Venue: MIDL 2023 Conference

Reviews:

final_score: rating: 5: Strong accept
review_content: summary: This paper proposes a model-based method to solve the inverse problem of recovering the image acquisition parameters (IAPs) that can create an MR image only using the image. Instead of directly predicting the IAPs, the model first predicts 12 attributes that are closely related to IAPs, and then uses these attributes to regress the IAPs. This framework not only can enhance the accuracy of IAPs prediction, but also improve the interpretability of the model. More importantly, the generated MR images significantly benefit cancer classification compared with baseline methods.
strengths: + This paper is well written. Motivation, method, experiment, result, and practical impact are clearly described.
+ The results are well presented, not only showing better MR images and IAP predictions, but also improved cancer classification performance.
weaknesses: No major weakness was detected. Minor concerns are as follows.

- Collecting these 12 attributes requires effort. Are these attributes all important in predicting IAPs? I saw some of the predictions can reach nearly perfect accuracy (Table 1), suggesting that these attributes might be very easy to be determined based on the image. A study on the importance of each attribute to the IAPs prediction is suggested to included (e.g., PCA analysis).
recommendation: Oral
review_title: null

final_score: rating: 1: Strong reject
review_content: summary: The method proposes to predict image acquisition parameters from input MRI data, with the objective of enabling downstream data selection and task forwarding. Methodologically, the paper is water tight, but this does not compensate the limited validation (in the sense that it is only applied to one body part, modality, and site) and the weak premise (IAPs are in the DICOM header). 
strengths: - Good method that clearly performs well on the chosen dataset
- Wide choice of IAPs demonstrate that the method can have broad applicability
- The performance analysis, including looking at different TopK percentages and the tails of the performance distribution, is strong.
weaknesses: - Method is applied only to 2D slices and using 2D networks. Medical imaging data (and breast MRI) is normally 3D. 
- Method is applied only to one body part, modality, and site
- The premise of the model (we do not know the IAPs) is not really a clinically relevant problem (DICOM has this info) or a research problem (as the protocol is defined ahead of time).
detailed_comments: This is a clearly good method that is well developed and performs well on the chosen dataset. The wide choice of IAPs demonstrate that the method can have broad applicability and the overall validation is quite strong, including the part where the authors look at the tails of distributions and system robustness. 

