Tissue Characterization by Ultrasound: Linking Envelope Statistics with Spectral Analysis for Simultaneous Attenuation Coefficient and Scatterer Clustering Quantification

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper innovatively combines spectral analysis methods (commonly used for determining attenuation coefficients) with envelope statistics (Homodyne-K distribution), proposing a biparametric imaging method based on the μ parameter and attenuation coefficient. This enhances the quantification of tissue microstructure and shows clinical translation potential. The following limitations should be addressed: 1. Although the composite sample imaging results are clearly illustrated, the textual analysis section consists mainly of qualitative descriptions. It is recommended to supplement this with some quantitative indicators for boundary identification to enhance persuasiveness. 2. It is recommended to add a comparison of the results with existing methods, such as quantifying the improvement in sensitivity of the new method compared to traditional ultrasound or single-parameter methods in the discussion, highlighting the advantages of the new method. 3. Figure 5 shows that the attenuation coefficient of aluminum particles is higher than that of alumina particles, which is attributed to the difference in particle density. However, it is recommended that an acoustic performance analysis be conducted to determine whether this difference is related to differences in the acoustic impedance of the materials. 4. Although the derivation of the Homodyne-K distribution and attenuation coefficient in Section 2 is relatively complete, the formula chain is lengthy. It is recommended to further simplify the derivation steps. In addition, some symbols in the formula are not explained, such as J0(us) and J0(uA)in Formula 1. 5. PVA phantoms’ acoustic properties differ from real tissues. Discuss whether this affects the method’s applicability to biological samples. 6. "Homodyne-K" and "Homodyned-K" are used interchangeably. Recommend unifying the term.

Author Response

Authors thanks the reviewer for the suggestions made which has contributed to improve the work. The comments of the reviewer appear in black, authors’ response in blue, and new text added to the manuscript in black with yellow background.

 

 

Comments 1

The paper innovatively combines spectral analysis methods (commonly used for determining attenuation coefficients) with envelope statistics (Homodyne-K distribution), proposing a biparametric imaging method based on the μ parameter and attenuation coefficient. This enhances the quantification of tissue microstructure and shows clinical translation potential. The following limitations should be addressed:

1. Although the composite sample imaging results are clearly illustrated, the textual analysis section consists mainly of qualitative descriptions. It is recommended to supplement this with some quantitative indicators for boundary identification to enhance persuasiveness.

Response 1:

As suggested, quantitative indicators were added to the improve de description of Figure 11.

Lines 626-630:

“The dominance of orange in the image corresponds to μ parameter close to 1.2 and attenuations closed to 300Np/m and clearly identified material 1. On the other side, the yellow region, with μ parameter above 1.6, and the dark region, with attenuations under 200Np/m, indicated the locations of the other two materials, 2 and 3, respectively.”

 

Comments 2:

2. It is recommended to add a comparison of the results with existing methods, such as quantifying the improvement in sensitivity of the new method compared to traditional ultrasound or single-parameter methods in the discussion, highlighting the advantages of the new method.

 

Response 2:

Now it is commonly accepted by the ultrasound research community that B-scans represent a first qualitative approach, which can be complemented by QUS methods. As a consequence and following the reviewer suggestion, authors highlighted the improved sensitivity of multiparametric QUS in comparison to previous simpler approaches providing only one parameter, which was added to the discussion of figure 10.

Lines 587-590

“Based only on attenuation images, it’s hard to distinguish materials 2 and 3. The same happens with materials 1 and 3 if only the μ parameter was analysed. This points out the higher efficiency of multiparametric quantitative analysis related to simple models based only on attenuation or μ parameter”.

 

Comments 3:

3. Figure 5 shows that the attenuation coefficient of aluminium particles is higher than that of alumina particles, which is attributed to the difference in particle density. However, it is recommended that an acoustic performance analysis be conducted to determine whether this difference is related to differences in the acoustic impedance of the materials.

Response 3:

We have introduced the suggested analysis and added to the text; the impedance of alumina is higher than that of aluminium which gives a higher- but not significant- reflection coefficient for alumina particles.

Lines 424-426

“The reflection coefficient of PVA gel-aluminium and PVA gel-alumina particle are 0.83 and 0.93, respectively, so not a significant impact was expected by these differences of impedance mismatch in the B-Scans.”

 

Such fact reinforces the idea that the of the μ values are higher as an effect of the lower density and, consequently, larger number of aluminium particles. We have improved this explanation in the text:

Lines 487-490

“Additionally, materials loaded with alumina particles exhibited lower μ values than those with the same concentrations of aluminium particles, which aligns with the echo envelope results presented earlier and is attributed to the lower density and, consequently, the larger number of aluminium particles”.

