Correction of dynamic off-resonance in spiral 2D real-time MRI of speech
Yongwan Lim1, Sajan Goud Lingala1, Shrikanth Narayanan 1, and Krishna Nayak1

1Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States


Spiral real-time MRI (RT-MRI) is a valuable tool in speech production research. A key drawback is off-resonance blurring artifact that appears at the boundaries of important articulators. In this work, we demonstrate dynamic off-resonance estimation that is directly captured from phase of single echo-time dynamic images after coil phase compensation. Multi-frequency reconstruction then provides deblurring and improved depiction of articulator boundaries including the tongue, hard palate, and soft palate.


Spiral real-time MRI (RT-MRI) has become a valuable tool for speech production research because it provides non-invasive depiction of the vocal tract dynamics, allowing for a time efficient acquisition1–2. A key drawback of the spiral trajectory in vocal tract imaging is off-resonance blurring3 at the boundaries of important articulators, such as the lips, palate, and tongue. The blurring is severe for longer spiral readout and impairs the analysis of dynamic articulators in speech science. Current speech RT-MRI protocols mitigate this by using short duration readouts (~2.5 ms) and low field strength (1.5 T), compromising acquisition efficiency4–6. Several deblurring methods have been proposed7–12, most of which require measurement of field map using multiple echo times7–9. This requires compromising temporal and/or spatial resolution9. In this work, we demonstrate dynamic off-resonance estimation that is directly captured from phase of single echo-time dynamic images after coil phase compensation. We apply this method into longer readout (4.016 ms) RT-MRI and show that this method, when combined with multi-frequency reconstruction, improves sharpness of the vocal tract articulator boundaries in spiral 2D RT-MRI of speech.


Dynamic Field Map Estimation

Consider spiral RT-MRI, where the phase of the image time series (Ic(r,t)) for c-th coil is:

where r(x,y) is image domain spatial coordinates, Sc(r) is coil-phase that is spatially smooth and independent of time, and Δf(r,t) is dynamic off-resonance. Phase accrual during the spiral readout is ignored.

We estimate the coil sensitivity map Sc^(r) and the coil-phase Sc^(r) using the sum-of-square method13 from a temporally-averaged and spatially-low-pass-filtered image Iavg,c(r)=LPFx,y{(1/N)t=1NIc(r,t)}. We then combine the individual coil images Ic(r,t) into a single image, I(r,t) using optimal B1 combination13. We compute a dynamic field map estimate Δf^(r,t) from I(r,t) as follows:


Note that this approach only captures the dynamic field map, i.e. there will be a residue (f(r,t)Δf^(r,t)) that equals LPFx,y{(1/N)t=1NΔf(r,t)}, a spatially low-pass filtered version of the time-averaged field map, where LPFx,y{} is the same one used to generate Iavg,c(r).


Experiments were performed on a GE Signa Excite 1.5 T scanner with a custom 8-channel upper-airway coil6 and an 8-interleaf spiral fast gradient echo pulse sequence (readout time = 4.016 ms). A volunteer was scanned at the mid-sagittal plane while performing the speech task: “one-two-three-four-five” several times at a normal pace followed by several times at a fast pace (roughly 2x). Imaging parameters: spatial resolution = 2.4×2.4 mm2, slice thickness = 6 mm, FOV = 20cm2, TR = 7.508 ms, TE = 0.8 ms, BW = ±125 kHz, FA = 15°. Sliding-window image reconstruction (22.2 frames per second) was performed, using multi-frequency interpolation method7 based on the proposed dynamic field map estimate Δf^(r,t).

Results and Discussion

Figure 1 contains reconstructed image frames without and with the proposed correction, and the corresponding estimated dynamic field map. Near air-tissue interfaces, we observed rapid temporal variations.

Figure 2 contains representative image frames without and with the proposed correction. The proposed correction improved the depiction of air-tissue boundaries, especially the hard palate, soft palate, and tongue boundaries (see red arrows).

Figure 3 contains intensity vs. time profiles from different image locations (dotted lines in Fig. 2). The profiles allow one to easily appreciate the sharper air-tongue boundary. Correction also results in more temporally consistent signal intensity in the hard and soft palate (red arrows). This result agrees with the fact that the hard palate, which is a bony structure covered by a thin layer of tissue, does not change its shape during speech production14.


We have demonstrated a method for estimating dynamic field variation in spiral 2D RT-MRI of speech. This method, when combined with multi-frequency reconstruction, improves sharpness of the vocal tract articulator boundaries including the hard palate, soft palate, and tongue, which has the potential to improve the analysis of articulator motion in speech science. A limitation of this work is that the time-averaged field map is not corrected. Additional estimation of this field and/or improvements to pre-scan shimming may have a synergistic role.


This work was supported by National Institute of Health under NIH-R01-DC007124 and National Science Foundation under NSF-1514544.


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Figure 1. Long spiral readout (readout duration = 4.016 ms) images (without and with the proposed correction). The left column shows an image frame with no correction, the middle column shows an image frame after the proposed correction, and the right column shows the estimated field map corresponding to the image frame. The estimated field map, Δf^(r,t) only captures the time-varying off-resonance frequency (f(r,t)LPFx,y{(1/N)t=1NΔf(r,t)}). The field map here is masked based on image intensity such that noise area has zero frequency value.

Figure 2. Representative mid-sagittal image frames of vocal tract in 2D RT-MRI of speech. The top row shows images reconstructed with no correction and the bottom row shows images reconstructed using the proposed correction. Red arrows point out the regions that are most obviously affected by off-resonance. Image after correction provides improved image depiction of the air-tissue boundaries such as the tongue, hard palate, and soft palate as shown with red arrows.

Figure 3. Comparison of image quality of articulator boundaries. The images show intensity vs. time profiles from cut-views lines that are extracted from three different locations marked by the white dotted lines in Figure 2. The profile after correction exhibits the sharper boundary between tongue and air than those with no correction. In addition, correction results in more temporally consistent signal intensity in the hard palate and soft palate (red arrows in the middle and right columns).

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)