Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The manuscript describes the use of two convolutional neural network approaches for correction of motion artifacts in structural 3D MR images. This is a worth-while approach, since motion affects interpretability of the images as well as performance of post-processing algorithms. Previous work has dealt with this issue and is not fully cited in this abstract (like Kristof Meding, Alexander Loktyushin, Michael Hirsch: Automatic detection of motion artifacts in MR images using CNNS, ICASSP 2017 - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), which use a similar approach). The paper is clearly written and easy to understand. The quality is mixed. While the authors seem to have a good understanding of machine learning approaches and similary measures they seem to lack understanding of MRI. Simulated motion artifacts are only partially realistic. 

The example with real-world motion (fig.4) seems to be a good pick, which worked well. Nonetheless, both CNN models remove a lot of information relevant for diagnostic purposes from the white matter signal.

It also remains unclear how the performance of the described approach would be in case of strong motion.

At which step was the segmentation of grey and white matter performed for the real motion affected MRI data? Before or after motion compensation? How meaningful would this be?

Pros:
- use of 3D patches instead of 2D
- nice use of adversarial CNN

Cons:
- use of magnitude images doesn't allow reconstruction of k-space, thus motion simulation is somewhat unrealistic
- used motion patterns (translation within axial plane) is not realistic. Often, nodding motion will occur, which is not captured in this approach. The examples, however, show mild rotational motion around z-axis ("no" motion), which can be easily approximated by inplane translation.
- selection of similarity measures does not show better (?) performance of the HR3DNetGAN approach.

review_title: The paper describes a deep learning approach for motion compensation of structural 3D MR images. While the manuscript describes a valid approach using convolutional neural networks, it would be appreciated if the approach would be better evaluated using real world motion patterns and data.

final_score: rating: 3: Marginally above acceptance threshold
review_content: The proposed methodology illustrates the application of two 3D convolutional neural network for motion artifacts estimation and correction in structural 3D MR images. The methodology is quantitatively and qualitatively evaluated in an appropriate manner.
 
What type of clinically significant information is lost during reconstruction? Some discussion would be useful.
 
Pros:
- Use of 3D CNN and GAN for motion correction
- Clearly written
 
Cons:
- It is not clear how the proposed approach differs from the one published in "Automatic detection of motion artifacts in MR images using CNNS, ICASSP 2017".
- The methodology is evaluated on simulated data. How well does the simulated data reflect real life motion artifacts.

review_title: Application of 3d CNN and GAN for motion estimation and artifact correction, clearly written and well organized, Evaluation done on simulated data, Discussion should include clinically significant information loss; Borderline Accept

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The author presents a novel motion correction method for brain images using 3D CNN based regression. The method is novel, the results are promising and comparison to GANs is interesting. Overall the paper is well illustrated and written. Some additional validations would make the paper even better 

1.  How does the model compare to traditional registration based methods like Nifty-Reg/ANTs for motion correcttion?
2.  What is the error metric for tissue similarity for 3D CNN regression model compared to a template brain image selected from the training image?
review_title: This paper investigates the use of GANs and CNN regression for motion correction of brain images. 

Decision: Poster