 

Comments 4:

4. Although the derivation of the Homodyne-K distribution and attenuation coefficient in Section 2 is relatively complete, the formula chain is lengthy. It is recommended to further simplify the derivation steps. In addition, some symbols in the formula are not explained, such as J0(us) and J0(uA)in Formula 1.

Response 4:

The J₀(x) is the Bessel function of order 0. It has been explained in line 133

The same was done with the gamma function of equation 2, line 145

According to the reviewer suggestion, authors simplified the formula chain from equation 8, while maintaining a detailed description of all the steps to guide the reader through the derivations of results. Equations 8, 10 and 12 condense some of the previous steps, reducing the number of equations from 23 to 19.

 

Comments 5:

5. PVA phantoms’ acoustic properties differ from real tissues. Discuss whether this affects the method’s applicability to biological samples.

Response 5:

Authors agree that discussion about phantom limitations is pertinent, so different comments were added to the text about the phantom used. In first place, at the beginning of the section 2.3, a sentence to describe the suitability of these gels to mimic biological tissues was added:

Lines 267-270

“That study highlighted the suitability of this gel for phantom fabrication due to its similarity in density and sound speed to biological soft tissues, the robustness of the resulting gels, and the possibility of tailoring their properties through the addition of specific additives to mimic different tissues. “

Some lines after, the limitation of these phantoms was commented:

Lines 298-302

“This customization yields isotropic gels with varying scatterer concentrations and attenuation properties. Although biological tissues often exhibit anisotropic behavior and boundary irregularities, experiments conducted with these phantoms may provide a preliminary assessment of the algorithm’s performance prior to further measurements with real tissues.”

Finally, in the conclusions section, the need of further studies to overcome these limitations was pointed:

Lines 689-692

“However, as noted above, the inherent anisotropy and boundary irregularities of biological tissues may affect the ability of the algorithms to accurately characterize them. Consequently, further studies on real biological tissues are required to fully assess the potential of the proposed methods.”

 

Comments 6:

6. "Homodyne-K" and "Homodyned-K" are used interchangeably. Recommend unifying the term.

Response 6:

Along the whole text, references to this function have been unified to “Homodyned K-distribution”. In this case, we have not highlighted in yellow these corrections to improve the readability of the other corrections.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

General Comments: This work presents a quantitative ultrasound method combining spectral attenuation estimation with envelope statistics (using Homodyned-K fitting) to simultaneously extract attenuation coefficients and scatterer clustering. The approach is tested on PVA phantoms with varied particle types and concentrations, and extended to a phantom mimicking tissue. The methodology is sound and well-detailed, the experimental design is fairly rigorous, and the results are clearly presented. The integration of both parameters into a bi-parametric representation is a noteworthy and potentially impactful contribution.

Abstract: The abstract clearly summarises objectives, methodology, and main findings, highlight the novelty of linking attenuation coefficient estimation with envelope statistics in a single study. I suggest to explicitly state that the study is based on phantoms, so readers immediately understand the testing stage.

Introduction: The introduction is clear and sets the scene well. There is good contextualisation of the need for this method beyond B-scans. The manuscript uses strong literature as background and to support certain statements. There is clear motivation for combining spectral and statistical approaches. In some cases, the introduction repeats certain contexts and ideas more than once. I suggest to further tighten the introduction by reducing long sentences. This can help improve impact of the manuscript. There might be a need to expand the clinical relevance of this work, including outlining in which diagnostic contexts the methods and approaches could be transformative in the current state of tissue characterisation. While AI is mentioned as a possible benefit, there is no deeper discussion on this. 

Methods: Mathematical derivations are thorough, with equations and parameter defined appropriately. The link between variations and attenuation is clear. I would like to point out that, however, in some cases, variables are defined later in the text or within equations without prior definition. I would suggest the authors to go through these once more.

There is clear progression from theory to practice within the methods section. It would help the reader if there is more discussion on limitations of any assumptions taken for these experiments as well as the related analysis, especially since the study is focused on biological tissue.

Phantom development is described in detail. The images for hardware setup and acquisition parameters are well-specified and clear.

Results and Discussion: The figures enforce the authors findings. There is good comparison of attenuation and μ parameter performance. More insight and detail on why these results are important within the larger area of this field of study would be beneficial in the Discussion.

