Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Tsui, Po-Hsiang; Chen, Chin Kuo; Kuo, Wang-Chuang; Chang, King‐Jen; Fang, Jui; Yang, Hsiang; Chou, Dean

doi:10.1038/srep41004

Cited by 65 publications

(54 citation statements)

References 61 publications

(93 reference statements)

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“…Entropy re ects the complexity and heterogeneous character of lesion texture. Larger entropy indicates more information contained in an image and greater uniformity of the pixel matrix of the image [16].…”

Section: Discussionmentioning

confidence: 99%

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Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

Preprint

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Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

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Section: Discussionmentioning

confidence: 99%

“…The increase in scattering media causes a variation of backscattering, which reduces entropy. Therefore, compared to benign tumours, the entropy of malignant tumours is often lower [16,17]. Category 4A benign and malignant lesions are atypical benign and malignant lesions.…”

Section: Discussionmentioning

confidence: 99%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

Preprint

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“…However, the m estimation stability was limited when a fixed window size was used in some cases, which have been indicated in Ref. []. Thus, Tsui et al proposed the window‐modulated estimation method to balance the smoothness and discriminability of m estimations and then enhanced the stability for tissue characterization .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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Purpose The Nakagami statistical model and Nakagami shape parameter m have been widely used in linear tissue characterization and preliminarily characterized the envelope distributions of nonlinear encapsulated microbubbles (EMBs). However, the Nakagami distribution of nonlinear scattering EMBs lacked a systematical investigation. Thus, this study aimed to investigate the Nakagami distribution of EMBs and illustrate the impact of EMBs' nonlinearity on the Nakagami model. Method A group of simulated EMB phantoms and in vitro EMB dilutions with an increasing concentration distribution under various EMB nonlinearities, as regulated by acoustic parameters, were characterized by using the window‐modulated compounding Greenwood–Durand estimator. Results Raw envelope histograms of simulated and in vitro EMBs were well matched with the Nakagami distribution with a high correlation coefficient of 0.965 ± 0.021 (P < 0.005). The mean values and gradients of m parameters of simulated and in vitro EMBs were smaller than those of linear scatterers due to the stronger nonlinearity. These m values exhibited a quasi‐linear improvement with the increase in second harmonic nonlinear‐to‐linear component ratio regulated by pulse lengths and excitation frequencies at low‐ and high‐concentration conditions. Conclusions The Nakagami distribution was suitable for the EMBs characterization but the corresponding m parameter was affected by the EMBs' nonlinearity. These validations provided support and nonlinear impact assessment for the EMBs' characterization using the Nakagami statistical model in the future.

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“…During the cooling period, a stirrer was used to suspend graphite powder in the agar solution until the solution was solidified. According to a previous study 26 , the applied ratio between the weight of graphite powder and the volume of the solution ensures the appearance of fully developed speckles in ultrasound B-mode images.…”

Section: Methodsmentioning

confidence: 99%

Frequency-domain CBE imaging for ultrasound localization of the HIFU focal spot: a feasibility study

Yang

Liu

et al. 2020

Sci Rep

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High-intensity focused ultrasound (HifU) is a well-accepted tool for noninvasive thermal therapy. to control the quality of HifU treatment, the focal spot generated in tissues must be localized. Ultrasound imaging can monitor heated regions; in particular, the change in backscattered energy (cBe) allows parametric imaging to visualize thermal information in the tissue. conventional cBe imaging constructed in the spatial domain may be easily affected by noises when the HIFU focal spot is visualized. this study proposes frequency-domain cBe imaging to improve noise tolerance and image contrast in HifU focal spot monitoring. phantom experiments were performed in a temperaturecontrolled environment. HIFU of 2.12 MHz was applied to the phantoms, during which a clinical scanner equipped with a 3-MHz convex array transducer was used to collect raw image data consisting of backscattered signals for B-mode, spatial-, and frequency-domain cBe imaging. concurrently, temperature changes were measured at the focal spot using a thermocouple for comparison with CBE values by calculating the correlation coefficient r. to further analyze cBe image contrast levels, a contrast factor was introduced, and an independent t-test was performed to calculate the probability value p. experimental results showed that frequency-domain cBe imaging performed well in thermal distribution visualization, enabling quantitative detection of temperature changes. the cBe value calculated in the frequency domain also correlated strongly with that obtained using the conventional spatial-domain approach (r = 0.97). In particular, compared with the image obtained through the conventional method, the contrast of the cBe image obtained using the method based on frequencydomain analysis increased by 2.5-fold (4 dB; p < 0.05). Frequency-domain computations may constitute a new strategy when ultrasound cBe imaging is used to localize the focal spot in HifU treatment planning.

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Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Cited by 65 publications

References 61 publications

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

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

Frequency-domain CBE imaging for ultrasound localization of the HIFU focal spot: a feasibility study

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