Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution

Wang, Diya; Sang, Yuchao; Zhang, Xinyu; Hu, Hong; Lu, Shukuan; Fu, Chaoying; Cloutier, Guy; Wan, Mingxi

doi:10.1002/mp.13833

Cited by 8 publications

(25 citation statements)

References 54 publications

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“…29 Simulation and in vitro studies have validated that the corresponding impact mechanism is regulated by pulse-emitted frequency, pressure, cycle, and other acoustic parameters. 29 Thus, MB characterization studies using the m parameter provide a possible option to overcome the limitations of backscattered echo amplitude-coded MEUS.…”

Section: Introductionmentioning

confidence: 91%

“…29,37 Monte--Carlo simulations have revealed that the Greenwood-Durand and Bowman estimators have the highest m estimation accuracy for tissue scatterers but poor calculation efficiency. 29 The ME has the highest operational efficiency and a relatively low but acceptable estimation accuracy for tissue echoes. 29 However, the utility of these estimators under in vivo MEUS imaging remains unclear and has not been fully evaluated.…”

Section: B Wmcme Vs Other Nakagami-m Estimatorsmentioning

confidence: 99%

“…29 The ME has the highest operational efficiency and a relatively low but acceptable estimation accuracy for tissue echoes. 29 However, the utility of these estimators under in vivo MEUS imaging remains unclear and has not been fully evaluated. Sensitivity and discriminability of MB characterization should be considered when MB echoes are surrounded by and mixed with tissue speckles in in vivo conditions.…”

Section: B Wmcme Vs Other Nakagami-m Estimatorsmentioning

confidence: 99%

“…However, due to the average effects of the multiscale window-by-window estimation strategy, the average values of axial and lateral resolutions of all WMCME-based m log -coded images regulated by RF and VF processing techniques were 0.11 AE 0.06 mm (P = 0.03) and 0.10 AE 0.05 mm (P = 0.02) slightly longer than those of the amplitude-coded images, respectively. In consideration of the improvements afforded by the m-coded method to suppress the artifacts due to variation in system parameters and operators, [26][27][28][29] the slightly decreased resolutions in the WMCME-based m log -coded MEUS method with the improved CTR and CNR were considered acceptable.…”

Section: D Echo Amplitude-coded Vs Wmcme-based M Logcoded Meusmentioning

confidence: 99%

“…Local m values have been used to detect and quantify the MB numbers within a resolution cell 17,27 due to the quasi-linear relationships between m values and MB distributions within a suitable concentration range. 29 Therefore, the proposed scheme that uses the WMCME method has the potential to characterize the local concentration distributions of MBs and the perfusion enhanced by MBs. This potential application would be useful to better approximate perfusion rates and volumes within the microvasculature, and potentially improve the precise diagnoses, monitoring, and evaluation of MEUSguided therapy.…”

Section: E Advantages and Limitations Of Mand M Logcoded Meusmentioning

confidence: 99%

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In vivo Nakagami‐m parametric imaging of microbubble‐enhanced ultrasound regulated by RF and VF processing techniques

et al. 2020

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Purpose Application of the Nakagami statistical model and associated m parameter has the potential to suppress artifacts from adjustable system parameters and operator selections typical in echo amplitude‐coded microbubble‐enhanced ultrasound (MEUS). However, the feasibility of applying m estimation and determination of the associated Nakagami distribution features for in vivo MEUS remain to be investigated. Sensitivity and discriminability of m‐coded MEUS are often limited since raw envelopes are regulated by complex radiofrequency (RF) and video‐frequency (VF) processing. This study aims to develop an improved imaging approach for the m parameter estimation which can overcome the above limitations in in vivo condition. Method The regulation effects of RF processing of pulse‐inversion (PI) harmonic detection techniques and VF processing of logarithmic compression in Nakagami distributions were investigated in MEUS. A window‐modulated compounding moment estimator was developed to estimate the MEUS m values. The sensitivity and discriminability of m‐coded MEUS were quantified with contrast‐to‐tissue ratio (CTR), contrast‐to‐noise ratio (CNR), and axial and lateral resolutions, which were validated through in vivo perfusion experiments on rabbit kidneys. Results Regulated by RF and VF processing, the distributions of MEUS obeyed the Nakagami statistical model. The Nakagami‐fitted correlation coefficient was 0.996 ± 0.003 (P < 0.05 in the t test and P < 0.001 in the Kolmogorov−Smirnov test). Among each of the m‐coded MEUS methods, the logarithmic m‐coded PI‐MEUS scheme effectively characterized the peripheral rim perfusion features and details within the renal cortex. The CTR and CNR in this region reached 7.9 ± 1.5 dB and 34.4 ± 1.7 dB, respectively, which were higher than those of standard amplitude‐coded MEUS; and the axial and lateral resolutions were 1.02 ± 0.02 and 0.91 ± 0.02 mm, respectively, which were slightly longer than those of amplitude‐coded MEUS. Conclusions The Nakagami statistical model could characterize MEUS even when the envelope distributions were regulated by RF and VF processing. The logarithmic m‐coded PI‐MEUS scheme significantly improved the sensitivity, discriminability, and robustness of m estimation in MEUS. The scheme provides an option to remove artifacts in echo amplitude‐coded MEUS and to distinctly characterize the inherent microvasculature enhanced by microbubbles, with potential to improve and expand the role of MEUS in diagnostic ultrasound.

