Next Article in Journal
Improving the Performance of Open-Set Recognition with Generated Fake Data
Next Article in Special Issue
Sinogram Upsampling via Sub-Riemannian Diffusion with Adaptive Weighting
Previous Article in Journal
Incentive Public Auditing Scheme with Identity-Based Designated Verifier in Cloud
 
 
Article
Peer-Review Record

Optimization of the Algorithm for the Implementation of Point Spread Function in the 3D-OSEM Reconstruction Algorithm Based on the List-Mode Micro PET Data

Electronics 2023, 12(6), 1309; https://doi.org/10.3390/electronics12061309
by Jie Zhao 1,†, Yunfeng Song 1,†, Qiong Liu 2,†, Shijie Chen 1 and Jyh-Cheng Chen 1,3,*
Reviewer 1:
Reviewer 2:
Electronics 2023, 12(6), 1309; https://doi.org/10.3390/electronics12061309
Submission received: 7 February 2023 / Revised: 27 February 2023 / Accepted: 7 March 2023 / Published: 9 March 2023
(This article belongs to the Special Issue Advances in Biomedical Imaging and Processing)

Round 1

Reviewer 1 Report

In this article, the authors demonstrate an algorithm to improve the imaging quality of positron emission tomography by first measuring the point spread function of the machine. Utilizing this information, the resolution of the image can be improved. The method proposed in this paper is reasonable, and it has been well-accepted in the field of optical microscopy. However, the improvement of imaging quality demonstrated seems not as significant, especially in the animal example. Detailed comments are given below.

1.       In lines 373 to 376, the authors wrote “Fig 9 (b) clearly shows that the mouse's 373 brain, heart, bladder, and other organizational structures have higher contrast than those 374 in Fig 9 (a), i.e., the structures and their "functional" morphology are more pronounced 375 than those shown in Figure 9 (a).” However, from the results in Figure 9, no obvious feature difference can be noticed, and the authors didn’t offer quantitative or statistical comparisons for images acquired from different algorithms.

2.       For all the demonstrations (phantom and animal), the authors only show 2D images instead of 3D models, which makes it hard to interpret how the PSF-OSEM algorithm performs better than the 3D-OSEM algorithm in three-dimensional structure characterizations.

3.       In lines 89, 90, 93, 95, and 237, the author wrote “22Na”. The “22” should be superscript to avoid potential misunderstanding.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The core of this study is judged to be point spread function estimation and PET image restoration based on it.

The following issues need to be addressed.

 

- Analysis of existing studies is lacking. Image blur can be caused by various factors such as optical, motion, and scattering. It is necessary to supplement the existing literature related to this (especially in the field of bio).

 

- In this study, the point spread function is assumed to be a Gaussian function. However, other existing studies model the PSF with various basis functions depending on the blur factor. You should be able to give reasons for assuming a Gaussian function and be able to justify it.

 

- Eventually, the deconvolution concept must be applied to restore the image through the estimated PSF. Which deconvolution method was applied? [20] Is the iterative approach of the paper based on the principle of deconvolution?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Back to TopTop