69 research outputs found
Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning
Purpose: To develop biophysics-based method for estimating perfusion Q from
arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net
(QTMnet) was trained to estimate perfusion from 4D tracer propagation images.
The network was trained and tested on simulated 4D tracer concentration data
based on artificial vasculature structure generated by constrained constructive
optimization (CCO) method. The trained network was further tested in a
synthetic brain ASL image based on vasculature network extracted from magnetic
resonance (MR) angiography. The estimations from both trained network and a
conventional kinetic model were compared in ASL images acquired from eight
healthy volunteers. Results: QTMnet accurately reconstructed perfusion Q from
concentration data. Relative error of the synthetic brain ASL image was 7.04%
for perfusion Q, lower than the error using single-delay ASL model: 25.15% for
Q, and multi-delay ASL model: 12.62% for perfusion Q. Conclusion: QTMnet
provides accurate estimation on perfusion parameters and is a promising
approach as a clinical ASL MRI image processing pipeline.Comment: 32 pages, 5 figure
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based
on deep neural networks have been developed with promising results, and
attention mechanism has been further designed to capture global contextual
information for performance enhancement. However, the large size of 3D volume
images poses a great computational challenge to traditional attention methods.
In this paper, we propose a folded attention (FA) approach to improve the
computational efficiency of traditional attention methods on 3D medical images.
The main idea is that we apply tensor folding and unfolding operations with
four permutations to build four small sub-affinity matrices to approximate the
original affinity matrix. Through four consecutive sub-attention modules of FA,
each element in the feature tensor can aggregate spatial-channel information
from all other elements. Compared to traditional attention methods, with
moderate improvement of accuracy, FA can substantially reduce the computational
complexity and GPU memory consumption. We demonstrate the superiority of our
method on two challenging tasks for 3D MIR and MIS, which are quantitative
susceptibility mapping and multiple sclerosis lesion segmentation.Comment: 9 pages, 7 figure
Geometric Loss for Deep Multiple Sclerosis lesion Segmentation
Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume,
and are heterogeneous with regards to shape, size and locations, which poses a
great challenge for training deep learning based segmentation models. We
proposed a new geometric loss formula to address the data imbalance and exploit
the geometric property of MS lesions. We showed that traditional region-based
and boundary-aware loss functions can be associated with the formula. We
further develop and instantiate two loss functions containing first- and
second-order geometric information of lesion regions to enforce regularization
on optimizing deep segmentation models. Experimental results on two MS lesion
datasets with different scales, acquisition protocols and resolutions
demonstrated the superiority of our proposed methods compared to other
state-of-the-art methods.Comment: 5 pages, three figure
2128 Free-breathing steady-state free precession 3D coronary MRA: comparison of diaphragm and cardiac fat navigator techniques
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