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Section: Introductionmentioning

confidence: 91%

Section: B Wmcme Vs Other Nakagami-m Estimatorsmentioning

confidence: 99%

Section: B Wmcme Vs Other Nakagami-m Estimatorsmentioning

confidence: 99%

Section: D Echo Amplitude-coded Vs Wmcme-based M Logcoded Meusmentioning

confidence: 99%

Section: E Advantages and Limitations Of Mand M Logcoded Meusmentioning

confidence: 99%

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In vivo Nakagami‐m parametric imaging of microbubble‐enhanced ultrasound regulated by RF and VF processing techniques

et al. 2020

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Feasibility investigation of logarithmic Nakagami parametric imaging in recovering underestimated perfusion metrics of DCEUS in the uneven acoustic field

Wang

et al. 2022

Medical Physics

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Owing to acoustic-pressure dependence, amplitudes of backscattered-echoes of encapsulated microbubbles (MBs) are unavoidably regulated by an uneven acoustic field, resulting in the misestimation of hemodynamics in conventional amplitude-coding dynamic contrast-enhanced ultrasound (DCEUS) with focused pulse transmission. This study aimed to investigate the feasibility and performance of Nakagami statistical-feature parametric imaging to recover the above misestimation. Methods: Logarithmic Nakagami parameter (m)-coding DCEUS scheme was investigated via simulation and in vitro MB phantoms as well as in vivo kidneyperfusion experiments of four rabbits in the uneven acoustic fields with two different focal depths. In vivo tissue artifacts for m estimation were suppressed by pulse-inversion second-harmonic imaging and its robustness was enhanced by multiscale moment-estimation strategy. Time-Nakagami-m curves and the corresponding perfusion metrics of intensity and volume were calculated from the logarithmic m-coding DCEUS images within the prefocal and focal regions. These curves and metrics were further compared with the perfusion curves and metrics estimated from the conventional amplitude-coding images within the same regions. Results: Compared with amplitudes of nonlinear scattering MB echoes, their logarithmic m values were relatively independent of the changes in acoustics pressures. Compared with the fixed-scale moment-estimation, the perfusion intensity estimated from logarithmic m-coding DCEUS scheme using multiscale statistical moment-estimation had smaller differences between the prefocal and focal regions. The differences of perfusion intensity induced by an uneven acoustic field decreased to 3.47% ± 1.58 %. The differences decreased by the logarithmic m-coding DCEUS scheme were further regulated by threshold values of m estimation. Conclusions:The logarithmic m-coding DCEUS scheme could recover the underestimated MB backscattered-echoes and the misestimated perfusion intensity induced by the uneven acoustic field. The scheme had the potential to weaken the limitation of microvasculature identification and hemodynamic characterization marked by MBs within tissues or tumors in the uneven acoustic field. K E Y W O R D Scontrast-enhanced ultrasound, focused acoustic field, hemodynamic, Nakagami parameter 2452

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Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

Xu,

Zhao,

Wan

et al. 2024

Medical Physics

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BackgroundBreast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied.PurposeThis work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B‐mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy.MethodsFirst, original US images coming directly from the hospital were cropped and resized. In 1385 B‐mode US BI‐RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K‐means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B‐mode images, K‐means clustering images and entropy images, they are fused in a three‐channel form multi‐feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1‐score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro‐Wilk test. DeLong test and independent t‐test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti‐log compression was conducted but no improvement has shown in CNNs classification results.ResultsWith multi‐feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi‐feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B‐mode images (p < 0.05).ConclusionThe CNNs with multi‐feature fusion show a good potential of reducing the false‐positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false‐positive rate in breast tumor within US BI‐RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

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Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution

Cited by 8 publications

References 54 publications

In vivo Nakagami‐m parametric imaging of microbubble‐enhanced ultrasound regulated by RF and VF processing techniques

In vivo Nakagami‐m parametric imaging of microbubble‐enhanced ultrasound regulated by RF and VF processing techniques

Feasibility investigation of logarithmic Nakagami parametric imaging in recovering underestimated perfusion metrics of DCEUS in the uneven acoustic field

Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks

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