154 research outputs found
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
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
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
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
Spontaneous pregnancy after tracking ovulation during menstruation: A case report of a woman with premature ovarian insufficiency and repeated failure of in vitro fertilization
The diagnosis of premature ovarian insufficiency (POI) is devastating in women of reproductive age because of the small chance of spontaneous pregnancy. Here, we report a very rare case with POI and repeated failure of in vitro fertilization (IVF) where the final result was natural fertilization following guidance to have sexual intercourse during menstruation as ovulation was monitored. Estradiol valerate was used to increase the thickness of the endometrium and stop the menstrual bleeding. There was a serum level of 208.44 IU/L of human chorionic gonadotropin (HCG) 14 days after the ovulation. Later, a series of transvaginal ultrasounds also indicated a normal-appearing intra-uterine pregnancy. A healthy baby girl was delivered at term by means of cesarean section. Our report suggested that although the chance of spontaneous pregnancy is relatively low in patients with POI with repeated IVF failures, as long as ovulation does occur, even if it happens during menstruation, natural pregnancy is still worth trying with a series of proper and personalized treatments
Sex differences in non-communicable disease prevalence in China: a cross-sectional analysis of the China Health and Retirement Longitudinal Study in 2011
ObjectivesTo describe the sex differences in the prevalence of non-communicable diseases (NCDs) in adults aged 45 years or older in China.DesignCross-sectional study.SettingNationally representative sample of the Chinese population 2011.Participants8401 men and 8928 women over 45 years of age who participated in the first wave of the China Health and Retirement Longitudinal Study (CHARLS).Outcome measuresSelf-reported data on overall health and diagnosis of hypertension, dyslipidaemia, diabetes, heart disease, stroke, chronic lung disease, cancer or arthritis. Sex differences in NCDs were described using logistic regression to generate odds ratios (OR) with adjustment for sociodemographic factors and health-related behaviours. All analyses were stratified by age group for 45–64-year-old and ≥65-year-old participants.ResultsIn both age groups, men reported better overall health than women. The crude prevalence of heart disease, cancer and arthritis was higher while that of stroke and chronic lung disease was lower in women than in men. After adjustment, ORs (95% CI) for the 45–64 and ≥65 year age groups were 0.70 (0.58 to 0.84) and 0.66 (0.54 to 0.80), respectively, for arthritis for men compared with women. In contrast, ORs were 1.66 (1.09 to 2.52) and 2.12 (1.36 to 3.30) for stroke and 1.51 (1.21 to 1.89) and 1.43 (1.09 to 1.88) for chronic lung disease for men compared with women. ORs for heart disease (0.65 (0.52 to 0.80)) were lower in men than in women only in the 45–64 year age group.ConclusionsOdds of arthritis were lower while those of stroke and chronic lung disease were higher in men than in women in both age groups. However, odds of heart disease were lower in men than in women, but only in the group of individuals aged 45–64 years.</jats:sec
Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study
PurposeTo evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).MethodsData of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models’ performance was evaluated using the area under the curve (AUC).ResultsThere were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).ConclusionMisdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT
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