• A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech

      Woo, Jonghye; Xing, Fangxu; Prince, Jerry L; Stone, Maureen; Gomez, Arnold D; Reese, Timothy G; Wedeen, Van J; El Fakhri, Georges (Elsevier B.V., 2021-06-12)
      Intelligible speech is produced by creating varying internal local muscle groupings—i.e., functional units—that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods. Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability. © 2021
    • Dual-Cycle Constrained Bijective VAE-GAN for Tagged-to-Cine Magnetic ResonanceE Image Synthesis

      Liu, Xiaofeng; Xing, Fangxu; Prince, Jerry L; Carass, Aaron; Stone, Maureen; El Fakhri, Georges; Woo, Jonghye (IEEE, 2021-05-25)
      Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.
    • Floor-of-the-Mouth Muscle Function Analysis Using Dynamic Magnetic Resonance Imaging

      Xing, Fangxu; Stone, Maureen; Prince, Jerry L; Liu, Xiaofeng; El Fakhri, Georges; Woo, Jonghye (SPIE, The International Society for Optical Engineering, 2021-02-15)
      To advance our understanding of speech motor control, it is essential to image and assess dynamic functional patterns of internal structures caused by the complex muscle anatomy inside the human tongue. Speech pathologists are investigating into new tools that help assessment of internal tongue muscle's cooperative mechanics on top of their anatomical differences. Previous studies using dynamic magnetic resonance imaging (MRI) of the tongue revealed that tongue muscles tend to function in different groups during speech, especially the floor-of-the-mouth (FOM) muscles. In this work, we developed a method that analyzed the unique functional pattern of the FOM muscles in speech. First, four-dimensional motion fields of the whole tongue were computed using tagged MRI. Meanwhile, a statistical atlas of the tongue was constructed to form a common space for subject comparison, while a manually delineated mask of internal tongue muscles was used to separate individual muscle's motion. Then we computed four-dimensional motion correlation between each muscle and the FOM muscle group. Finally, dynamic correlation of different muscle groups was compared and evaluated. We used data from a study group of nineteen subjects including both healthy controls and oral cancer patients. Results revealed that most internal tongue muscles coordinated in a similar pattern in speech while the FOM muscles followed a unique pattern that helped supporting the tongue body and pivoting its rotation. The proposed method can help provide further interpretation of clinical observations and speech motor control from an imaging point of view.
    • Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis

      Liu, Xiaofeng; Xing, Fangxu; Stone, Maureen; Zhuo, Jiachen; Reese, Timothy; Prince, Jerry L.; El Fakhri, Georges; Woo, Jonghye (Springer Nature, 2021-09-21)
      Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-training UDA for generative tasks, such as image synthesis, is not fully investigated. In this work, we propose a novel generative self-training (GST) UDA framework with continuous value prediction and regression objective for cross-domain image synthesis. Specifically, we propose to filter the pseudo-label with an uncertainty mask, and quantify the predictive confidence of generated images with practical variational Bayes learning. The fast test-time adaptation is achieved by a round-based alternative optimization scheme. We validated our framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis problem, where datasets in the source and target domains were acquired from different scanners or centers. Extensive validations were carried out to verify our framework against popular adversarial training UDA methods. Results show that our GST, with tagged MRI of test subjects in new target domains, improved the synthesis quality by a large margin, compared with the adversarial training UDA methods.