Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 1: Strong reject
review_content: summary: This paper proposes a warmstart strategy for DIP network initialization for MPI reconstruction. In particular, for a series of MPI scans, this paper proposes to initialize each subsequent image’s reconstruction network weights with the learned weights of the previous scan. The goal is for this strategy to reduce the number of DIP optimization steps required to optimize each image. This strategy is tested on a sequence of 5 phantom MPI scans comprised of a triangle which is shifted twice, transformed to a circle, and transformed back to a triangle. The proposed initialization approach improves reconstruction time for 3 of the 4 frames for which it is used (two of which are translations from the previous frame), and increases reconstruction time for the other frame.
strengths: - **The paper is well-written and easy to understand.** The exposition on DIP is very clear and would be easy for a newcomer to the field to read and understand.
- **The idea behind this paper suggests an interesting question**: when should we start off with more specific priors on the image than those provided by DIP, and how detailed should they be? The discussion section contains interesting hypotheses toward the first question (I'd be really interested in a paper which developed a good similarity metric in the raw measurement space). For the second, I'm curious, for example, if applying the warm-starting approach developed here only to a subset of layers could improve the quality of the reconstructions. With this strategy, for more complex images, the networks could in theory share basic information about low-frequency structures which are unlikely to change from image to image, while allowing finer-grained structure to be reconstructed from scratch.
weaknesses: - **The experiments are performed on only one series of 5 scans with very simple phantoms, which cannot shed light on how well this method performs for more realistic images.** For more realistic anatomical images, the relevant differences are defined by finer scale image features; these will likely require a higher SSIM threshold to declare an image adequate. In that setting, a worry is that the warm-started networks get “stuck” close to the initialization, which provides a reasonably low value for the data discrepancy term to begin with, but are unable to move out of that optimum to the one corresponding to the actual acquired image. Overall, given that the method is quite straightforward and does not introduce a new conceptual idea, more sophisticated experiments on real data are needed to demonstrate that this method makes a big difference.

- **The proposed method increases the number of iterations required for one of the two frames where the shape of the reconstructed phantom changes qualitatively from the previous frame**. It is hard to argue that this method is working well since it can’t handle this case. The directions described in the “Discussion” for deciding when to apply a warm-starting method are interesting and seem to be a good starting point for future work to improve the reliability and usability of this method.
detailed_comments: - pg. 3 “The former is usually chosen to be the l1 or the l2 distance while for the later several terms…” —> “The former is usually chosen to be the l1 or the l2 distance, while for the latter, several terms…”
- pg. 3 “When choosing an appropriate regularizer, (3) delivers high quality…”: I believe the should reference Eq. (2).

recommendation: []
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: The paper proposes a warmstart approach for DIP reconstruction in the case where one tries to reconstruct highly correlated samples between the time frames. I find the method and the experiments overly simplistic, and does not add much value to the existing literature in its current form. In order to facilitate the impact, the authors should perform much more experiments, and also compare with the existing works.
strengths: The method is sound, and simple to implement, probably within 1-2 lines of code. Warmstarted version of DIP shows clear advantage over Coldstarted version, which is expected. The paper is rather easy to follow and understand.
weaknesses: 1. The experimental data is so simple that it is hard to verify if the method will actually be valid on real-world datasets. The authors should try to implement the idea using more data that contain more complicated structure, e.g. in vivo data.

2. The only methodological comparison the authors provide is the comparison with coldstarted DIP. Adding to this comparison, the authors should also consider comparing with the existing approach for using DIP for dynamical imaging [1]. Yoo et al. show that it is indeed unnecessary to train DIP on individual timeframe. What advantage does the proposed method have over [1]?

3. Figure 2 is not really convincing. There is only a single frame where the warmstarted version shows better performance than the coldstarted version once the optimality is reached. However, for Frame 5, the convergence is actually faster when using the coldstarted DIP. The results do not seem robust, and this may be correlated with the concern that I have with the overly simplistic data.

Reference
[1] Yoo et al. "Time-dependent deep image prior for dynamic MRI", IEEE TMI, 2021.
recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: The work proposes a warmstart approach which helps in initialization of the weights and hence improves the reconstruction time for the dynamic tomography scenarios. It also shows a comparison with the coldstart method and shows that the times are improved considerably using the deep image prior in the field of dynamic tomography.. 
strengths: The strength of the paper lies in the application of warmstart approach for improving the reconstruction time using deep image priors for dynamic tomography. A comparison with the coldstart approach has also been shown which clearly shows the benefits of using such an approach.
weaknesses: Since it is a methodological paper I will assume that there will be new insights into the method developed. Using a good initialization is very common for getting faster solutions, how can this impact the other scenarios where other techniques for initialization are used.
detailed_comments: 1. There is a problem with finding the best architecture and the stopping criteria always for the DL models. How is it different here in this case. Please clarify.

2. Page 3 mentions the use of Equation 3 before defining it, I believe it should be equation 2.

3. As mentioned that the architecture will influence the time taken for reconstruction. How the different strategies work for different type of architectures. Any insights in this will be helpful.

4. The strategy of using a warmstart is the most obvious technique which can be used, can you please show how it is better from some pre-defined initialization for the models.

5. Figure 3 caption repeats the phrase - three rows, 2 times. Please proofread it and check these mistakes.
recommendation: Poster
review_title: null

Meta-review: metareview: The paper has received three detailed reviews and enjoyed a lively discussion during review/response. The central criticism of all reviewers prior to revision was the simplicity of the data used to demonstrate the method as well as the limited choice of baselines. After response, especially due to the inclusion of an experiment on in-vivo data, all reviewers have raised their review scores, skewing marginally towards acceptance. 
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)