted in the highest SNR, for mid-contrast materials the standard filter gave the best results. The presented approach provides an accurate, analytical description of the material-specific ESF, PSF and TF as well as an estimate of the signal transfer. The transfer function TF together with the NPS and simulated signals allow the calculation of a task-specific SNR.