Direct strain estimation in elastography using spectral cross-correlation

Spectral estimation of tissue strain has been performed previously by using the centroid shift of the power spectrum or by estimating the variation in the mean scatterer spacing in the spectral domain. The centroid shift method illustrates the robustness of the direct, incoherent strain estimator. In this paper, we present a strain estimator that uses spectral cross-correlation of the pre- and postcompression power spectrum. The centroid shift estimator estimates strain from the mean center frequency shift, while the spectral cross-correlation estimates the shift over the entire spectrum. Spectral cross-correlation is shown to be more sensitive to small shifts in the power spectrum and, thus, provides better estimation for smaller strains when compared to the spectral centroid shift. Spectral cross-correlation shares all the advantages gained using the spectral centroid shift, in addition to providing accurate and precise strain estimation for small strains. The variance and noise properties of the spectral strain estimators quantified by their respective strain filters are also presented.     

Novel Flow Quantification of the Carotid Bulb and the Common Carotid Artery with Vector Flow Ultrasound

Abnormal blood flow is usually assessed using spectral Doppler estimation of the peak systolic velocity. The technique, however, only estimates the axial velocity component, and therefore the complexity of blood flow remains hidden in conventional ultrasound examinations. With the vector ultrasound technique transverse oscillation the blood velocities of both the axial and the transverse directions are obtained and the complexity of blood flow can be visualized. The aim of the study was to determine the technical performance and interpretation of vector concentration as a tool for estimation of flow complexity. A secondary aim was to establish accuracy parameters to detect flow changes/patterns in the common carotid artery (CCA) and the carotid bulb (CB). The right carotid bifurcation including the CCA and CB of eight healthy volunteers were scanned in a longitudinal plane with vector flow ultrasound (US) using a commercial vector flow ultrasound scanner (ProFocus, BK Medical, Denmark) with a linear 5 MHz transducer transverse oscillation vector flow software. CCA and CB areas were marked in one cardiac cycle from each volunteer. The complex flow was assessed by medical expert evaluation and by vector concentration calculation. A vortex with complex flow was found in all carotid bulbs, whereas the CCA had mainly laminar flow. The medical experts evaluated the flow to be mainly laminar in the CCA (0.82 ± 0.14) and mainly complex (0.23 ± 0.22) in the CB. Likewise, the estimated vector concentrations in CCA (0.96 ± 0.16) indicated mainly laminar flow and in CB (0.83 ± 0.07) indicated mainly turbulence. Both methods were thus able to clearly distinguish the flow patterns of CCA and CB in systole. Vector concentration from angle-independent vector velocity estimates is a quantitative index, which is simple to calculate and can differentiate between laminar and complex flow.     

Wavelet-generalized least squares: a new BLU estimator of linear regression models with 1/f errors.

Long-memory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, short-memory errors but may be inefficient and/or insufficient to secure type 1 error control in the context of fractal or scale invariant noise with a more slowly decaying autocorrelation function. Here we introduce a novel method, called wavelet-generalized least squares (WLS), which is (to a good approximation) the best linear unbiased (BLU) estimator of regression model parameters in the context of long-memory errors. The method also provides maximum likelihood (ML) estimates of the Hurst exponent (which can be readily translated to the fractal dimension or spectral exponent) characterizing the correlational structure of the errors, and the error variance. The algorithm exploits the whitening or Karhunen-Loéve-type property of the discrete wavelet transform to diagonalize the covariance matrix of the errors generated by an iterative fitting procedure after both data and design matrix have been transformed to the wavelet domain. Properties of this estimator, including its Cramèr-Rao bounds, are derived theoretically and compared to its empirical performance on a range of simulated data. Compared to ordinary least squares and ARMA-based estimators, WLS is shown to be more efficient and to give excellent type 1 error control. The method is also applied to some real (neurophysiological) data acquired by functional magnetic resonance imaging (fMRI) of the human brain. We conclude that wavelet-generalized least squares may be a generally useful estimator of regression models in data complicated by long-memory or fractal noise.     

