2019
DOI: 10.1016/j.zemedi.2019.01.001
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SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network

Abstract: Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which ar… Show more

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Cited by 23 publications
(12 citation statements)
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“…Deep learning based techniques can offer an increase in robustness and speed-up in computation. For example, SHARQnet 43 was designed for background field removal using a 3D convolutional neural network and was trained on synthetic background fields overlaid on top of a brain simulation. When the performance was compared to SHARP, RESHARP and V-SHARP, SHARQnet delivered accurate background field corrections on simulations and on in-vivo data.…”
Section: Background Field Removalmentioning
confidence: 99%
“…Deep learning based techniques can offer an increase in robustness and speed-up in computation. For example, SHARQnet 43 was designed for background field removal using a 3D convolutional neural network and was trained on synthetic background fields overlaid on top of a brain simulation. When the performance was compared to SHARP, RESHARP and V-SHARP, SHARQnet delivered accurate background field corrections on simulations and on in-vivo data.…”
Section: Background Field Removalmentioning
confidence: 99%
“…Recently, several studies 44,47,48 for DL have suggested using simulated data as an alternative to in vivo data for training, which allows us to generate ground-truth information without the need of multiple scans. In addition, a similar non-DL approach (ie, machine learning-based algorithms) using simulated training data termed dictionary-based conductivity mapping 63 has previously been suggested.…”
Section: F I G U R Ementioning
confidence: 99%
“…Recently, deep learning (DL) approaches have been introduced for various image reconstruction problems in MR. [37][38][39][40][41][42][43][44][45][46][47] Furthermore, it has recently been suggested to use DL for conductivity reconstructions as an alternative to the computation of the spatial derivatives. [48][49][50] These approaches describe the foundation of alternative algorithms, which allow solving the conductivity reconstruction problem.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Currently, we have used background field corrected data to compute a QSM solution. However, it is also possible to incorporate realistic simulations of background fields originating at tissue boundaries into the training step as shown in the follow-up work of this manuscript (Bollmann et al, 2019). Incorporating the background field correction and the inversion in a single step would then allow the background field removal together with the field-to-source inversion (Heber et al, 2019) in an end-to-end fashion, similar to state of the art iterative single-step QSM algorithms (Langkammer et al, 2015).…”
Section: Accepted Manuscriptmentioning
confidence: 99%