Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: This paper presents methods to use CNNs to predict the motion directions from 2D CT angiography patches for motion compensation. The problem is formulated into a multi-value regression problem with 2D image patch input. The motion blurred data used for training are synthesized by the MC-FBP model. The methods were tested with six cases from an in-house dataset. Both qualitative and quantitative results were reported.

Pros:
The paper is well written and easy to follow.
The proposed methods are interesting and sound.  The results look promising.

Cons:
The experiments fall short of the comparison with the previous baseline methods. It is not clear how the paper compares to the previous literature.
The paper only considers motions in the 2D plane. It is not described why 3D motions are not considered for motion compensation, especially for curvilinear structures.
review_title: Well written interesting paper. Visual results look promising

final_score: rating: 3: Marginally above acceptance threshold
review_content: First of all, I find it important to note that I know little about motion artifacts in CT and about methods for their correction, apart from what I could learn from this paper and a very shallow examination of some of the references.

Strengths:
+ The method presented in the paper is novel and very different from the other methods for solving the same problem (as far as I can see from the literature review in the paper). It has an advantage of needing only one CT image compared to some of the other proposed methods.
+ The paper is written rather well and is interesting to read.
+ The proposed method works well in the majority of the cases.
+ Various aspects of the algorithm's performance were studied (the relationship between angle and strength of motion and network's error when estimating those parameters; artifact probability and severity predicted by a network and the angle and strength).
+ I clearly see that a lot of work has gone into this research project.

Weaknesses:
- The validation of the method is rather weak. Motion correction performance was evaluated only in 6 scans. and only qualitatively. The motion estimation performance in clinical data with simulated artifacts is validated only using 2 scans (as far as I understood, no cross-validation was performed). In the phantom data experiment (both images and motion is simulated), as far as I understood, only the case when the artery is perpendicular to z was considered. In all cases, only 2D motion was simulated. To sum up: 1) the algorithm was validation on only 6 real cases, 2) the validation on artificial data is not very extensive.
- The advantages of the method over a method that also only needs short scan range [6] are discussed but do not sound convincing to me. As I understood, those are: 1) [6] relies on raw projection data, 2) [6] is more computationally expensive. (Written in the introduction and discussion). I do not understand why relying on raw projection data is bad, since it should be always available. (The discussion says: "Furthermore, the dissociation from the raw projection data opens up additional applications beside motion compensation. For instance, utilization for motion model establishment should be considered. In general, reconstructed image data is less limited in scope than raw projection data." I do not understand what this means.) Regarding the second stated advantage: CNNs are also computationally expensive and require GPUs to train and apply them. If the authors wish to make an argument of computational efficiency, they should compare the running times, at least theoretical ones.
- No performance comparison with a similar method - e.g. [6]. (Though, I am not entirely sure they are comparable, and if they are not it should be explicitly stated in the paper.)
- What is the difference between the paper and [7-8]? I am going to guess that in [7-8] are only about artifact recognition, whereas this paper is about MVF estimation. I think it should be stated more clearly.
- The paper is rather involved and it might be possible to make it more understandable, especially for people who are new to the problem. Here are things that I am not sure I understand or I didn't understand:
1) When I hear "scan range" I think about spatial range (e.g. which body part is examined?). I think in the paper it mostly means duration of the scan. I think it should be clarified.
2) What is multi-phase and single-phase reconstruction?
3) What is "reconstruction direction"?
review_title: A very interesting and novel approach to correcting the coronary artery motion artifacts in CT. However, the validation of the method is very limited. The method was not empirically compared with any alternatives and theorethical advantages of it over alternatives are unclear to me.

final_score: rating: 3: Marginally above acceptance threshold
review_content: The authors investigated the feasibility of single-phase, image-based motion estimation by convolutional neural networks (CNNs). Based on simulated motion to artifact-free CT images, regression networks are trained to estimate underlying 2D motion vectors from axial coronary cross-sections. CNNs predict the motion direction and the motion strength with average
accuracies of 1.08◦ and 0.06 mm, respectively. This study looks very challenge and very important and experimental results are relatively extensive.

1. Why don't you compare mortion vector map of deformable registration with your CNN's estimation on voxel-by-voxel and evaluate the accuracy 
2. What's the reason of angle and strength paritioning? Is there any possibility to make another overfitting?
3. Considering 80x80 patch size, is it reasonable to assume that the there is only one motion direction in patch? Did you only concern patch's center motion?
4. How do you evaluate the gold standard of motion in clinical study?
5. Is is good enough to estimate motion in 2D patch in axial slices?
review_title: Review of Motion Estimation in Coronary CT Angiography Images using Convolutional Neural Networks 

Decision: Poster