Evaluation of techniques for estimating the power spectral density of RR-intervals under paced respiration conditions

Heart rate variability (HRV) analysis is increasingly used in anaesthesia and intensive care monitoring of spontaneous breathing and mechanical ventilated patients. In the frequency domain, different estimation methods of the power spectral density (PSD) of RR-intervals lead to different results. Therefore, we investigated the PSD estimates of fast Fourier transform (FFT), autoregressive modeling (AR) and Lomb–Scargle periodogram (LSP) for 25 young healthy subjects subjected to metronomic breathing. The optimum method for determination of HRV spectral parameters under paced respiration was identified by evaluating the relative error (RE) and the root mean square relative error (RMSRE) for each breathing frequency (BF) and spectral estimation method. Additionally, the sympathovagal balance was investigated by calculating the low frequency/high frequency (LF/HF) ratio. Above 7 breaths per minute, all methods showed a significant increase in LF/HF ratio with increasing BF. On average, the RMSRE of FFT was lower than for LSP and AR. Therefore, under paced respiration conditions, estimating RR-interval PSD using FFT is recommend.     

In Vivo Lateral Blood Flow Velocity Measurement Using Speckle Size Estimation

In previous studies, we proposed blood measurement using speckle size estimation, which estimates the lateral component of blood flow within a single image frame based on the observation that the speckle pattern corresponding to blood reflectors (typically red blood cells) stretches (i.e., is “smeared”) if blood flow is in the same direction as the electronically controlled transducer line selection in a 2-D image. In this observational study, the clinical viability of ultrasound blood flow velocity measurement using speckle size estimation was investigated and compared with that of conventional spectral Doppler of carotid artery blood flow data collected from human patients in vivo. Ten patients (six male, four female) were recruited. Right carotid artery blood flow data were collected in an interleaved fashion (alternating Doppler and B-mode A-lines) with an Antares Ultrasound Imaging System and transferred to a PC via the Axius Ultrasound Research Interface. The scanning velocity was 77 cm/s, and a 4-s interval of flow data were collected from each subject to cover three to five complete cardiac cycles. Conventional spectral Doppler data were collected simultaneously to compare with estimates made by speckle size estimation. The results indicate that the peak systolic velocities measured with the two methods are comparable (within ±10%) if the scan velocity is greater than or equal to the flow velocity. When scan velocity is slower than peak systolic velocity, the speckle stretch method asymptotes to the scan velocity. Thus, the speckle stretch method is able to accurately measure pure lateral flow, which conventional Doppler cannot do. In addition, an initial comparison of the speckle size estimation and color Doppler methods with respect to computational complexity and data acquisition time indicated potential time savings in blood flow velocity estimation using speckle size estimation. Further studies are needed for calculation of the speckle stretch method across a field of view and combination with an appropriate axial flow estimator.     

Stability of benzylpenicillin for continuous intravenous infusions: An isotonic formulation for therapeutic use and a low-dose formulation for clinical trial

IntroductionThe objectives of this study were to develop a stability-indicating high performance liquid chromatography (HPLC) assay for benzylpenicillin (BPC) in pharmaceutical fluids, and to investigate the stability of (i) isotonic citrate-buffered BPC solutions at the clinically relevant concentration of 30 mg/mL, and (ii) low concentration citrate-buffered BPC intravenous infusions (5–30 μg/mL).MethodsThe stability of isotonic BPC solutions containing 3.4 or 7.2 mg/mL sodium citrate was compared against contemporary hypertonic solutions. The HPLC assay was shown to be stability-indicating following acidic, alkali, oxidative and elevated temperature stress testing.ResultsAfter 7 d storage at 4 °C and 24 h at 35 °C, the concentrations of isotonic BPC 30 mg/mL solutions containing 3.4 and 7.2 mg/mL sodium citrate were 96% and 95% respectively, compared to day 0. After 3 d at 4 °C and 24 h at room temperature (22 °C), the concentrations of isotonic BPC solutions with 3.4 and 7.2 mg/mL sodium citrate were 99% and 96% respectively, compared to day 0. These data were comparable to the hypertonic solutions and meet pharmacopeial stability requirements. Low concentration BPC infusions showed 0.5% and 2.5% degradation after 24 h storage at 22 °C and 35 °C, respectively.ConclusionsThe isotonic BPC 30 mg/mL formulation is simple to prepare and may offer clinical benefits in settings where hypertonic solutions are problematic. This study provides assurance that high- and low-dose isotonic BPC infusions are stable at room temperature and our findings may be applicable to in vitro studies of BPC.     

