ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2022
CMRI2SPEC: CINE MRI SEQUENCE TO SPECTROGRAM SYNTHESIS VIA A PAIRWISE HETEROGENEOUS TRANSLATOR.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Speech Motion Anomaly Detection via Cross-Modal Translation of 4D Motion Fields from Tagged MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Synthesizing Audio from Tongue Motion During Speech Using Tagged MRI Via Transformer.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Multimodal representation learning using visual movements from cine magnetic resonance imaging (MRI) and their acoustics has shown great potential to learn shared representation and to predict one modality from another. Here, we propose a new synthesis framework to translate from cine MRI sequences to spectrograms with a limited dataset size. Our framework hinges on a novel fully convolutional heterogeneous translator, with a 3D CNN encoder for efficient sequence encoding and a 2D transpose convolution decoder. In addition, a pairwise correlation of the samples with the same speech word is utilized with a latent space representation disentanglement scheme. Furthermore, an adversarial training approach with generative adversarial networks is incorporated to provide enhanced realism on our generated spectrograms. Our experimental results, carried out with a total of 63 cine MRI sequences alongside speech acoustics, show that our framework improves synthesis accuracy, compared with competing methods. Our framework thereby has shown the potential to aid in better understanding the relationship between the two modalities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.