18 research outputs found
Denoising method for dynamic contrast-enhanced CT perfusion studies using three-dimensional deep image prior as a simultaneous spatial and temporal regularizer
This study aimed to propose a denoising method for dynamic contrast-enhanced
computed tomography (DCE-CT) perfusion studies using a three-dimensional deep
image prior (DIP), and to investigate its usefulness in comparison with total
variation (TV)-based methods with different regularization parameter (alpha)
values through simulation studies. In the proposed DIP method, the DIP was
incorporated into the constrained optimization problem for image denoising as a
simultaneous spatial and temporal regularizer, which was solved using the
alternating direction method of multipliers. In the simulation studies, DCE-CT
images were generated using a digital brain phantom and their noise level was
varied using the X-ray exposure noise model with different exposures (15, 30,
50, 75, and 100 mAs). Cerebral blood flow (CBF) images were generated from the
original contrast enhancement (CE) images and those obtained by the DIP and TV
methods using block-circulant singular value decomposition. The quality of the
CE images was evaluated using the peak signal-to-noise ratio (PSNR) and
structural similarity index (SSIM). To compare the CBF images obtained by the
different methods and those generated from the ground truth images, linear
regression analysis was performed. When using the DIP method, the PSNR and SSIM
were not significantly dependent on the exposure, and the SSIM was the highest
for all exposures. When using the TV methods, they were significantly dependent
on the exposure and alpha values. The results of the linear regression analysis
suggested that the linearity of the CBF images obtained by the DIP method was
superior to those obtained from the original CE images and by the TV methods.
Our preliminary results suggest that the DIP method is useful for denoising
DCE-CT images at ultra-low to low exposures and for improving the accuracy of
the CBF images generated from them
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction
Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image
(MRI) processing and achieves accurate MRI reconstruction from under-sampled
k-space data. According to the current research, there are still several
problems with dynamic MRI k-space reconstruction based on CS. 1) There are
differences between the Fourier domain and the Image domain, and the
differences between MRI processing of different domains need to be considered.
2) As three-dimensional data, dynamic MRI has its spatial-temporal
characteristics, which need to calculate the difference and consistency of
surface textures while preserving structural integrity and uniqueness. 3)
Dynamic MRI reconstruction is time-consuming and computationally
resource-dependent. In this paper, we propose a novel robust low-rank dynamic
MRI reconstruction optimization model via highly under-sampled and Discrete
Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition
Model (RDLEDM). Our method mainly includes linear decomposition, double Total
Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear
image domain error analysis, the noise is reduced after under-sampled and DFT
processing, and the anti-interference ability of the algorithm is enhanced.
Double TV and NN regularizations can utilize both spatial-temporal
characteristics and explore the complementary relationship between different
dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and
non-convexity of TV and NN terms, it is difficult to optimize the unified
objective model. To address this issue, we utilize a fast algorithm by solving
a primal-dual form of the original problem. Compared with five state-of-the-art
methods, extensive experiments on dynamic MRI data demonstrate the superior
performance of the proposed method in terms of both reconstruction accuracy and
time complexity
Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution
Multi-contrast magnetic resonance imaging (MRI) reflects information about
human tissue from different perspectives and has many clinical applications. By
utilizing the complementary information among different modalities,
multi-contrast super-resolution (SR) of MRI can achieve better results than
single-image super-resolution. However, existing methods of multi-contrast MRI
SR have the following shortcomings that may limit their performance: First,
existing methods either simply concatenate the reference and degraded features
or exploit global feature-matching between them, which are unsuitable for
multi-contrast MRI SR. Second, although many recent methods employ transformers
to capture long-range dependencies in the spatial dimension, they neglect that
self-attention in the channel dimension is also important for low-level vision
tasks. To address these shortcomings, we proposed a novel network architecture
with compound-attention and neighbor matching (CANM-Net) for multi-contrast MRI
SR: The compound self-attention mechanism effectively captures the dependencies
in both spatial and channel dimension; the neighborhood-based feature-matching
modules are exploited to match degraded features and adjacent reference
features and then fuse them to obtain the high-quality images. We conduct
experiments of SR tasks on the IXI, fastMRI, and real-world scanning datasets.
The CANM-Net outperforms state-of-the-art approaches in both retrospective and
prospective experiments. Moreover, the robustness study in our work shows that
the CANM-Net still achieves good performance when the reference and degraded
images are imperfectly registered, proving good potential in clinical
applications.Comment: This work has been submitted to the IEEE for possible publication.
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