In Vivo Blood Velocity Vector Imaging Using Adaptive Velocity Compounding in the Carotid Artery Bifurcation

Visualization and quantification of blood flow are considered important for early detection of atherosclerosis and patient-specific diagnosis and intervention. As conventional Doppler imaging is limited to 1-D velocity estimates, 2-D and 3-D techniques are being developed. We introduce an adaptive velocity compounding technique that estimates the 2-D velocity vector field using predominantly axial displacements estimated by speckle tracking from dual-angle plane wave acquisitions. Straight-vessel experiments with a 7.8-MHz linear array transducer connected to a Verasonics Vantage ultrasound system revealed that the technique performed with a maximum velocity magnitude bias and angle bias of –3.7% (2.8% standard deviation) and –0.16° (0.41° standard deviation), respectively. In vivo, complex flow patterns were visualized in two healthy and three diseased carotid arteries and quantified using a vector complexity measure that increased with increasing wall irregularity. This measure could potentially be a relevant clinical parameter which might aid in early detection of atherosclerosis.     

Reduction of Echo Decorrelation via Complex Principal Component Filtering

Ultrasound motion estimation is a fundamental component of clinical and research techniques that include color flow Doppler, spectral Doppler, radiation force imaging and ultrasound-based elasticity estimation. In each of these applications, motion estimates are corrupted by signal decorrelation that originates from nonuniform target motion across the acoustic beam. In this article, complex principal component filtering (PCF) is demonstrated as a filtering technique for dramatically reducing echo decorrelation in blood flow estimation and radiation force imaging. We present simulation results from a wide range of imaging conditions that illustrate a dramatic improvement over simple bandpass filtering in terms of overall echo decorrelation (≤99.9% reduction), root mean square error (≤97.3% reduction) and the standard deviation of displacement estimates (≤97.4% reduction). A radiation force imaging technique, termed sonorheometry, was applied to fresh whole blood during coagulation, and complex PCF operated on the returning echoes. Sonorheometry was specifically chosen as an example radiation force imaging technique in which echo decorrelation corrupts motion estimation. At 2 min after initiation of blood coagulation, the average echo correlation for sonorheometry improved from 0.996 to 0.9999, which corresponded to a 41.0% reduction in motion estimation variance as predicted by the Cramer-Rao lower bound under reasonable imaging conditions. We also applied complex PCF to improve blood velocity estimates from the left carotid artery of a healthy 23-year-old male. At the location of peak blood velocity, complex PCF improved the correlation of consecutive echo signals from an average correlation of 0.94 to 0.998. The improved echo correlation for both sonorheometry and blood flow estimation yielded motion estimates that exhibited more consistent responses with less noise. Complex PCF reduces speckle decorrelation and improves the performance of ultrasonic motion estimation. (E-mail: fwm5f@virginia.edu)     

Effect of prior probability quality on biased time-delay estimation

When properly constructed, biased estimators are known to produce lower mean-square errors than unbiased estimators. A biased estimator for the problem of ultrasound time-delay estimation was recently proposed. The proposed estimator incorporates knowledge of adjacent displacement estimates into the final estimate of a displacement. This is accomplished by using adjacent estimates to create a prior probability on the current estimate. Theory and simulations are used to investigate how the prior probability impacts the final estimate. The results show that with estimation quality on the order of the Cramer-Rao lower bound at adjacent locations, the local estimate in question should generally exceed the Cramer-Rao lower-bound limitations on performance of an unbiased estimator. The results as a whole provide additional confidence for the proposed estimator.     

