PhD Position: Merging acquisition and processing of cineMRI of the vocal tract

Subject

Tracking the position of speech articulators along time is crucial to understand speech production better. For a long time X-ray imaging was the only technology able to acquire images at a sufficiently high sampling frequency (around 50 images per second) for visualizing articulatory gestures. However, this technique has been abandoned at the end of the eighties because of the health hazard implied by ionizing radiations. Furthermore, since the whole vocal tract is projected on the image plane contours of organs (especially the mandible, teeth and tongue) overlap on the images making the processing of images very difficult.

The interest of Magnetic Resonance Imaging (MRI) is to provide an excellent contrast of soft tissues for a slice placed in any orientation and dynamic MRI is acknowledged as a powerful tool for imaging speech production [3].  However, current performance of cineMRI remains inadequate in terms of sampling rate and spatial resolution, and the objective of this thesis is to develop more efficient acquisition protocols and algorithms.

The objective is to develop protocols by exploiting the latest advances in MRI, particularly parallel imaging and reconstruction under parsimony constraints called ?compressed sensing? [1]. IADI laboratory developed MRI reconstruction techniques with movement compensation [2] and multi-slice dynamic reconstruction enabling super resolution. These techniques have been already applied to cardio-respiratory movements by using physiological signals (ECG and respiratory) as constraints for the reconstruction algorithms.

A first preliminary work consisting of applying these techniques to the domain of speech production  has been carried out [4]. A second work was dedicated to the development of an acquisition protocol based on ?compressed sensing?. The idea is to exploit the parsimony of the image Fourier transform coefficients in order to acquire only a small number of them, and then to reconstruct the image in an optimal manner.

However, it is possible to do better since the speech signal is acquired simultaneously then denoised before being segmented into speech sounds. Therefore, the contribution of each line acquired in the image Fourier space can be related to the speech sound it corresponds to, and one can take advantage of this information to improve the resolution of reconstructed images. This  idea will be exploited with the objective of realizing a proof of concept of automatic acquisition/reconstruction of MRI images of vocal tract during speech production. We would like to go further by utilizing the knowledge of the speech sound and the approximative vocal tract shape predicted for the sound by an articulatory model to pilot acquisition.

Keywords: MRI acquisition, compressed sensing, speech processing, articulatory modeling, vocal tract

Some references

[1]Michael Lustig, David Donoho, and John M. Pauly. Sparse MRI: The application of compressed sensing for rapid MR imaging. MAGNETIC RESONANCE IN MEDICINE, 58(6):1182?1195, December 2007.

[2]F. Odille, P. A. Vuissoz, P. Y. Marie, and J. Felblinger. Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI. Magn Reson Med, 60(1):146?57, July 2008.

[3]Andrew D Scott, Marzena Wylezinska, Malcolm J Birch, and Marc E Miquel. Speech MRI: Morphology and function. Physica Medica, 30(6):604?618, 2014.

[4]P.A. Vuissoz, F. Odille, Y. Laprie, E. Vincent, G. Hossu, and J. Felblinger. Speech Cine SSFP with optical microphone synchronization and motion compensated reconstruction. In ISMRM Workshop on Motion Correction in MRI, Tromso, Norvège, May 2014.

Environment

Both laboratories IADI and LORIA have developed a narrow and fruitful collaboration for years which in particular resulted in the development of a ?compress sensing? acquisition algorithm and in a research contract on articulatory synthesis. A working environment covering articulatory modeling and MRI data acquisition domains is now available and will offer very favorable conditions for this work.

Supervisors

·       Pierre-André Vuissoz (IADI ? Imagerie Adaptative Diagnostique et Interventionnelle, unité INSERM U947) pa.vuissoz@chu-nancy.fr

·       Yves Laprie (MultiSpeech team at LORIA ? Laboratoire Lorrain de Recherche en Informatique et ses Applications, UMR 7503)

Application

We are looking for a highly motivated person with a master degree in computer sciences, applied mathematics or computer sciences. The applicant should have a solid background in signal processing (and Matlab software) and computer sciences. Knowledge in speech processing will be also appreciated.

Interested applicants should send their application file to Yves.Laprie@loria.fr and pa.vuissoz@chru-nancy.fr:

a)     Motivation letter,

b)     CV,

c)     academic transcripts (with explanation of the grade scale adopted),

d)     2 references (letters or names)

Expected start date: 1st October 2016