However, the paper has major weaknesses, which I expand below:
- Method is applied only to 2D slices and using 2D networks, but medical imaging data, and primarily breast MRI, is normally 3D. This means predictions are done on a per-slice basis, meaning that there will be between-slice prediction differences (which does not make sense for 3D acquisitions). Also, 2D networks are much easier to train due to their size and parameter counts, meaning that the reader is left to guess if they would perform well in 3D.
- Method is applied only to one body part, modality, and site. The title, on the other side, is very broad. One would expect this to be applied to several body regions and different modalities (to test for generalisability), and to different data sources (to test for population and acquisition biases). Without this, the paper is not really of value to many researchers. 
- The premise that we do not know the IAPs is not really a clinically relevant problem, as DICOM has this info. In a clinical setting, DICOM images HAVE to have this information otherwise they would be invalid. Authors can argue that this info can be wrong (it happens) but then the training data used in this paper (the target IAPs) might also be wrong. The authors can also argue that this can be applied to a research setting, but again, in a research cohort, the protocol is defined ahead of time. If this is to be applied to random open-source images, then the authors would need to demonstrate that the model generalises to many more sites and data sources. 
recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: This work proposed a deep learning method for breast DCE MRI image acquisition parameters prediction. Both categorical and continuous acquisition parameters were predicted, most of which with high prediction accuracies. The proposed method was leveraged by a downstream breast cancer classification task to improve classification accuracy.
strengths: Complete MR acquisition parameters are useful for efficient harmonization/adaptation, so the acquisition parameter prediction task this paper tried to tackle has significant value for the community. Besides, promising prediction accuracies showed potential of the proposed method. 
weaknesses: A major weakness of this paper is that some of the acquisition parameters may not be the appropriate targets to predict, such as “FOV Computed” and  “Acquisition Matrix”, and it is unclear how the image resizing preprocessing affect the acquisition parameters to predict.
recommendation: Oral
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: The authors train a deep learning network to infer acquisition parameters from MRI 2D images. The parameters include scanner type, slice thickness, acquisition resolution, etc…, with both categorical and continuous values. The cost function is therefore a combination of categorical cross-entropy and mean squared error for real values.
strengths: The system works, with high accuracy in categorical parameters and low errors on real parameters. The application to different models trained on images from different vendors is interesting. The paper is well written and easy to follow. The evaluation is correct. 
weaknesses: While the authors claim that many of the acquisition parameters are not available, it is standard to include them as part of the DICOM headers. For example, patient position, slice thickness, fov, scanner manufacturer and model have their own DICOM fields and are filled by any acquisition device. 
recommendation: Poster
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: The authors design and train a model that predicts image acquisition parameters (IAPs) of MRI slices, such as field strength, echo and repetition times used for the sequence, and contrast agent types, but also more abstract parameters such as the manufacturer of the used scanner. The model achieves accurate predictions, and the authors demonstrate several areas where such a model could be useful in the field.
strengths: The problem is well-defined and the authors detail various use cases which shows the merit of the paper and the potential value of their proposed model. I agree with the authors that the work is an important step in domain adaptation of medical imaging data.
weaknesses: I think the model design could be improved to take into consideration the relationship between the IAPs. The variations in the training dataset should also be described in more detail, as in this form, it is hard to tell if the data contained sufficient enough samples.
detailed_comments: - The categorization of the IAPs makes the training of the model more straightforward and the results easy to interpret, however they would hide outlier IAPs not included in these categories (ie. using an image from a new MRI scanner not included in the categories would still be categorized as one of the scanners included). As the model would be used to remove uncertainties and uncover unknown information about the images, it would be interesting to explore some uncertainty metrics for the model, for example Monte-Carlo drop-out. This would perhaps show that although the slice thickness and FOV have lower accuracies, they probably also have larger uncertainties, as---how the authors have also stated,---there are only slight differences between neighboring categories.
- The range of TE and TR values are relatively small. I would recommend including a wider range of values for both training and evaluation.

recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: This paper introduces a neural network model that can accurately predict image acquisition parameters (IAPs) from MRI scans with a single forward pass. These IAPs include field strength, echo and repetition times, acquisition matrix, scanner model, scan options and contrast agent type. The authors conducted experiments to test the accuracy of their model in predicting these parameters on many MRI scans of new patients. This research is important for understanding how deep learning models can be applied to medical images by better understanding the relationship between image appearance and IAPs as well as helping with domain shift, domain adaptation and data harmonization.
strengths: - the authors have developed a neural network model that can accurately predict image acquisition parameters from MRI scans with a single forward pass. This is an important step towards better understanding the relationship between image appearance and IAPs which could help in areas like domain shift, domain adaptation and data harmonization. 
- they conducted experiments to test how well their model can predict these parameters on many MRI scans of new patients which demonstrates its potential value to the community. 
- the paper is well written
weaknesses: - the method has been evaluated only on breast MRI. This is a very specific setup and findings might not generalise to other sequences. 
- how does the acquisition coil setup influence the prediction? Can you predict the used coil if you had that data?
- some parameters seem trivial, e.g. patient position but others might be influenced by patient presentation. How do the indiviudal parameters correlate with downstream model choice in Table 2?

detailed_comments: I found no typos and the paper seems sound and reproducible. It seems like an interesting proof-of-concept study. 
recommendation: Poster
review_title: null

Meta-review: metareview: This paper received mixed reviews, and I have read all reviews and recommend that it be accepted.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)