Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior

Stefano van Gogh; Subhadip Mukherjee; Jinqiu Xu; Zhentian Wang; Michał Rawlik; Zsuzsanna Varga; Rima Alaifari; Carola-Bibiane Schönlieb; Marco Stampanoni

doi:10.1371/journal.pone.0272963

Peer Review History

Original SubmissionJune 1, 2022
27 Jun 2022 Decision Letter - Jude Hemanth, Editor PONE-D-22-15849Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven priorPLOS ONE Dear Dr. van Gogh, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Aug 11 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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We will update your Data Availability statement to reflect the information you provide in your cover letter. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: N/A ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: In this work the authors presented an iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. The combination of conventional methods and deep learning methods is an important topic. The presented work combines L-BFGS optimization scheme with a deep learning parameterized prior for iterative reconstruction for breast cancer imaging. The theoretical analysis provides the motivation of proposing PnP-LBFGS. The experimental results on simulated and real datasets confirm the advantages of the proposed method. This reviewer thinks that this paper could be improved in the following ways. 1. The compared baseline methods only include conventional methods. The literature has shown that the deep learning has shown better/competitive performance than iterative algorithms in some applications. Therefore, deep learning-based methods, such as post-processing FBP reconstructed images, should be considered as a competitive baseline. 2. I am wondering if the authors could provide the comparison of reconstruction times, which is a factor for clinical use. 3. In literature there are many works synergizing deep learning and iterative methods. A brief discussion on key references may be important to further highlight the novelty of the proposed methods. Reviewer #2: The manuscript “Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior” discusses an iterative procedure for phase-contrast tomography that make use of a neural network denoising step. The manuscript is generally well-written and clear, even if many aspects of the proposed strategy are not properly discussed. In addition, often, the authors make statements that are not properly justified or that would need at least some references. About these remarks from my side, more detailed information can be found in the attached pdf. In general: 1) I am not convinced the proposed approach can be called data-driven as the denoising part is associated with the model space (the images), not the data space; 2) In both the synthetic and the experimental tests, the proposed approach tends to remove many details (that are spatially consistent, so, probably not related to random noise, and that are connecting different subdomains in the reconstruction). My impression is that similar results could be obtained, for example, with a much simpler spatial filter or a more appropriate choice of the regularization within the framework of more "standard" approaches. This, of course, does not mean that the proposed approach is not interesting, but a more accurate and fair comparison with the “mainstream” approaches would make the paper more relevant to the readers. 3) A more in-depth discussion of the noise in the data would be important. Moreover, why no data covariance matrix is included in the inversion algorithm? It seems to me that the proposed strategy might be affected, for example, by severe modeling error that is not taken into account and that might lead to dangerous over-interpretation of the results. I do not expect the authors to modify their algorithm accordingly (at least for this manuscript), but I feel a short discussion about that would be important. I hope these comments might be helpful in further improving the quality of the paper. Best P.S. Authors must improve the quality of the images. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0272963.r001
Revision 1
22 Jul 2022 Author Response Response to Reviewers: Please find below our rebuttals to the editor’s and reviewers’ comments. We hope we could satisfactorily address the concerns. We highlighted the changes in red in the text. Editor’s comments: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf - We changed the figure labelling from Fig. to Fig and multiple figure labelling to Figs … and … - We justified the text - We changed the author affiliations - We added the corresponding author’s initials after the corresponding email address - We changed the last section’s name from conclusions to discussion Please amend your Methods section to include the ethics approval number included in your Ethics Statement. - We added the ethics statement In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability. Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter. - Due to legal restrictions, we are not allowed to share the patient data obtained with written informed consent under the ethical approval KEK-2012 554 granted by the Cantonal Ethics Commission of canton Zürich. We can however share our in-silico breast phantoms and the corresponding sinogram data. They will be uploaded to the ETH research collection archive as supporting material to our paper: https://www.research-collection.ethz.ch. The exact URL will follow. Reviewers’ comments: The compared baseline methods only include conventional methods. The literature has shown that the deep learning has shown better/competitive performance than iterative algorithms in some applications. Therefore, deep learning-based methods, such as post-processing FBP reconstructed images, should be considered as a competitive baseline. - We added the comparison to a deep learning-based post-processing step in the “Results” section and in the “Effect of the proposed data-driven regularizer” section. For the comparison to be fair, we used the same network as within the iterative reconstruction. We would like to stress that this comparison only makes sense when it is possible to analytically reconstruct the phase contrast volumes (which is not always the case, e.g. in Teuffenbach et al., 2017). - We accordingly edited Figs 9 and 10. I am wondering if the authors could provide the comparison of reconstruction times, which is a factor for clinical use. - We added a paragraph in the “Results” section describing the overall reconstruction times of the two iterative methods, as well as a more detailed description of the computational times of the single steps of the algorithms. In literature there are many works synergizing deep learning and iterative methods. A brief discussion on key references may be important to further highlight the novelty of the proposed methods. - We added a paragraph in the “Contributions” section that discusses prior work on combining deep learning with iterative methods and explained where the novelty of our method lies and why it is relevant to the scientific community. I am not convinced the proposed approach can be called data-driven as the denoising part is associated with the model space (the images), not the data space; - In the machine learning community, the term “data-driven” refers to algorithms that are fitted on training data, to differentiate them from algorithms that are solely designed by human engineering, and which do not contain trainable parameters. The fact that the mapping is applied in image space does not preclude the term data-driven in our opinion. In both the synthetic and the experimental tests, the proposed approach tends to remove many details (that are spatially consistent, so, probably not related to random noise, and that are connecting different subdomains in the reconstruction). My impression is that similar results could be obtained, for example, with a much simpler spatial filter or a more appropriate choice of the regularization within the framework of more "standard" approaches. This, of course, does not mean that the proposed approach is not interesting, but a more accurate and fair comparison with the “mainstream” approaches would make the paper more relevant to the readers. - The article already contains a comparison to the most celebrated and successful classical regularization scheme for CT, i.e. TV regularization. As it can be seen from the results in Figs 9 and 10 and in Table 1, TV regularization is not able to achieve comparable performance to the data-driven PnP strategy. - Moreover, the new comparison with FBP-denoising uses a CNN that is composed of spatial filters, which can thus be regarded as a spatial filter. - It is true that some small details are lost during the denoising. However, given the high amount of noise in the data and the very small size of the structures, we believe it is unrealistic to hope for those small features to be recovered. A more in-depth discussion of the noise in the data would be important. Moreover, why no data covariance matrix is included in the inversion algorithm? It seems to me that the proposed strategy might be affected, for example, by severe modeling error that is not taken into account and that might lead to dangerous over-interpretation of the results. I do not expect the authors to modify their algorithm accordingly (at least for this manuscript), but I feel a short discussion about that would be important. - We added a paragraph in the “DPC forward and backward tomographic operators” section in which we argue why we did assume constant variance in the data. The reason is that to accurately estimate the DPC variance, one needs to know the dark-field signal. Since the dark-field signal is hard to accurately compute based on highly noisy data, we decided not to include this into our model. We are currently working on a new algorithm which explicitly models the variance in the intensity data instead of the retrieved DPC data. P.S. Authors must improve the quality of the images. - All images were very high-resolution and all passed the PACE system test. Please let us know in case we must edit them. Attachments Attachment Submitted filename: Response_to_Reviewers.pdf https://doi.org/10.1371/journal.pone.0272963.r002
1 Aug 2022 Decision Letter - Jude Hemanth, Editor Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior PONE-D-22-15849R1 Dear Dr. Gogh We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Jude Hemanth Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response) Reviewer #2: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) Reviewer #2: N/A ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response) Reviewer #2: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response) Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) Reviewer #2: (No Response) ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0272963.r003
Formally Accepted
16 Aug 2022 Acceptance Letter - Jude Hemanth, Editor PONE-D-22-15849R1 Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior Dear Dr. van Gogh: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Jude Hemanth Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0272963.r004

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