Conclusion: The conclusion summarises key contributions clearly, reiterating advantages of the proposed methods. There is a need for the authors to acknowledge that the results might have limitations and that further real-world studies are essential to validate clinical utility.

Comments on the Quality of English Language

Overall, the manuscript is clear and professional. There are some sentences which could be shortened to improve readability and impact. Minor language changes and sentence structure could help improve flow. 

 

Author Response

Authors thanks the reviewer for the suggestions made which has contributed to improve the work. The comments of the reviewer appear in black, authors’ response in blue, and new text added to the manuscript in black with yellow background.

 

Comments 1:

General Comments: This work presents a quantitative ultrasound method combining spectral attenuation estimation with envelope statistics (using Homodyned-K fitting) to simultaneously extract attenuation coefficients and scatterer clustering. The approach is tested on PVA phantoms with varied particle types and concentrations, and extended to a phantom mimicking tissue. The methodology is sound and well-detailed, the experimental design is fairly rigorous, and the results are clearly presented. The integration of both parameters into a bi-parametric representation is a noteworthy and potentially impactful contribution.

Abstract: The abstract clearly summarises objectives, methodology, and main findings, highlight the novelty of linking attenuation coefficient estimation with envelope statistics in a single study. I suggest to explicitly state that the study is based on phantoms, so readers immediately understand the testing stage.

Response 1:

The abstract was changed to clarify this point as soon as possible (lines 15-18):

“Initially, the Homodyned K-distribution model used to fit data obtained from ultrasound signal envelopes was reviewed and the necessary equations to further derive the attenuation coefficient from this model were developed. To test and discuss the performance of these methods, experimental work was conducted in phantoms...”

 

Comments 2:

Introduction: The introduction is clear and sets the scene well. There is good contextualisation of the need for this method beyond B-scans. The manuscript uses strong literature as background and to support certain statements. There is clear motivation for combining spectral and statistical approaches. In some cases, the introduction repeats certain contexts and ideas more than once. I suggest to further tighten the introduction by reducing long sentences. This can help improve impact of the manuscript.

Response 2:

The following sentences were modified in the introduction according to reviewer suggestion:

Lines 33-35

“Changes in the acoustic scattering properties of tissues has been related with the modification of tissue’s physical properties by disease, as demonstrated in [1] for liver and ocular tissues.”

Lines 41-42

“To avoid this, substantial efforts have been made to develop methods capable of extracting quantitative information from ultrasound data.”

Lines 44-45

“Several algorithms have attempted to characterize tissue sound attenuation by analyzing the spectral content of the waves reflected from different depths [3-5].”

Lines 77-79

“First, a wideband radiofrequency (RF) signal scattered by tissues is split into different frequency components using bandpass filtering.”

Lines 85-87

“This clustering can be related to scatterer concentration, although the relation is indirect since the μ parameter depends on acquisition system settings, total attenuation, and the density and spatial organization of scatterers [33].”

Lines 90-92

“Consequently, all this information can be visualized as new images superimposed onto ultrasound B-scan images, thereby enriching the understanding of tissue characteristics.”

Lines 95-97

“Within this work, the algorithm was applied to ultrasound signals from tissue mimicking phantoms to evaluate the performance of the method.”

Lines 105-112

“The structure of this paper is as follows: first, the mathematical approach utilizing the Homodyned K-distribution used to analyse ultrasound signals backscattered by tissues was presented. It was also shown how the attenuation coefficient can be obtained from the derivative of one of the coefficients provided by this distribution. In the following section, the experimental procedure employed to obtain the ultrasound dataset was explained: This section includes the description of the tissue mimicking samples, the ultrasound hardware used (based on a scanning single-channel transducer arrangement), and the signal treatment methodology.”

Comments 3:

There might be a need to expand the clinical relevance of this work, including outlining in which diagnostic contexts the methods and approaches could be transformative in the current state of tissue characterisation. While AI is mentioned as a possible benefit, there is no deeper discussion on this.

 

Response 3:

In this initial stage of the algorithm research, when it was derived and applied to phantoms at the lab, it is difficult to discuss which could be the relevance of the proposal in a clinical setting; however, it is expected that the technique may help to increase the diagnostic sensitivity where the spectral variation techniques or the envelope techniques have been applied separately. A paragraph has been added in relation to this at the introduction:

Lines 98-104

“This new approach may significantly impact clinical settings where the characterization of backscattering spectral changes [11–15] or Homodyned K-distribution parameters [9, 10] has already proven clinically relevant. These include, between others, dermatological, oncological, and hepatic diseases, in which measuring an additional parameter could further enhance the technique’s sensitivity. Furthermore, the proposed analysis holds potential for application to other pathologies that would benefit from more precise tissue characterization.”

