Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative\n Adversarial Network
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is being increasingly utilized to assess,\ndiagnose, and plan treatment for a variety of diseases. The ability to\nvisualize tissue in varied contrasts in the form of MR pulse sequences in a\nsingle scan provides valuable insights to physicians, as well as enabling\nautomated systems performing downstream analysis. However many issues like\nprohibitive scan time, image corruption, different acquisition protocols, or\nallergies to certain contrast materials may hinder the process of acquiring\nmultiple sequences for a patient. This poses challenges to both physicians and\nautomated systems since complementary information provided by the missing\nsequences is lost. In this paper, we propose a variant of generative\nadversarial network (GAN) capable of leveraging redundant information contained\nwithin multiple available sequences in order to generate one or more missing\nsequences for a patient scan. The proposed network is designed as a\nmulti-input, multi-output network which combines information from all the\navailable pulse sequences, implicitly infers which sequences are missing, and\nsynthesizes the missing ones in a single forward pass. We demonstrate and\nvalidate our method on two brain MRI datasets each with four sequences, and\nshow the applicability of the proposed method in simultaneously synthesizing\nall missing sequences in any possible scenario where either one, two, or three\nof the four sequences may be missing. We compare our approach with competing\nunimodal and multi-modal methods, and show that we outperform both\nquantitatively and qualitatively.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".