1,072 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Inhomogeneity Correction in High Field Magnetic Resonance Images
Projecte realitzat en col.laboració amb el centre Swiss Federal Institute of Technology (EPFL)Magnetic Resonance Imaging, MRI, is one of the most powerful and harmless ways to study human inner tissues. It gives the chance of having an accurate insight into the physiological condition of the human body, and specially, the brain. Following this aim, in the last decade MRI has moved to ever higher magnetic field strength that allow us to get advantage of a better signal-to-noise ratio. This improvement of the SNR, which increases almost linearly with the field strength, has several advantages: higher spatial resolution and/or faster imaging, greater spectral dispersion, as well as an enhanced sensitivity to magnetic susceptibility. However, at high magnetic resonance imaging, the interactions between the RF pulse and the high permittivity samples, which causes the so called Intensity Inhomogeneity or B1 inhomogeneity, can no longer be negligible. This inhomogeneity causes undesired efects that afects quantitatively image analysis and avoid the application classical intensity-based segmentation and other medical functions. In this Master thesis, a new method for Intensity Inhomogeneity correction at high ¯eld is presented. At high ¯eld is not possible to achieve the estimation and the correction directly from the corrupted data. Thus, this method attempt the correction by acquiring extra information during the image process, the RF map. The method estimates the inhomogeneity by the comparison of both acquisitions. The results are compared to other methods, the PABIC and the Low-Pass Filter which try to correct the inhomogeneity directly from the corrupted data
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Illumination Correction on Biomedical Images
RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the approach has been compared successfully against the Guillemaud filter and the SPM2 method. Moreover, the method has been successfully applied on several real MR images of the brain (0.18 T, 1.5 T and 7 T). The description of the overall technique is reported along with the experimental results that show its effectiveness in different anatomical regions and its ability to compensate both underexposed and overexposed areas. Our approach is also effective on non-radiological images, like retinal ones
Monte Carlo Framework for Prostate Cancer Correction and Reconstruction in Endorectal Multi-parametric MRI
Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multi-parametric MRI has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci which is required to plan treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multi-parametric data sets. An endorectal receiver coil is often used to improve signal-to-noise ratio (SNR) and aid in detection of smaller cancer foci. Despite increased SNR, intensity bias fields can exist where nearest the endorectal coil the signal is greater than those regions farther from the coil. Weak delineation of the prostate as well as poor prostate gland visualization can greatly impact the ease and accuracy of diagnosis. For this reason, there is a need for an automated system which can correct endorectal multi-parametric MRI for enhanced visualization. A framework using Monte Carlo sampling techniques has been developed for prostate cancer correction and reconstruction in endorectal multi-parametric MRI. Its performance against state-of-the-art approaches demonstrate improved results for visualization and prostate delineation.
The first step in the proposed framework involves reconstructing an intensity bias-free image. Using importance-weighted Monte Carlo sampling, the intensity bias field is estimated to approximate the bias-free result. However, the reconstruction is still pervaded by noise which becomes amplified and non-stationary as a result of intensity bias correction. The second step in the framework applies a spatially-adaptive Rician distributed Monte Carlo sampling approach while accounting for the endorectal coil's underlying SNR characteristics.
To evaluate the framework, the individual steps are compared against state-of-the-art approaches using phantoms and real patient data to quantify visualization improvement. The intensity bias correction technique is critiqued based on detail preservation and delineation of the prostate from the background as well as improvement in tumor identification. The noise compensation approach is considered based on the noise suppression, contrast of tissue as well as preservation of details and texture. Utilizing quantitative and qualitative metrics in addition to visual analysis, the experimental results demonstrated that the proposed framework allows for improved visualization, with increased delineation of the prostate and preservation of tissue textures and details. This allows radiologists to more easily identify characteristics of cancerous and healthy tissue leading to more accurate and confident diagnoses
MR coil sensitivity inhomogeneity correction for plaque characterization in carotid arteries
We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity. With carotid surface coils, challenges include: (1) a steep bias field with an 80% change; (2) presence of nearby muscular structures lacking high frequency information to distinguish bias from anatomical features; (3) many confounding zero-valued voxels subject to fat suppression, blood flow cancellation, or air, which are not subject to coil sensitivity; and (4) substantial noise. Bias was corrected using a modification of the adaptive fuzzy c-mean method reported by Pham et al. (IEEE TMI, 18:738-752), whereby a bias field modeled as a mechanical membrane was iteratively improved until cluster means no longer changed. Because our images were noisy, we added a noise reduction filtering step between iterations and used approximate to5 classes. In a digital phantom having a bias field measured from our MR system, variations across an area comparable to a carotid artery were reduced from 50% t
Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images
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