Super-resolution Reconstruction for Tongue MR Images

Abstract

Magnetic resonance (MR) images of the tongue have been used in both clinical medicine and scientific research to reveal tongue structure and motion. In order to see different features of the tongue and its relation to the vocal tract it is beneficial to acquire three orthogonal image stacks-e.g., axial, sagittal and coronal volumes. In order to maintain both low noise and high visual detail, each set of images is typically acquired with in-plane resolution that is much better than the through-plane resolution. As a result, any one data set, by itself, is not ideal for automatic volumetric analyses such as segmentation and registration or even for visualization when oblique slices are required. This paper presents a method of super-resolution reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image stacks. The method uses preprocessing steps that include intensity matching and registration and a data combination approach carried out by Markov random field optimization. The performance of the proposed method was demonstrated on five clinical datasets, yielding superior results when compared with conventional reconstruction methods.

Figure 1

Tongue images are acquired in three orthogonal volumes with field of view encompassing the tongue and surrounding structures. (a) Coronal, (b) sagittal, and (c) axial volumes are illustrated. The final super-resolution volume using the proposed method is shown in (d). The red arrows indicate the tongue region.

Figure 2

A flowchart of the proposed method.

Figure 3

One representative final super-resolved image is shown in (a), regions defined from the masks are shown in (b), a volume with regions defined from the masks is illustrated in (c), and a 3D volume rendering is illustrated in (d).

Figure 4

Comparison of different reconstruction methods using normal subject. Original low-resolution coronal, sagittal, and axial volumes are shown in (a), (b), and (c), respectively. Red boxes represent original volumes after isotropic volume upsampling using B-spline. Three different reconstruction methods including simple averaging, Tikhonov regularization, and the proposed method are presented in (d), (e), and (f), respectively. Original three volumes are illustrated in (g). Please notice that the proposed method provides detailed anatomical information compared to averaging and Tikhonov regularization methods as visually assessed.

Figure 5

Comparison of different reconstruction methods using a glossectomy patient. Original low-resolution coronal, sagittal, and axial volumes are shown in (a), (b), and (c), respectively. Red boxes represent original volumes after isotropic volume upsampling using B-spline. Three different reconstruction methods include (d) simple averaging, (e) Tikhonov regularization, and (f) the proposed method, respectively. Original three volumes are illustrated in (g). Please notice that the proposed method provides detailed anatomical information compared to averaging and Tikhonov regularization methods as visually assessed.