Chaotic Sensing

Chandra, Shekhar S.; Ruben, Gary; Jin, Jin; Li, Mingyan; Kingston, Andrew; Svalbe, Imants; Crozier, Stuart

doi:10.1109/tip.2018.2864918

Cited by 6 publications

(1 citation statement)

References 68 publications

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“…This means that there are physical limitations on how fast MR measurements can be made in k ‐space over time. For example, MR measurements can be sampled on Cartesian and non‐Cartesian grids and different techniques have been developed to increase robustness including random sampling, 18 spirals 19 and fractals 20 . See Hsieh and Svalbe 21 for a recent review of clinical applications of spiral and finger‐printing techniques.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in magnetic resonance image reconstruction

et al. 2021

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Summary Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi‐contrast imaging. Publications with deep learning‐based image reconstruction for MR imaging were identified from the literature (PubMed and Google Scholar), and a comprehensive description of each of the works was provided. A detailed comparison that highlights the differences, the data used and the performance of each of these works were also made. A discussion of the potential use cases for each of these methods is provided. The sparse image reconstruction methods were found to be most popular in using deep learning for improved performance, accelerating acquisitions by around 4–8 times. Multi‐contrast image reconstruction methods rely on at least one pre‐acquired image, but can achieve 16‐fold, and even up to 32‐ to 50‐fold acceleration depending on the set‐up. Parallel imaging provides frameworks to be integrated in many of these methods for additional speed‐up potential. The successful use of compressed sensing techniques and multi‐contrast imaging with deep learning and parallel acquisition methods could yield significant MR acquisition speed‐ups within clinical routines in the near future.

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Section: Introductionmentioning

confidence: 99%

Deep learning in magnetic resonance image reconstruction

et al. 2021

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Transformer Compressed Sensing Via Global Image Tokens

Lorenzana

Engstrom

Chandra

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Convolutional neural networks (CNN) have demonstrated outstanding Compressed Sensing (CS) performance compared to traditional, hand-crafted methods. However, they are broadly limited in terms of generalisability, inductive bias and difficulty to model long distance relationships. Transformer neural networks (TNN) overcome such issues by implementing an attention mechanism designed to capture dependencies between inputs. However, high-resolution tasks typically require vision Transformers (ViT) to decompose an image into patch-based tokens, limiting inputs to inherently local contexts. We propose a novel image decomposition that naturally embeds images into low-resolution inputs. These Kaleidoscope tokens (KD) provide a mechanism for global attention, at the same computational cost as a patch-based approach. To showcase this development, we replace CNN components in a well-known CS-MRI neural network with TNN blocks and demonstrate the improvements afforded by KD. We also propose an ensemble of image tokens, which enhance overall image quality and reduces model size. Supplementary material is available: https://github.com/uqmarlonbran/TCS.git.

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