In relation with the AI comment, it is reasonable to expect that the additional data provided by QUS algorithms will enhance the performance of AI methods as they are progressively trained with more information. However, the authors believe that a thorough discussion of this issue should be reserved until such AI algorithms can be applied in conjunction with the newly generated images, thereby allowing experimental results to serve as a solid basis for the discussion. However, the text was refined to be more concrete about the role of such QUS models in the improvement of AI algorithm performance:

Lines 70-72

“This pre-processing step is expected to facilitate the automatic detection of pathological tissue features, as AI algorithms could be trained with more information.”

Comment 4:

Methods: Mathematical derivations are thorough, with equations and parameter defined appropriately. The link between variations and attenuation is clear. I would like to point out that, however, in some cases, variables are defined later in the text or within equations without prior definition. I would suggest the authors to go through these once more.

Response 4:

The equations were reviewed and we have replaced X by the previous S in eq.7 (it was a mistake), and central frequency,  was now defined in line 196. In addition, previous variable s of the gaussian was replace by d in new equation 13, to avoid confusion with the s parameter of the Homodyned K-distribution.

Comment 5:

There is clear progression from theory to practice within the methods section. It would help the reader if there is more discussion on limitations of any assumptions taken for these experiments as well as the related analysis, especially since the study is focused on biological tissue. Phantom development is described in detail. The images for hardware setup and acquisition parameters are well-specified and clear.

Response 5:

Authors agree that discussion about phantom limitations is pertinent, so different comments were added to the text about the phantom used. In first place, at the beginning of the section 2.3, a sentence to describe the suitability of these gels to mimic biological tissues was added:

Lines 267-270

“That study highlighted the suitability of this gel for phantom fabrication due to its similarity in density and sound speed to biological soft tissues, the robustness of the resulting gels, and the possibility of tailoring their properties through the addition of specific additives to mimic different tissues.”

Some lines after, the limitation of these phantoms was commented:

Lines 298-302

“This customization yields isotropic gels with varying scatterer concentrations and attenuation properties. Although biological tissues often exhibit anisotropic behavior and boundary irregularities, experiments conducted with these phantoms may provide a preliminary assessment of the algorithm’s performance prior to further measurements with real tissues.”

Finally, in the conclusions section, the need of further studies to overcome these limitations was pointed:

Lines 689-692

“However, as noted above, the inherent anisotropy and boundary irregularities of biological tissues may affect the ability of the algorithms to accurately characterize them. Consequently, further studies on real biological tissues are required to fully assess the potential of the proposed methods.”

 

Comment 6:

Results and Discussion: The figures enforce the authors findings. There is good comparison of attenuation and μ parameter performance. More insight and detail on why these results are important within the larger area of this field of study would be beneficial in the Discussion.

Response 6:

Now it is commonly accepted by the ultrasound research community that B-scans represent a first qualitative approach, which can be complemented by QUS methods. As a consequence, and following the reviewer suggestion, authors highlighted the improved sensitivity of multiparametric QUS in comparison to previous simpler approaches providing only one parameter, which was added to the discussion of figure 10.

Lines 587-590

“Based only on attenuation images, it’s hard to distinguish materials 2 and 3. The same happens with materials 1 and 3 if only the μ parameter was analysed. This points out the higher efficiency of multiparametric quantitative analysis related to simple models based only on attenuation or μ parameter”.

 

We have also highlighted at the conclusions section, that these bi-parametric images can be obtained with just a single model using the approach presented, simplifying postprocessing tasks:

Lines 684-687

“This approach was used to enhance the information yielded by ultrasound scanning through the generation of multi-modal images based on the attenuation coefficient and clustering of scatterers (by using the single model presented in this work), and the conventional tissue echo envelope.”

 

Comments 7:

Conclusion: The conclusion summarises key contributions clearly, reiterating advantages of the proposed methods. There is a need for the authors to acknowledge that the results might have limitations and that further real-world studies are essential to validate clinical utility.

Response 7:

This issue was already addressed, as explained above in the commentary about phantoms.

Comments 8:

 

Comments on the Quality of English Language. Overall, the manuscript is clear and professional. There are some sentences which could be shortened to improve readability and impact. Minor language changes and sentence structure could help improve flow.

 

Response 8:

This issue was already addressed above, at the Introduction section.

Author Response File: Author Response.pdf