Abstract
Magnetic resonance images of neonates, compared with toddlers, exhibit lower signal-to-noise ratio and spatial resolution. In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual-structured sparse representation with convex regularization. Specifically, we introduce a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites. The base layer consists of the smoothed version of the image obtained via Gaussian filtering. The detail layer is the difference between the original image and the base layer. High-frequency details in the detail layer are borrowed across subjects for super-resolution reconstruction. Experimental results on T1 and T2 images demonstrate that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.
| Original language | English |
|---|---|
| Pages (from-to) | 76-87 |
| Number of pages | 12 |
| Journal | Medical Image Analysis |
| Volume | 55 |
| DOIs | |
| Publication status | Published - 2019 Jul 1 |
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Keywords
- Convex optimization
- Dictionary learning
- Magnetic resonance imaging
- Sparse representation
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Computer Vision and Pattern Recognition
- Health Informatics
- Computer Graphics and Computer-Aided Design
Cite this
Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation. / Zhang, Yongqin; Yap, Pew Thian; Chen, Geng; Lin, Weili; Wang, Li; Shen, Dinggang.
In: Medical Image Analysis, Vol. 55, 01.07.2019, p. 76-87.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation
AU - Zhang, Yongqin
AU - Yap, Pew Thian
AU - Chen, Geng
AU - Lin, Weili
AU - Wang, Li
AU - Shen, Dinggang
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Magnetic resonance images of neonates, compared with toddlers, exhibit lower signal-to-noise ratio and spatial resolution. In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual-structured sparse representation with convex regularization. Specifically, we introduce a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites. The base layer consists of the smoothed version of the image obtained via Gaussian filtering. The detail layer is the difference between the original image and the base layer. High-frequency details in the detail layer are borrowed across subjects for super-resolution reconstruction. Experimental results on T1 and T2 images demonstrate that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.
AB - Magnetic resonance images of neonates, compared with toddlers, exhibit lower signal-to-noise ratio and spatial resolution. In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual-structured sparse representation with convex regularization. Specifically, we introduce a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites. The base layer consists of the smoothed version of the image obtained via Gaussian filtering. The detail layer is the difference between the original image and the base layer. High-frequency details in the detail layer are borrowed across subjects for super-resolution reconstruction. Experimental results on T1 and T2 images demonstrate that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.
KW - Convex optimization
KW - Dictionary learning
KW - Magnetic resonance imaging
KW - Sparse representation
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U2 - 10.1016/j.media.2019.04.010
DO - 10.1016/j.media.2019.04.010
M3 - Article
VL - 55
SP - 76
EP - 87
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
ER -