Improving the statistics of quantitative ultrasound techniques with deformation compounding: an experimental study

Many quantitative ultrasound (QUS) techniques are based on estimates of the radio-frequency (RF) echo signal power spectrum. Historically, reliable spectral estimates required spatial averaging over large regions-of-interest (ROIs). Spatial compounding techniques have been used to obtain robust spectral estimates for data acquired over small regions of interest. A new technique referred to as “deformation compounding” is another method for providing robust spectral estimates over smaller regions of interest. Motion tracking software is used to follow an ROI while the tissue is deformed (typically by pressing with the transducer). The deformation spatially reorganizes the scatterers so that the resulting echo signal is decorrelated. The RF echo signal power spectrum for the ROI is then averaged over several frames of RF echo data as the tissue is deformed, thus, undergoing deformation compounding. More specifically, averaging spectral estimates among the uncorrelated RF data acquired following small deformations allows reduction in the variance of the power spectral density estimates and, thereby, improves accuracy of spectrum-based tissue property estimation. The viability of deformation compounding has been studied using phantoms with known attenuation and backscatter coefficients. Data from these phantoms demonstrates that a deformation of about 2% frame-to-frame average strain is sufficient to obtain statistically-independent echo signals (with correlations of less than 0.2). Averaging five such frames, where local scatterer reorganization has taken place due to mechanical deformations, reduces the average percent standard deviation among power spectra by 26% and averaging 10 frames reduces the average percent standard deviation by 49%. Deformation compounding is used in this study to improve measurements of backscatter coefficients. These tests show deformation compounding is a promising method to improve the accuracy of spectrum-based quantitative ultrasound for tissue characterization.     

Hybrid spectral domain method for attenuation slope estimation

Attenuation estimation methods for medical ultrasound are important because attenuation properties of soft tissue can be used to distinguish between benign and malignant tumors and to detect diffuse disease. The classical spectral shift method and the spectral difference method are the most commonly used methods for the estimation of the attenuation; however, they both have specific limitations. Classical spectral shift approaches for estimating ultrasonic attenuation are more sensitive to local spectral noise artifacts and have difficulty in compensating for diffraction effects because of beam focusing. Spectral difference approaches, on the other hand, fail to accurately estimate attenuation coefficient values at tissue boundaries that also possess variations in the backscatter. In this paper, we propose a hybrid attenuation estimation method that combines the advantages of the spectral difference and spectral shift methods to overcome their specific limitations. The proposed hybrid method initially uses the spectral difference approach to reduce the impact of system-dependent parameters including diffraction effects. The normalized power spectrum that includes variations because of backscatter changes is then filtered using a Gaussian filter centered at the transmit center frequency of the system. A spectral shift method, namely the spectral cross-correlation algorithm is then used to compute spectral shifts from these filtered power spectra to estimate the attenuation coefficient. Ultrasound simulation results demonstrate that the estimation accuracy of the hybrid method is better than the centroid downshift method (spectral shift method), in uniformly attenuating regions. In addition, this method is also stable at boundaries with variations in the backscatter when compared with the reference phantom method (spectral difference method). Experimental results using tissue-mimicking phantom also illustrate that the hybrid method is more robust and provides accurate attenuation estimates in both uniformly attenuating regions and across boundaries with backscatter variations. The proposed hybrid method preserves the advantages of both the spectral shift and spectral difference approaches while eliminating the disadvantages associated with each of these methods, thereby improving the accuracy and robustness of the attenuation estimation.     

