2 March 2018 Fast super-resolution with iterative-guided back projection for 3D MR images
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Abstract
Multimodal magnetic resonance images (e.g., T1-weighted image (TIWI) and T2-weighted image (T2WI)) are used for accurate medical imaging analysis. Different modal images have different resolution depending on pulse sequence parameters under limited data acquisition time. Therefore, interpolation methods are used to match the low-resolution (LR) image with the high-resolution (HR) image. However, the interpolation causes blurring that affects analysis accuracy. Although some recent works such as non-local-means (NLM) filter have manifested impressive super-resolution (SR) performance with available HR modal images, the filter has high computational cost. Therefore, we propose a fast SR framework with iterative-guided back projection, which incorporates iterative back projection with a guided filter (GF) method for resolution enhancement of LR images (e.g., T2WI) by referring HR images in another modality image (e.g., T1WI). The proposed method not only achieves both high accuracy than conventional interpolation methods and original GF and computational efficiency by applying an integral 3D image technique. In addition, although the proposed method is slightly inferior in accuracy visually than the state-of-the-art NLM filter, it can run 22 times faster than the state-of-theart method in expanding three times in the slice-select direction from 180 × 216 × 60 voxels to 180 × 216 × 180 voxels. The computational time of our method is about 1 min only. Therefore, the proposed method will be applied to various applications in practice, including not only multimodal MR images but also multimodal image analysis such as computed tomography (CT) and positron emission tomography (PET).
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Yutaro Iwamoto, Yutaro Iwamoto, Xian-Hua Han, Xian-Hua Han, Akihiko Shiino, Akihiko Shiino, Yen-Wei Chen, Yen-Wei Chen, } "Fast super-resolution with iterative-guided back projection for 3D MR images", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741T (2 March 2018); doi: 10.1117/12.2285336; https://doi.org/10.1117/12.2285336
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