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Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction [article]

Hongjiang Wei, Steven Cao, Yuyao Zhang, Xiaojun Guan, Fuhua Yan, Kristen W. Yeom, Chunlei Liu
2019 arXiv   pre-print
To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and  ...  Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps.  ...  Jongho Lee for QSMnet reconstruction used in the Figure S4 in the supplemental material.  ... 
arXiv:1905.05953v1 fatcat:c45dvrj76fa45op36wn25fsepy

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction

Hongjiang Wei, Steven Cao, Yuyao Zhang, Xiaojun Guan, Fuhua Yan, Kristen W. Yeom, Chunlei Liu
2019 NeuroImage  
To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and  ...  Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps.  ...  Jongho Lee for QSMnet reconstruction used in the Figure 5 .  ... 
doi:10.1016/j.neuroimage.2019.116064 pmid:31377323 pmcid:PMC6819263 fatcat:tayob7wnlzgbpbi7ozur4kt5xy

Model-based Learning for Quantitative Susceptibility Mapping [article]

Juan Liu, Kevin M. Koch
2020 arXiv   pre-print
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements.  ...  When qualitatively evaluated on single-orientation datasets, uQSM outperforms other methods and reconstructed high quality QSM.  ...  Model-based Learning for QSM  ... 
arXiv:2004.06259v3 fatcat:ktisth3g2bcjjhjrag6cvvffle

Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping [article]

Juan Liu, Kevin M Koch
2020 arXiv   pre-print
To address these limitations, we propose a weakly-supervised single-step QSM reconstruction method, denoted as wTFI, to directly reconstruct QSM from the total field without BFR. wTFI uses the BFR method  ...  Quantitative susceptibility mapping (QSM) utilizes MRI phase information to estimate tissue magnetic susceptibility.  ...  Weakly-supervised Learning for Single-step QSM  ... 
arXiv:2008.06187v1 fatcat:w4ikfc4uhbdxdo37xhywcghmdq

Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks [article]

Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun
2022 arXiv   pre-print
This study develops a large-stencil Laplacian preprocessed deep learning-based neural network for near instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MR phase data  ...  Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data.  ...  Indeed, the proposed iQFM and iQSM can be directly computed on the raw phase without any brain tissue extraction step.  ... 
arXiv:2111.07665v3 fatcat:ral2hjz2qfhxlkgceoe5qpe7lq

Improved Padding in CNNs for Quantitative Susceptibility Mapping [article]

Juan Liu
2021 arXiv   pre-print
Recently, deep learning methods have been proposed for quantitative susceptibility mapping (QSM) data processing: background field removal, field-to-source inversion, and single-step QSM reconstruction  ...  QSM reconstruction.  ...  Introduction In quantitative susceptibility mapping (QSM), tissue susceptibility is quantitatively estimated by extracting Larmor frequency offsets from complex MR signals to solve for the source tissue  ... 
arXiv:2106.15331v1 fatcat:zxm5s26oqffr3hkjisp5ea7aue

NeXtQSM – A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data [article]

Francesco Cognolato, Kieran O'Brien, Jin Jin, Simon Robinson, Frederik B. Laun, Markus Barth, Steffen Bollmann
2021 arXiv   pre-print
accurate quantitative susceptibility maps.  ...  Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, outperforming traditional non-learning approaches in speed and accuracy.  ...  the susceptibility map.  ... 
arXiv:2107.07752v1 fatcat:6iyvf52j7fhzdf6uhroq3vmasy

CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN [article]

Gyutaek Oh, Hyokyoung Bae, Hyun-Seo Ahn, Sung-Hong Park, Jong Chul Ye
2020 arXiv   pre-print
Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique which provides spatial distribution of magnetic susceptibility values of tissues.  ...  Most of the existing deep learning methods are, however, based on supervised learning, so matched pairs of input phase images and the ground-truth maps are needed.  ...  Fig. 6 shows the mean susceptibility values of brain structures in reconstructed QSM. Deep learning based algorithms show lower susceptibility values than conventional algorithms on average.  ... 
arXiv:2012.03842v1 fatcat:c2ke2ibxtbgure3443ehifgpua

MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks [article]