The nonstationary strain filter in elastography: Part I. Frequency dependent attenuation

The accuracy and precision of the strain estimates in elastography depend on a myriad number of factors. A clear understanding of the various factors (noise sources) that plague strain estimation is essential to obtain quality elastograms. The nonstationary variation in the performance of the strain filter due to frequency-dependent attenuation and lateral and elevational signal decorrelation are analyzed in this and the companion paper for the cross-correlation-based strain estimator. In this paper, we focus on the role of frequency-dependent attenuation in the performance of the strain estimator. The reduction in the signal-to-noise ratio (SNR_s) in the RF signal, and the center frequency and bandwidth downshift with frequency-dependent attenuation are incorporated into the strain filter formulation. Both linear and nonlinear frequency dependence of attenuation are theoretically analyzed. Monte-Carlo simulations are used to corroborate the theoretically predicted results. Experimental results illustrate the deterioration in the precision of the strain estimates with depth in a uniformly elastic phantom. Theoretical, simulation and experimental results indicate the importance of high SNR_s values in the RF signals, because the strain estimation sensitivity, elastographic SNR_e and dynamic range deteriorate rapidly with a decrease in the SNR_s. In addition, a shift in the strain filter toward higher strains is observed at large depths in tissue due to the center frequency downshift.     

Investigation of parametric spectral estimation techniques for elasticity imaging

Several autoregressive (AR) and autoregressive moving average (ARMA) parametric spectral estimators were evaluated for use in tissue strain estimation. Using both 1-D simulations and in vitro phantom experiments, the performance of these parametric spectral strain estimators were compared against both a nonparametric discrete Fourier transform (DFT) spectral strain estimator and a coherent elastographic technique. Parametric spectral estimator model orders were selected based on a modified strain filter approach. This technique illustrated the trade-offs between different signal-processing parameters and a strain estimator performance measure, namely the area under the strain filter (using applied strain dynamic range of 0.1 to 50%). The Yule-Walker AR spectral strain estimator outperformed all other parametric methods evaluated, but failed to outperform the DFT-based approach. Furthermore, both these spectral strain-estimation techniques exhibit an elastographic signal-to-noise ratio (SNR(e)) and strain estimation dynamic range not achievable using conventional elastography without global stretching.     

A method for experimental characterization of the noise performance of elastographic systems

Characterization of the noise performance of the elastographic system is necessary to evaluate the accuracy and precision of the estimated strain. The elastographic system includes the ultrasonic scanner, the computer controlled compression device and the strain estimation algorithm. In this paper, we present a method of characterizing the elastographic system experimentally using a uniformly elastic homogenous tissue-mimicking phantom. The strain response of the elastographic system is evaluated by characterizing the accuracy and precision of the strain estimates for a large range of input strains. The experimental results obtained follow the theoretical predictions obtained using the Strain Filter (SF) concept for the cross-correlation based strain estimator. In this paper, we illustrate the application of the Experimental Strain Response (ESR) to characterize strain estimation at the focus of the transducer and the axis of symmetry of the phantom.     

The use of stationarity and nonstationarity in the detection and analysis of neural oscillations

Using available signal (i.e., spectral and time-frequency) analysis methods, it can be difficult to detect neural oscillations because of their continuously changing properties (i.e., nonstationarities) and the noise in which they are embedded. Here, we introduce fractally scaled envelope modulation (FSEM) estimation which is sensitive specifically to the changing properties of oscillatory activity. FSEM utilizes the fractal characteristic of wavelet transforms to produce a compact, two-dimensional representation of time series data where signal components at each frequency are made directly comparable according to the spectral distribution of their envelope modulations. This allows the straightforward identification of neural oscillations and other signal components with an envelope structure different from noise. For stable oscillations, we demonstrate how partition-referenced spectral estimation (PRSE) removes the noise slope from spectral estimates, yielding a level estimate where only peaks signifying the presence of oscillatory activity remain. The functionality of these methods is demonstrated with simulations and by analyzing MEG data from human auditory brain areas. FSEM uncovered oscillations in the 9- to 12-Hz and 15- to 18-Hz ranges whereas traditional spectral estimates were able to detect oscillations only in the former range. FSEM further showed that the oscillations exhibited envelope modulations spanning 3-7 s. Thus, FSEM effectively reveals oscillations undetectable with spectral estimates and allows the use of EEG and MEG for studying cognitive processes when the common approach of stimulus time-locked averaging of the measured signal is unfeasible.     

