Venue: MIDL 2018 Conference

Reviews:

final_score: rating: 3: Marginally above acceptance threshold
review_content: 
Quality & Clarity

#1. This paper is well organized.
#2. Experimental results are well organized and interesting.
#3. The detailed analysis have been described.

Originality & Significance

(+) Although it is an existing method, it have been applied to newly defined problems.
(-) Authors have evaluated the proposed algorithm with small scale test set.
review_title: This paper presents the reconstruction method of 3D non-contrast CT from 4D CT by using stacked bidirectional convolutional LSTMs. Although authors have experimented on the small scale dataset, the problem-solving approach is novel and the experimental results are interesting.

final_score: rating: 4: Top 50% of accepted papers, clear accept
review_content: The author aim at extracting non-contrast CT (and potentially CT angio) from CT perfusion scans. They follow a generalized LSTM approach to track the changes resulting from the tracer dynamics. Good validation.

Pro: Innovative application for 'modality synthesis'. Successfully illustrates 4D learning of CT tracer dynamics which can be considered a hard learning problem. Use of C LSTM is novel. 

Con: In theory, one can set up perfusion scans that contain native CT and CTA naturally as part of their 4D sequence - although at a very high radiation dose for the patient.  To this end perfusion often has low spatial or temporal resolution - or even field of view - in the clinical setting, and CTA and native CT do offer complementary information that is not contained (at that resolution, contrast, or detail) in the clinical perfusion data. 
review_title: C LSTM for modality synthesis

Decision: Oral