Windowed Eigen-Decomposition Algorithm for Motion Artifact Reduction in Optical Coherence Tomography-Based Angiography

Round 1

Reviewer 1 Report

The paper claims to propose a windowed eigen-decomposition algorithm to reduce motion artifacts of the Optical coherence tomography-based angiography (OCTA) that optimized using normal distribution. In this work, a windowed eigen-decomposition algorithm is applied for contrasting the blood-flow signals and reducing motion artifacts. The average PSNR and time registration of the work is evaluated with different windows sizes and compared with of different OCTA reconstruction algorithms. The flowing points must be included:

·         The introduction section needs modification. Authors need to include clear contributions of this work and include the need for the proposed windowed eigen-decomposition algorithm.

·          The authors must separate related work from Introduction. The authors have not mentioned their real drawbacks or the need of the proposed model. This section must include recent works.

·         The author has to include the details about modules used in system architecture with precise functionality and roles.

·         The author has to include the details about the Implementation environment and tool details used for the proposed work.

·         The author has to include the equations of time registration.

·         The results discussion and justification of the findings need to be improved.

·         The author needs to look into recent works in the relevant optimization of motion artifacts.

Author Response

Please see the attachment. 

Author Response File: Author Response.docx

Reviewer 2 Report

OCTA has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA and therefore can be analyzed using diffferent algorythms like ED for OCTA reconstruction, but have limitations due to fact that data are  not normally distributed. Moving from there, authors propose moving window to the input dana to be used on ED, which can contrast the blood-flow signals with signifi cantly reduced motion artifacts.

 Methodology is clear and well documented including optimization (ideal window size by fitting the data distribution with the normal distribution) and validation (cross-sectional and en-face results compared among several OCTA reconstruction algorithms).

 

 Proposed approach brings novel angle in analysis although does not solve all issues. But, it can reduce motion artifacts, and improve performance compared to other selected algorithms, is quite robust and can be improved in future research.

 

Author Response

Authors appreciate the reviewer's careful examination of our manuscript and the valuable feedback provided. Thank you for taking the time and effort to review our work.

Round 2

Reviewer 1 Report

Authors have been addressed all my comments and the experimental results given by the author are also perfect.

 I recommend accepting this paper