Frequency domain analysis of cerebral near infrared spectroscopy signals during application of an impedance threshold device in spontaneously ventilating volunteers

Currently available near infrared spectroscopy (NIRS) devices are unable to discriminate between arterial and venous blood, a potential source of artifact. The purpose of this study was to test the hypothesis that oscillations in NIR signals at the respiratory and cardiac frequency could be attributed to venous and arterial blood, respectively, and thereby isolated. After written informed consent was obtained, a two-wavelength NIRS device was placed over the left frontal cortex in 20 volunteers. After 5 min of unimpeded spontaneous ventilation, an impedance threshold device (ITD, average resistance—7 cm H₂O) was applied and an additional two minutes of data recorded. Tissue saturation (StO₂) calculated at the ventilatory and cardiac frequencies was compared to non-pulsatile StO₂, before and after application of the ITD using spectral peak and power algorithms. The ITD increased non-pulsatile cerebral saturation by 3.6 %. The ITD had no discernable effect on pulsatile estimates of StO₂ at either the ventilatory or cardiac frequencies. StO₂ estimated at the NIRS spectral peak from 0.75 to 1.75 Hz was 24 % higher than non-pulsatile StO₂ (p = 0.0013). There were no other significant differences between pulsatile and non-pulsatile algorithms in the estimation of StO₂. In 64 % of cases, both the low (ventilator) and high (cardiac) frequency estimates of StO₂ were either both larger or both smaller than non-pulsatile StO₂, suggesting that they were interrogating the same vascular bed. Frequency domain analysis cannot reliably separate NIRS waveforms into arterial and venous components.     

Characterization of Tissue Microstructure Scatterer Distribution with Spectral Correlation

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echo-formation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.     

Adaptive spectral strain estimators for elastography

In conventional elastography, internal tissue deformations, induced by external compression applied to the tissue surface, are estimated by cross-correlation analysis of echo signals obtained before and after compression. Conventionally, strains are estimated by computing the gradient of estimated displacement. However, gradient-based algorithms are highly susceptible to noise and decorrelation, which could limit their utility. We previously developed strain estimators based on a frequency-domain (spectral) formulation that were shown to be more robust but less precise compared to conventional strain estimators, In this paper, we introduce a novel spectral strain estimator that estimates local strain by maximizing the correlation between the spectra of pre- and postcompression echo signals using iterative frequency-scaling of the latter; we also discuss a variation of this algorithm that may be computationally more efficient but less precise. The adaptive spectral strain estimator combines the advantages of time- and frequency-domain methods and has outperformed conventional estimators in experiments and 2-D finite-element simulations.     

Visual estimation of shoulder posture: accuracy and reliability across five planes of motion

Background: Visual estimation is still widely used by physiotherapists in clinical practice. The accuracy and reliability of visual estimation are under question and vary with the joint being evaluated. The utility of using visual estimation to evaluate shoulder angular posture is inconclusive and has yet to be evaluated across a comprehensive range of planes and postures.

Objective: The purpose of this observational, cross-sectional study was to determine whether visual estimation is an accurate and reliable procedure for determining shoulder angular postures in a range of shoulder motions.

Methods: Sixty-three physical therapy students viewed digital photographs of an asymptomatic volunteer to visually estimate degrees of arm abduction, flexion, extension, internal rotation, and external rotation. Six photographs from each plane of movement were shown, at positions spanning the available range of motion, and participants were asked to estimate the angular posture. Estimates were averaged, and errors from computer measurements were calculated. Differences between estimates and measurements were determined with t-tests.

Results: The overall accuracy of the visual estimates is considered poor to fair. Root mean square errors were reasonable, from 5° to 17°, but the 95% spread of the data reached a maximum of 53°. Estimates were most accurate at the abduction and flexion postures closest to 90°. Most estimates for extension were underestimated, while more internal rotation estimates were overestimated. Reliability ranged from poor to fair (0.12–0.63).

Conclusions: Using visual estimation to evaluate shoulder planar postures results in a wide range of error. Physiotherapy students should be encouraged to use more objective measurement methods.     

Robust Rician noise estimation for MR images

In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.