Juan Liu, Kevin M. Koch
2019 arXiv   pre-print
To overcome these limitations, a robust deep-learning-based single-step QSM reconstruction approach is proposed and demonstrated.  ...  Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide novel image contrast and  ...  Conclusion In summary, a deep-learning-based single-step QSM approach have been demonstrated. It can substantially improve brain susceptibility estimation using clinical QSM data.  ... 
arXiv:1904.07105v1 fatcat:ps4ebf5b2ja3hcquxqrygmsq6q

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping [article]

Kasper Gade Botker Rasmussen, Mads Janus Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Ostergaard, Maciej Plocharski, Kieran O'Brien, Christian Langkammer, Andrew Janke, Markus Barth, Steffen Bollmann
2018 bioRxiv   pre-print
Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements.  ...  We demonstrate that DeepQSM's susceptibility maps enable identification of deep brain substructures that are not visible in MRI phase data and provide information on their respective magnetic tissue properties  ...  Introduction Quantitative susceptibility mapping (QSM) is an increasingly utilized post-processing technique that extracts magnetic susceptibility from the phase of magnetic resonance imaging (MRI) gradient  ... 
doi:10.1101/278036 fatcat:hwbb2lovjbhvfej75lloc6p6qe

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Woojin Jung, Steffen Bollmann, Jongho Lee
2020 NMR in Biomedicine  
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance  ...  With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM  ...  phase image with no brain mask as the input. 103 These deep learning QSM methods might prevent the potential error propagations from background field removal and dipole inversion by a single-step reconstruction  ... 
doi:10.1002/nbm.4292 pmid:32207195 fatcat:p55i6eydtjekbpfpcq2z52l624

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

Christian Langkammer, Ferdinand Schweser, Karin Shmueli, Christian Kames, Xu Li, Li Guo, Carlos Milovic, Jinsuh Kim, Hongjiang Wei, Kristian Bredies, Sagar Buch, Yihao Guo (+5 others)
2017 Magnetic Resonance in Medicine  
Purpose: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data  ...  Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map.  ...  However, unlike in image reconstruction challenges that only provide a subset of the data for reconstruction and then test against a gold standard that is unknown to the challenge participants, the same  ... 
doi:10.1002/mrm.26830 pmid:28762243 fatcat:hxjvtj7w5ncpzdw56cjbvltgmq

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources [article]

Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun
2022 arXiv   pre-print
Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM).  ...  In addition, BFRnet produced the most consistent local field and susceptibility maps between different sizes of brain masks, while conventional methods depend drastically on precise brain extraction and  ...  However, these single-step methods do not produce the local field maps required by other methods such as COSMOS (27) .  ... 
arXiv:2204.02760v1 fatcat:pxs2tyf3qzb2ddlcvqfejkkize

Accelerated white matter lesion analysis based on simultaneous T 1 and T 2 ∗ quantification using magnetic resonance fingerprinting and deep learning

Ingo Hermann, Eloy Martínez-Heras, Benedikt Rieger, Ralf Schmidt, Alena-Kathrin Golla, Jia-Sheng Hong, Wei-Kai Lee, Wu Yu-Te, Martijn Nagtegaal, Elisabeth Solana, Sara Llufriu, Achim Gass (+3 others)
2021 Magnetic Resonance in Medicine  
MRF is a fast and robust tool for quantitative T 1 and T 2 ∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.  ...  Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision.  ...  This renders quantitative MRI susceptibility to intra-scan motion.  ... 
doi:10.1002/mrm.28688 pmid:33547656 fatcat:n74dmv4qqvdgjabrinn4r6cjtm

QSMxT: Robust Masking and Artefact Reduction for Quantitative Susceptibility Mapping [article]

Ashley Wilton Stewart, Simon Daniel Robinson, Kieran O'Brien, Jin Jin, Georg Widhalm, Gilbert Hangel, Angela Walls, Jonathan Goodwin, Korbinian Eckstein, Monique Tourell, Catherine Morgan, Aswin Narayanan (+2 others)
2021 bioRxiv   pre-print
AbstractPurposeQuantitative Susceptibility Mapping (QSM) is a post-processing technique applied to gradient-echo phase data.  ...  separated sources before combination, extracting more information while reducing the influence of artefacts.ResultCompared with standard masking and reconstruction procedures, the two-pass inversion reduces  ...  Finally, studies involving multiple participants require the construction of a common space to facilitate group-level analyses, as well as segmentation of the susceptibility maps to extract quantitative  ... 
doi:10.1101/2021.05.05.442850 fatcat:upvjpxwhargdjh76utm4bsewvy
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