Reconstruction of 7T-Like Images From 3T MRI

Bahrami, Khosro; Shi, Feng; Zong, Xiaopeng; Shin, Hae Won; An, Hongyu; Shen, Dinggang

doi:10.1109/tmi.2016.2549918

Cited by 73 publications

(64 citation statements)

References 45 publications

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“…A study using 3T input data and 7T output data showed that a deep network can be trained to create simulated "7T-like" images from 3T data. 16 Often obtaining a certain imaging sequence can be very time-consuming; an example is DTI, in which the need for multiple angular directions lengthens the examination beyond what many patients can tolerate. A deep learning approach can reduce imaging duration 12-fold by predicting final parameter maps (fractional anisotropy, mean diffusivity, and so forth) from relatively few angular directions.…”

Section: -13mentioning

confidence: 99%

Deep Learning in Neuroradiology

Zaharchuk

Gong

Wintermark

et al. 2018

AJNR Am J Neuroradiol

266

169

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SUMMARY:Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.ABBREVIATIONS: AD ϭ Alzheimer disease; ADNI ϭ Alzheimer's Disease Neuroimaging Initiative; ASL ϭ arterial spin-labeling; CNN ϭ convolutional neural network;

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Section: -13mentioning

confidence: 99%

Deep Learning in Neuroradiology

Zaharchuk

Gong

Wintermark

et al. 2018

AJNR Am J Neuroradiol

266

169

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“…For reconstruction of 7T-like MR images from 3T MR image, we compare our method with different methods, such as Sparse [4], MIMECS [6], and multi-level canonical correspondence analysis (M-CCA) [7]. Furthermore, we compare our method with the segmentation results based on the 3T MR images and also the reconstructed 7T-like MR images by different methods.…”

Section: Resultsmentioning

confidence: 99%

“…Left to right of (a) show the axial views and the close-up views of 3T MRI, the four reconstructed 7T-like MRIs (by Sparse [4], MIMECS [6], M-CCA [7], and our method), and the ground-truth 7T MRI. The segmentation results in (b) are arranged in the same way as in (a).…”

Section: Figmentioning

confidence: 99%

“…From left to right of each subfigure, the distribution of Dice ratios on 3T MRI and the 7T-like MR images (by Sparse [6], MIMECS [6], M-CCA [7], and our proposed method) are shown, respectively.…”

Section: Figmentioning

confidence: 99%

“…In another work, Bahrami et al . [7] proposed a sparse representation in multi-level canonical correlation analysis (CCA) to increase the correlation between 3T MRI and 7T MRI for 7T-like MR image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Reconstruction and Segmentation of 7T-like MR Images from 3T MRI Based on Cascaded Convolutional Neural Networks

Bahrami

Rekik

Shi

et al. 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

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7T MRI scanner provides MR images with higher resolution and better contrast than 3T MR scanners. This helps many medical analysis tasks, including tissue segmentation. However, currently there is a very limited number of 7T MRI scanners worldwide. This motivates us to propose a novel image post-processing framework that can jointly generate high-resolution 7T-like images and their corresponding high-quality 7T-like tissue segmentation maps, solely from the routine 3T MR images. Our proposed framework comprises two parallel components, namely (1) reconstruction and (2) segmentation. The reconstruction component includes the multi-step cascaded convolutional neural networks (CNNs) that map the input 3T MR image to a 7T-like MR image, in terms of both resolution and contrast. Similarly, the segmentation component involves another paralleled cascaded CNNs, with a different architecture, to generate high-quality segmentation maps. These cascaded feedbacks between the two designed paralleled CNNs allow both tasks to mutually benefit from each another when learning the respective reconstruction and segmentation mappings. For evaluation, we have tested our framework on 15 subjects (with paired 3T and 7T images) using a leave-one-out cross-validation. The experimental results show that our estimated 7T-like images have richer anatomical details and better segmentation results, compared to the 3T MRI. Furthermore, our method also achieved better results in both reconstruction and segmentation tasks, compared to the state-of-the-art methods.

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High‐performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks

Pitchaimani¹,

Rajan²

2019

Int J Imaging Syst Tech

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This research paper shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high-level features from convolutional Neural Network (CNN) system. By recognize inherent deep feature representations in image patches for morphological changes in medicinal imaging information discovery. Various performance measurements, High Frequency Error Norm (HFEN), Mean Squared Error MSE, peak Signal-to-noise-ratio (PSNR), Structural Similarity Index (SSI), are utilized to inspect different dataset. As presented in the paper outcomes, the proposed method can accomplish predominant performance compared with other cutting-edge techniques with either low or high-level features in terms of

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Reconstruction of 7T-Like Images From 3T MRI

Cited by 73 publications

References 45 publications

Deep Learning in Neuroradiology

Deep Learning in Neuroradiology

Joint Reconstruction and Segmentation of 7T-like MR Images from 3T MRI Based on Cascaded Convolutional Neural Networks

High‐performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks

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