A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI

Shilling, Richard; Robbie, Trevor Q; Bailloeul, T.; Mewes, Klaus; Mersereau, R.M.

doi:10.1109/tmi.2008.2007348

Cited by 99 publications

(89 citation statements)

References 24 publications

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“…10 Such motionbased and learning-based frameworks have also been employed for super-resolution in the medical image domains, especially for MR images. 12,13 However, looking more closely, the 4D-CT resolution enhancement problem that we consider is quite distinct from the above discussed SR approaches in terms of data usage and availability. This is because, unlike motion-based methods, it is difficult to rely on accurate registration in 4D-CT because of the poor superior-inferior resolution.…”

Section: A Relation To Previous Workmentioning

confidence: 99%

Resolution enhancement of lung 4D‐CT via group‐sparsity

Bhavsar

Lian

et al. 2013

Medical Physics

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Purpose: 4D-CT typically delivers more accurate information about anatomical structures in the lung, over 3D-CT, due to its ability to capture visual information of the lung motion across different respiratory phases. This helps to better determine the dose during radiation therapy for lung cancer. However, a critical concern with 4D-CT that substantially compromises this advantage is the low superior-inferior resolution due to less number of acquired slices, in order to control the CT radiation dose. To address this limitation, the authors propose an approach to reconstruct missing intermediate slices, so as to improve the superior-inferior resolution. Methods: In this method the authors exploit the observation that sampling information across respiratory phases in 4D-CT can be complimentary due to lung motion. The authors' approach uses this locally complimentary information across phases in a patch-based sparse-representation framework. Moreover, unlike some recent approaches that treat local patches independently, the authors' approach employs the group-sparsity framework that imposes neighborhood and similarity constraints between patches. This helps in mitigating the trade-off between noise robustness and structure preservation, which is an important consideration in resolution enhancement. The authors discuss the regularizing ability of group-sparsity, which helps in reducing the effect of noise and enables better structural localization and enhancement. Results: The authors perform extensive experiments on the publicly available DIR-Lab Lung 4D-CT dataset [R. Castillo, E. Castillo, R. Guerra, V. Johnson, T. McPhail, A. Garg, and T. Guerrero, "A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets," Phys. Med. Biol. 54, 1849-1870 (2009)]. First, the authors carry out empirical parametric analysis of some important parameters in their approach. The authors then demonstrate, qualitatively as well as quantitatively, the ability of their approach to achieve more accurate and better localized results over bicubic interpolation as well as a related state-of-the-art approach. The authors also show results on some datasets with tumor, to further emphasize the clinical importance of their method. Conclusions:The authors have proposed to improve the superior-inferior resolution of 4D-CT by estimating intermediate slices. The authors' approach exploits neighboring constraints in the group-sparsity framework, toward the goal of achieving better localization and noise robustness. The authors' results are encouraging, and positively demonstrate the role of group-sparsity for 4D-CT resolution enhancement.

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Section: A Relation To Previous Workmentioning

confidence: 99%

Resolution enhancement of lung 4D‐CT via group‐sparsity

Bhavsar

Lian

et al. 2013

Medical Physics

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“…Two kinds of approaches can be identified: one works at the acquisition level over raw data (frequency space), while the others act on the volumetric images (spatial or image space) as an additional processing step. At the acquisition stage, the k-space data can be manipulated and combined to obtain adequate spatial resolution while reducing acquisition time (Herment et al, 2003); or parameters can be configured to obtain multiple scans with different slice directions which are then mixed up (Shilling et al, 2009). Regarding volumetric images, Peled and Yeshurun (2001) and Greenspan et al (2002) have proposed the first approaches to adapt the iterative back-projection method proposed by Irani and Peleg (1993) to 2D and 3D MR images, respectively; followed by other strategies such as the resolution enhancement method described by Carmi et al (2006).…”

Section: Introductionmentioning

confidence: 99%

Single-image super-resolution of brain MR images using overcomplete dictionaries

Rueda

Malpica

Romero

2013

Medical Image Analysis

163

102

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“…SR in MRI has been mostly investigated using a specialized protocol to acquire shifted images, where the motion between slices is known (translation in the slice direction [7,8,9] or rotation around a common frequency encoding axis [10]). In this paper, we study reconstruction techniques with a commonly used fetal imaging protocol: multiple orthogonal stacks of thick slices where the slice thickness is usually around three times the dimension of the in-plane resolution.…”

Section: Introductionmentioning

confidence: 99%

On Super-Resolution for Fetal Brain MRI

Rousseau

Kim

Studholme

et al. 2010

Lecture Notes in Computer Science

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Abstract. Super-resolution techniques provide a route to studying fine scale anatomical detail using multiple lower resolution acquisitions. In particular, techniques that do not depend on regular sampling can be used in medical imaging situations where imaging time and resolution are limited by subject motion. We investigate in this work the use of a super-resolution technique for anisotropic fetal brain MR data reconstruction without modifying the data acquisition protocol. The approach, which consists of iterative motion correction and high resolution image estimation, is compared with a previously used scattered data interpolation-based reconstruction method. To optimize acquisition time, an evaluation of the influence of the number of input images and image noise is also performed. Evaluation on simulated MR images and real data show significant improvements in performance provided by the super-resolution approach.

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A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI

Cited by 99 publications

References 24 publications

Resolution enhancement of lung 4D‐CT via group‐sparsity

Resolution enhancement of lung 4D‐CT via group‐sparsity

Single-image super-resolution of brain MR images using overcomplete dictionaries

On Super-Resolution for Fetal Brain MRI

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