![]() ![]() In brief, the authors propose to utilize conditional generative adversarial networks to generate diffusion gradient volumes in in directions that are not present in the given sparse set once the model has been trained. What is the ranking of this paper in your review stack?.Possibly a bit niche for oral presentation, although I guess the general approach could have applicability in other quantitative imaging areas. Timely work solving a long-standing limitation in a popular topic. What were the major factors that led you to your overall score for this paper? Please state your overall opinion of the paper.I’m unsure what they mean by this and how their approach differs in this aspect - other methods that synthesise diffusion MRI according to a particular protocol can still feed into downstream processing. My only additional comment is a minor presentational issue, which is that the authors say several times that “downstream usages such as fiber tractography and diffusion orientation distribution function (dODF) estimation are precluded with” other methods. The weakness I highlight above is common among work on this topic and not a showstopper, but rather an area for future work, as the authors acknowledge. Clearly presented and with convincing results. This is a very nice solution to a long-standing limitation. Please also refer to our Reviewer’s guide on what makes a good review: Please provide detailed and constructive comments for the authors.Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance Note, that authors have filled out a reproducibility checklist upon submission. Please comment on the reproducibility of the paper.Please rate the clarity and organization of this paper.Only trained on health subjects so far and no test of generalisability to unseen content such as pathology. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work. Please list the main weaknesses of the paper.The paper solves a long-standing limitation of non-model-based diffusion weighted image generation techniques. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting. Please list the main strengths of the paper you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work.In addition the generator can be conditioned on existing diffusion weighted scans enabling it to complete sparse or partial acquisitions. The unique aspect is that the diffusion weighting parameters can be arbitrarily specified at test time without retraining the model. The paper presents an image synthesis algorithm for generating diffusion weighted images from standard structural MRI scans (T1w, T2w). Please describe the contribution of the paper.Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Moreover, this approach enables downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Our translation network linearly modulates its internal representations conditioned on continuous q-space information, thus removing the need for fixed sampling schemes. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary q-space sampling given commonly acquired structural images (e.g., B0, T1, T2). Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. However, they implicitly make unrealistic assumptions of static q-space sampling during training and reconstruction. Mengwei Ren, Heejong Kim, Neel Dey, Guido GerigĬurrent deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. Paper Info Reviews Meta-Review Author Feedback Post-rebuttal Meta-Reviewsīack to top List of papers List of papers - by topics Author List Paper Info ![]()
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