129 research outputs found
Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications.
Quantitative susceptibility mapping (QSM) is a recently developed MRI technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field is a result of the superposition of the dipole fields generated by all voxels and that each voxel has its unique magnetic susceptibility. QSM provides improved contrast to noise ratio for certain tissues and structures compared to its magnitude counterpart. More importantly, magnetic susceptibility is a direct reflection of the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism generating susceptibility contrast within tissues and some associated applications
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
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
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