1,724 research outputs found
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated
impressive empirical performance in many imaging modalities. These approaches
generally require a large amount of high-quality training data, which is often
not available. To circumvent this issue, we develop a novel unsupervised
knowledge-transfer paradigm for learned iterative reconstruction within a
Bayesian framework. The proposed approach learns an iterative reconstruction
network in two phases. The first phase trains a reconstruction network with a
set of ordered pairs comprising of ground truth images and measurement data.
The second phase fine-tunes the pretrained network to the measurement data
without supervision. Furthermore, the framework delivers uncertainty
information over the reconstructed image. We present extensive experimental
results on low-dose and sparse-view computed tomography, showing that the
proposed framework significantly improves reconstruction quality not only
visually, but also quantitatively in terms of PSNR and SSIM, and is competitive
with several state-of-the-art supervised and unsupervised reconstruction
techniques
NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great
potential in recent years, obtaining similar results to established
non-learning approaches. Many current deep learning approaches are not data
consistent, require in vivo training data or solve the QSM problem in
consecutive steps resulting in the propagation of errors. Here we aim to
overcome these limitations and developed a framework to solve the QSM
processing steps jointly. We developed a new hybrid training data generation
method that enables the end-to-end training for solving background field
correction and dipole inversion in a data-consistent fashion using a
variational network that combines the QSM model term and a learned regularizer.
We demonstrate that NeXtQSM overcomes the limitations of previous deep learning
methods. NeXtQSM offers a new deep learning based pipeline for computing
quantitative susceptibility maps that integrates each processing step into the
training and provides results that are robust and fast
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
Deep neural networks have demonstrated great potential in solving dipole
inversion for Quantitative Susceptibility Mapping (QSM). However, the
performances of most existing deep learning methods drastically degrade with
mismatched sequence parameters such as acquisition orientation and spatial
resolution. We propose an end-to-end AFfine Transformation Edited and Refined
(AFTER) deep neural network for QSM, which is robust against arbitrary
acquisition orientation and spatial resolution up to 0.6 mm isotropic at the
finest. The AFTER-QSM neural network starts with a forward affine
transformation layer, followed by an Unet for dipole inversion, then an inverse
affine transformation layer, followed by a Residual Dense Network (RDN) for QSM
refinement. Simulation and in-vivo experiments demonstrated that the proposed
AFTER-QSM network architecture had excellent generalizability. It can
successfully reconstruct susceptibility maps from highly oblique and
anisotropic scans, leading to the best image quality assessments in simulation
tests and suppressed streaking artifacts and noise levels for in-vivo
experiments compared with other methods. Furthermore, ablation studies showed
that the RDN refinement network significantly reduced image blurring and
susceptibility underestimation due to affine transformations. In addition, the
AFTER-QSM network substantially shortened the reconstruction time from minutes
using conventional methods to only a few seconds
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Deep learning algorithms utilizing magnetic resonance (MR) images have
demonstrated cutting-edge proficiency in autonomously segmenting multiple
sclerosis (MS) lesions. Despite their achievements, these algorithms may
struggle to extend their performance across various sites or scanners, leading
to domain generalization errors. While few-shot or one-shot domain adaptation
emerges as a potential solution to mitigate generalization errors, its efficacy
might be hindered by the scarcity of labeled data in the target domain. This
paper seeks to tackle this challenge by integrating one-shot adaptation data
with harmonized training data that incorporates labels. Our approach involves
synthesizing new training data with a contrast akin to that of the test domain,
a process we refer to as "contrast harmonization" in MRI. Our experiments
illustrate that the amalgamation of one-shot adaptation data with harmonized
training data surpasses the performance of utilizing either data source in
isolation. Notably, domain adaptation using exclusively harmonized training
data achieved comparable or even superior performance compared to one-shot
adaptation. Moreover, all adaptations required only minimal fine-tuning,
ranging from 2 to 5 epochs for convergence
DeDA: Deep Directed Accumulator
Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can
be characterized by a hyperintense rim at the edge of the lesion on
quantitative susceptibility maps. These rim+ lesions exhibit a geometrically
simple structure, where gradients at the lesion edge are radially oriented and
a greater magnitude of gradients is observed in contrast to rim- (non rim+)
lesions. However, recent studies have shown that the identification performance
of such lesions remains unsatisfied due to the limited amount of data and high
class imbalance. In this paper, we propose a simple yet effective image
processing operation, deep directed accumulator (DeDA), that provides a new
perspective for injecting domain-specific inductive biases (priors) into neural
networks for rim+ lesion identification. Given a feature map and a set of
sampling grids, DeDA creates and quantizes an accumulator space into finite
intervals, and accumulates feature values accordingly. This DeDA operation is a
generalized discrete Radon transform and can also be regarded as a symmetric
operation to the grid sampling within the forward-backward neural network
framework, the process of which is order-agnostic, and can be efficiently
implemented with the native CUDA programming. Experimental results on a dataset
with 177 rim+ and 3986 rim- lesions show that 10.1% of improvement in a partial
(false positive rate<0.1) area under the receiver operating characteristic
curve (pROC AUC) and 10.2% of improvement in an area under the precision recall
curve (PR AUC) can be achieved respectively comparing to other state-of-the-art
methods. The source code is available online at
https://github.com/tinymilky/DeDAComment: 18 pages, 3 Tables and 4 figure
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