2 research outputs found
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging
inverse problem driven by ill conditioning of its field-to -susceptibility
transformation. State-of-art QSM reconstruction methods either suffer from
image artifacts or long computation times, which limits QSM clinical
translation efforts. To overcome these limitations, a non-locally
encoder-decoder gated convolutional neural network is trained to infer whole
brain susceptibility map, using the local field and brain mask as the inputs.
The performance of the proposed method is evaluated relative to synthetic data,
a publicly available challenge dataset, and clinical datasets. The proposed
approach can outperform existing methods on quantitative metrics and visual
assessment of image sharpness and streaking artifacts. The estimated
susceptibility maps can preserve conspicuity of fine features and suppress
streaking artifacts. The demonstrated methods have potential value in advancing
QSM clinical research and aiding in the translation of QSM to clinical
operations
Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities
Quantitative susceptibility mapping (QSM) has gained broad interests in the
field by extracting biological tissue properties, predominantly myelin, iron
and calcium from magnetic resonance imaging (MRI) phase measurements in vivo.
Thereby, QSM can reveal pathological changes of these key components in a
variety of diseases. QSM requires multiple processing steps such as phase
unwrapping, background field removal and field-to-source-inversion. Current
state of the art techniques utilize iterative optimization procedures to solve
the inversion and background field correction, which are computationally
expensive and require a careful choice of regularization parameters. 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 processing steps can be solved by
efficient feed forward multiplications not requiring iterative optimization nor
the choice of regularization parameters. Here, we review the current status of
deep learning based approaches for processing QSM, highlighting limitations and
potential pitfalls, and discuss the future directions the field may take to
exploit the latest advances in deep learning for QSM