70 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
mcLARO: Multi-Contrast Learned Acquisition and Reconstruction Optimization for simultaneous quantitative multi-parametric mapping
Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM
mapping in a single scan using multi-contrast Learned Acquisition and
Reconstruction Optimization (mcLARO).
Methods: A pulse sequence was developed by interleaving inversion recovery
and T2 magnetization preparations and single-echo and multi-echo gradient echo
acquisitions, which sensitized k-space data to T1, T2, T2* and magnetic
susceptibility. The proposed mcLARO used a deep learning framework to optimize
both the multi-contrast k-space under-sampling pattern and the image
reconstruction based on image feature fusion. The proposed mcLARO method with
R=8 under-sampling was validated in a retrospective ablation study using fully
sampled data as reference and evaluated in a prospective study using separately
acquired conventionally sampled quantitative maps as reference standard.
Results: The retrospective ablation study showed improved image sharpness of
mcLARO compared to the baseline network without multi-contrast sampling pattern
optimization or image feature fusion, and negligible bias and narrow 95% limits
of agreement on regional T1, T2, T2* and QSM values were obtained by the
under-sampled reconstructions compared to the fully sampled reconstruction. The
prospective study showed small or negligible bias and narrow 95% limits of
agreement on regional T1, T2, T2* and QSM values by mcLARO (5:39 mins) compared
to reference scans (40:03 mins in total).
Conclusion: mcLARO enabled fast sub-millimeter T1, T2, T2* and QSM mapping in
a single scan
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