Interpolation-based super-resolution reconstruction: effects of slice thickness

Abstract

Abstract. Standard clinical magnetic resonance imaging (MRI) is acquired in two-dimensions where the in-plane resolution is higher than the slice select direction. These acquisitions include axial, coronal, and sagittal planes. To date, there have been few attempts to combine the information of these three orthogonal orientations. This paper aims to take advantage of the different in-plane resolution acquired from each plane orientation and combine them into one volume in order to attain a higher resolution image. This combination of MRI data will allow the detection of smaller areas that would otherwise be missed using only one slice orientation. A comparison of slice thicknesses along with image registration is performed. The mean-squared error and peak signal-to-noise were computed for quantitative assessment. MRI and phantom scans and joint histograms were used for qualitative assessment.

Global Left Ventricular Area Strain Using Standard Two-Dimensional Cine Magnetic Resonance Imaging with Inter-Slice Interpolation

“…However, during clinical MR examination, there are some limitations for acquiring 3D cinematic data due to the long acquisition time and increased patient burden due to repeated breath holds. Previously, several inter-slice interpolation techniques have been reported for medical imaging analysis [11][12][13]. We hypothesized that standard 2D cine MR data can be interpolated as volumetric image data acquired by these techniques.…”

MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model

“…There are generally three methods to achieve image SR in MRI: (a) interpolation‐based, (b) reconstruction‐based, and (c) machine learning‐based . Interpolation‐based techniques assume that points/regions in an LR image can be expanded into corresponding points/regions in the SR reconstruction using polynomial or interpolation functions with some smoothness priors, which is not valid in inhomogeneous regions . Moreover, the actual LR sampled points represent a nonideal sampling where the sampled points represent the intermediate value of the underlying HR points that exist within the LR points.…”

“…20 Since then, various advanced SR techniques established in MRI have offered the possibility to efficiently improve the image resolution and increase the diagnostic potential. 2,18,[21][22][23][24][25] There are generally three methods to achieve image SR in MRI: (a) interpolation-based 9,26,27 , (b) reconstructionbased [28][29][30] , and (c) machine learning-based. [31][32][33][34][35] Interpolation-based techniques assume that points/regions in an LR image can be expanded into corresponding points/regions in the SR reconstruction using polynomial or interpolation functions with some smoothness priors, 9,26,27 which is not valid in inhomogeneous regions.…”

FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution

“…The super-resolution (SR) is a technique to generate a high-resolution (HR) image from a single or a group of low-resolution (LR) images, which can improve the visibility of image details or restore image details ( Tourbier et al, 2015 ; Dong et al, 2016 ; Shi et al, 2018a ). Without changing hardware or scanning components, SR methods can significantly improve the spatial resolution of MRI ( Mahmoudzadeh and Kashou, 2014 ; Luo et al, 2017 ). Generally, there are three methods to implement image SR in MRI: interpolation-based, construction-based, and machine learning-based ( Shi et al, 2018b ; Jog et al, 2014 ).…”