360 research outputs found
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
Neural Radiance Field (NeRF) has widely received attention in Sparse-View
Computed Tomography (SVCT) reconstruction tasks as a self-supervised deep
learning framework. NeRF-based SVCT methods represent the desired CT image as a
continuous function of spatial coordinates and train a Multi-Layer Perceptron
(MLP) to learn the function by minimizing loss on the SV sinogram. Benefiting
from the continuous representation provided by NeRF, the high-quality CT image
can be reconstructed. However, existing NeRF-based SVCT methods strictly
suppose there is completely no relative motion during the CT acquisition
because they require \textit{accurate} projection poses to model the X-rays
that scan the SV sinogram. Therefore, these methods suffer from severe
performance drops for real SVCT imaging with motion. In this work, we propose a
self-calibrating neural field to recover the artifacts-free image from the
rigid motion-corrupted SV sinogram without using any external data.
Specifically, we parametrize the inaccurate projection poses caused by rigid
motion as trainable variables and then jointly optimize these pose variables
and the MLP. We conduct numerical experiments on a public CT image dataset. The
results indicate our model significantly outperforms two representative
NeRF-based methods for SVCT reconstruction tasks with four different levels of
rigid motion.Comment: 5 page
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Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.
The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery
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Oscillation-specific nodal alterations in early to middle stages Parkinsons disease.
Background: Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinsons disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods: Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results: Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions: Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Parallel imaging is a commonly used technique to accelerate magnetic
resonance imaging (MRI) data acquisition. Mathematically, parallel MRI
reconstruction can be formulated as an inverse problem relating the sparsely
sampled k-space measurements to the desired MRI image. Despite the success of
many existing reconstruction algorithms, it remains a challenge to reliably
reconstruct a high-quality image from highly reduced k-space measurements.
Recently, implicit neural representation has emerged as a powerful paradigm to
exploit the internal information and the physics of partially acquired data to
generate the desired object. In this study, we introduced IMJENSE, a
scan-specific implicit neural representation-based method for improving
parallel MRI reconstruction. Specifically, the underlying MRI image and coil
sensitivities were modeled as continuous functions of spatial coordinates,
parameterized by neural networks and polynomials, respectively. The weights in
the networks and coefficients in the polynomials were simultaneously learned
directly from sparsely acquired k-space measurements, without fully sampled
ground truth data for training. Benefiting from the powerful continuous
representation and joint estimation of the MRI image and coil sensitivities,
IMJENSE outperforms conventional image or k-space domain reconstruction
algorithms. With extremely limited calibration data, IMJENSE is more stable
than supervised calibrationless and calibration-based deep-learning methods.
Results show that IMJENSE robustly reconstructs the images acquired at
5 and 6 accelerations with only 4 or 8
calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and
19.5% undersampling rates. The high-quality results and scanning specificity
make the proposed method hold the potential for further accelerating the data
acquisition of parallel MRI
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
In this paper, we propose an efficient self-supervised arbitrary-scale
super-resolution (SR) framework to reconstruct isotropic magnetic resonance
(MR) images from anisotropic MRI inputs without involving external training
data. The proposed framework builds a training dataset using in-the-wild
anisotropic MR volumes with arbitrary image resolution. We then formulate the
3D volume SR task as a SR problem for 2D image slices. The anisotropic volume's
high-resolution (HR) plane is used to build the HR-LR image pairs for model
training. We further adapt the implicit neural representation (INR) network to
implement the 2D arbitrary-scale image SR model. Finally, we leverage the
well-trained proposed model to up-sample the 2D LR plane extracted from the
anisotropic MR volumes to their HR views. The isotropic MR volumes thus can be
reconstructed by stacking and averaging the generated HR slices. Our proposed
framework has two major advantages: (1) It only involves the
arbitrary-resolution anisotropic MR volumes, which greatly improves the model
practicality in real MR imaging scenarios (e.g., clinical brain image
acquisition); (2) The INR-based SR model enables arbitrary-scale image SR from
the arbitrary-resolution input image, which significantly improves model
training efficiency. We perform experiments on a simulated public adult brain
dataset and a real collected 7T brain dataset. The results indicate that our
current framework greatly outperforms two well-known self-supervised models for
anisotropic MR image SR tasks.Comment: 10 pages, 5 figure
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Emerging neural reconstruction techniques based on tomography (e.g., NeRF,
NeAT, and NeRP) have started showing unique capabilities in medical imaging. In
this work, we present a novel Polychromatic neural representation (Polyner) to
tackle the challenging problem of CT imaging when metallic implants exist
within the human body. The artifacts arise from the drastic variation of
metal's attenuation coefficients at various energy levels of the X-ray
spectrum, leading to a nonlinear metal effect in CT measurements.
Reconstructing CT images from metal-affected measurements hence poses a
complicated nonlinear inverse problem where empirical models adopted in
previous metal artifact reduction (MAR) approaches lead to signal loss and
strongly aliased reconstructions. Polyner instead models the MAR problem from a
nonlinear inverse problem perspective. Specifically, we first derive a
polychromatic forward model to accurately simulate the nonlinear CT acquisition
process. Then, we incorporate our forward model into the implicit neural
representation to accomplish reconstruction. Lastly, we adopt a regularizer to
preserve the physical properties of the CT images across different energy
levels while effectively constraining the solution space. Our Polyner is an
unsupervised method and does not require any external training data.
Experimenting with multiple datasets shows that our Polyner achieves comparable
or better performance than supervised methods on in-domain datasets while
demonstrating significant performance improvements on out-of-domain datasets.
To the best of our knowledge, our Polyner is the first unsupervised MAR method
that outperforms its supervised counterparts.Comment: 19 page
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