emg2vec: Self-supervised Pretraining in Electromyography-based Silent Speech Interfaces.

Silent speech interfaces (SSI) enable the generation of audio speech or readable texts without vocalization. Electromyography (EMG), being one of the possible source signals of SSI, demonstrates its superiority, particularly for individuals with vocal organ injuries. In this work, we propose a self-pretraining framework, i.

Diff-ETS: Learning a Diffusion Probabilistic Model for Electromyography-to-Speech Conversion.

Electromyography-to-Speech (ETS) conversion has demonstrated its potential for silent speech interfaces by generating audible speech from Electromyography (EMG) signals during silent articulations. ETS models usually consist of an EMG encoder which converts EMG signals to acoustic speech features, and a vocoder which then synthesises the speech signals. Due to an inadequate amount of available data and noisy signals, the synthesised speech often exhibits a low level of naturalness.

Cross-Speaker Training and Adaptation for Electromyography-to-Speech Conversion.

Surface Electromyography (EMG) signals of articulatory muscles can be used to synthesize acoustic speech with Electromyography-to-Speech (ETS) models. Recent models have improved the synthesis quality by combining training data from multiple recordings of single speakers. In this work, we evaluated whether using recordings of multiple speakers also increases performance and if cross-speaker models can be adapted to unseen speakers with limited data.

Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction.

Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes.

Towards clinical-dose grating interferometry breast CT with fused intensity-based iterative reconstruction.

X-ray grating interferometry CT (GI-CT) is an emerging imaging modality which provides three complementary contrasts that could increase the diagnostic content of clinical breast CT: absorption, phase, and dark-field. Yet, reconstructing the three image channels under clinically compatible conditions is challenging because of severe ill-conditioning of the tomographic reconstruction problem. In this work we propose to solve this problem with a novel reconstruction algorithm that assumes a fixed relation between the absorption and the phase-contrast channel to reconstruct a single image by automatically fusing the absorption and phase channels.

Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior.

Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging.

Intensity-based iterative reconstruction for helical grating interferometry breast CT with static grating configuration.

Grating interferometry breast computed tomography (GI-BCT) has the potential to provide enhanced soft tissue contrast and to improve visualization of cancerous lesions for breast imaging. However, with a conventional scanning protocol, a GI-BCT scan requires longer scanning time and higher operation complexity compared to conventional attenuation-based CT. This is mainly due to multiple grating movements at every projection angle, so-called phase stepping, which is used to retrieve attenuation, phase, and scattering (dark-field) signals.

INSIDEnet: Interpretable NonexpanSIve Data-Efficient network for denoising in grating interferometry breast CT.

Purpose: Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft-tissue contrast. Grating interferometry breast computed tomography (GI-BCT) is a promising X-ray phase contrast modality that could overcome these limitations by offering high soft-tissue contrast and excellent three-dimensional resolution.