PONE-D-24-04756Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasetsPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: - The overall length of the background is adequate; however, this section needs significant improvement. P3L47-53 is redundant, and it's generally not stylistically pleasing to have only two references, one of which is repeated in two different sentences.

- What is the role of DLIR? Why is it better than iterative reconstructions? The latter have been used in both CCTA and calcium scoring, but they have intrinsic flaws and have not been mentioned at all.

- 10.1097/RTI.0000000000000340

- 10.1016/j.ejrad.2017.03.011

- 10.1007/s00330-019-06359-6

- 10.2214/AJR.10.4285

- Recent publications on this specific topic (DLIR and CCTA) have been overlooked. They could increase the value of the introduction or discussion. Please consider adding them.

- 0.1007/s11547-023-01607-8

- 10.1016/j.jcct.2020.01.002

- 10.1007/s00330-021-08367-x

- 10.1007/s00330-021-08424-5

• A paragraph on image acquisition must be written with all the information to be reproducible. Information on the injection of contrast media must also be provided.

• Inclusion and exclusion criteria must be clearly stated.

• What was used as the muscle parameter in patients with single ventricle? In these patients, the current CNR formula may not have been applicable.

• This ERD parameter is kind of obscure to me, I suggest performing a "traditional" subjective image quality analysis like almost every radiology manuscript does, readers are surely more familiar with it.

• Results: please double-check the number of patients: the sum of different pathologies is greater than the total, and the number of patients with "other diseases" is also missing.

• Results: simply indicate the actual p-values, not < 0.05: it is implicit that a statistically significant value is less than 0.05.

• "Compared to recent studies using DLIR, this study has the advantage of conducting a more objective assessment of pediatric cardiac CT scans in a larger population." Authors cannot write such a statement without providing any citation. What are these studies? What is their population? Why would this study be more objective? I suggest toning down statements like these.

• In the discussion, there are many general statements without citations, and the results are not properly discussed, as they should be.

Reviewer #2: Although I have fully confirmed the authors' efforts for the results of this study, there are several maor comments.

1. ASiR-V and DLIR are thought to be different kinds of image reconstruction methods. However, the authors did not make a distinction between these two reconstruction methods. For example, in Table 2, the authors would need to separate DLIR and ASiR-V.

2. I would like to emphasize in the purpose of the paper what differentiates it from existing studies. In fact, there is a paper that is very similar to this paper. ( Yoon et al. Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction, BMC Medical Imaging, 2021)

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Reviewer #1: No

Reviewer #2: No

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