4,918 research outputs found
Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG--MRI
With a hybrid MEG--MRI device that uses the same sensors for both modalities,
the co-registration of MRI and MEG data can be replaced by an automatic
calibration step. Based on the highly accurate signal model of ultra-low-field
(ULF) MRI, we introduce a calibration method that eliminates the error sources
of traditional co-registration. The signal model includes complex sensitivity
profiles of the superconducting pickup coils. In ULF MRI, the profiles are
independent of the sample and therefore well-defined. In the most basic form,
the spatial information of the profiles, captured in parallel ULF-MR
acquisitions, is used to find the exact coordinate transformation required. We
assessed our calibration method by simulations assuming a helmet-shaped
pickup-coil-array geometry. Using a carefully constructed objective function
and sufficient approximations, even with low-SNR images, sub-voxel and
sub-millimeter calibration accuracy was achieved. After the calibration,
distortion-free MRI and high spatial accuracy for MEG source localization can
be achieved. For an accurate sensor-array geometry, the co-registration and
associated errors are eliminated, and the positional error can be reduced to a
negligible level.Comment: 11 pages, 8 figures. This work is part of the BREAKBEN project and
has received funding from the European Union's Horizon 2020 research and
innovation programme under grant agreement No 68686
A novel image space formalism of Fourier domain interpolation neural networks for noise propagation analysis
Purpose: To develop an image space formalism of multi-layer convolutional
neural networks (CNNs) for Fourier domain interpolation in MRI reconstructions
and analytically estimate noise propagation during CNN inference. Theory and
Methods: Nonlinear activations in the Fourier domain (also known as k-space)
using complex-valued Rectifier Linear Units are expressed as elementwise
multiplication with activation masks. This operation is transformed into a
convolution in the image space. After network training in k-space, this
approach provides an algebraic expression for the derivative of the
reconstructed image with respect to the aliased coil images, which serve as the
input tensors to the network in the image space. This allows the variance in
the network inference to be estimated analytically and to be used to describe
noise characteristics. Monte-Carlo simulations and numerical approaches based
on auto-differentiation were used for validation. The framework was tested on
retrospectively undersampled invivo brain images. Results: Inferences conducted
in the image domain are quasi-identical to inferences in the k-space,
underlined by corresponding quantitative metrics. Noise variance maps obtained
from the analytical expression correspond with those obtained via Monte-Carlo
simulations, as well as via an auto-differentiation approach. The noise
resilience is well characterized, as in the case of classical Parallel Imaging.
Komolgorov-Smirnov tests demonstrate Gaussian distributions of voxel magnitudes
in variance maps obtained via Monte-Carlo simulations. Conclusion: The
quasi-equivalent image space formalism for neural networks for k-space
interpolation enables fast and accurate description of the noise
characteristics during CNN inference, analogous to geometry-factor maps in
traditional parallel imaging methods
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
Advancements to Magnetic Resonance Flow Imaging in the Brain
abstract: Magnetic resonance flow imaging techniques provide quantitative and qualitative information that can be attributed to flow related clinical pathologies. Clinical use of MR flow quantification requires fast acquisition and reconstruction schemes, and minimization of post processing errors. The purpose of this work is to provide improvements to the post processing of volumetric phase contrast MRI (PCMRI) data, identify a source of flow bias for cine PCMRI that has not been previously reported in the literature, and investigate a dynamic approach to image bulk cerebrospinal fluid (CSF) drainage in ventricular shunts. The proposed improvements are implemented as three research projects.
In the first project, the improvements to post processing are made by proposing a new approach to estimating noise statistics for a single spiral acquisition, and using the estimated noise statistics to generate a mask distinguishing flow regions from background noise and static tissue in an image volume. The mask is applied towards reducing the computation time of phase unwrapping. The proposed noise estimation is shown to have comparable noise statistics as that of a vendor specific noise dynamic scan, with the added advantage of reduced scan time. The sparse flow region subset of the image volume is shown to speed up phase unwrapping for multidirectional velocity encoded 3D PCMRI scans. The second research project explores the extent of bias in cine PCMRI based flow estimates is investigated for CSF flow in the cerebral aqueduct. The dependance of the bias on spatial and temporal velocity gradient components is described. A critical velocity threshold is presented to prospectively determine the extent of bias as a function of scan acquisition parameters.
Phase contrast MR imaging is not sensitive to measure bulk CSF drainage. A dynamic approach using a CSF label is investigated in the third project to detect bulk flow in a ventricular shunt. The proposed approach uses a preparatory pulse to label CSF signal and a variable delay between the preparatory pulse and data acquisition enables tracking of the CSF bulk flow.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201
On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion
La imagen por resonancia magnética (MRI), hoy en dÃa, representa una potente herramienta para el diagnóstico clÃnico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos fÃsicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clÃnicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, asà como un incremento del tiempo de adquisición, aumentando asà tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clÃnica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatÃas y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hÃgado.Departamento de TeorÃa de la Señal y Comunicaciones e IngenierÃa TelemáticaDoctorado en TecnologÃas de la Información y las Telecomunicacione
A Study of Nonlinear Approaches to Parallel Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) has revolutionized radiology in the past four decades by its ability to visualize not only the detailed anatomical structures, but also function and metabolism information. A major limitation with MRI is its low imaging speed, which makes it difficult to image the moving objects. Parallel MRI (pMRI) is an emerging technique to increase the speed of MRI. It acquires the MRI data from multiple coils simultaneously such that fast imaging can be achieved by reducing the amount of data acquired in each coil. Several methods have developed to reconstruct the original image using the reduced data from multiple coils based on their distinct spatial sensitivities. Among the existing methods, Sensitivity Encoding (SENSE) and GeneRally Autocalibrating Partially Parallel Acquisition (GRAPPA) are commercially used reconstruction methods for parallel MRI. Both methods use linear approaches for image reconstruction. GRAPPA is known to outperform SENSE because no coil sensitivities are needed in reconstruction. However, GRAPPA can only accelerate the speed by a factor of 2-3. The objective of this dissertation is to develop novel techniques to significantly improve the acceleration factor upon the existing GRAPPA methods. Motivated by the success of recent study in our group which has demonstrated the benefit of nonlinear approaches for SENSE, in this dissertation, nonlinear approaches are studied for GRAPPA. Based on the fact that GRAPPA needs a calibration step before reconstruction, nonlinear models are investigated in both calibration and reconstruction using a kernel method widely used in machine learning. In addition, compressed sensing (CS), a nonlinear optimization technique will also be incorporated for even higher accelerations. In order to reduce the computation time, a nonlinear approach is proposed to reduce the effective number of coils in reconstruction. The imaging speed is expected to improve by a factor of 4-6 using the proposed nonlinear techniques. These new techniques will find many applications in accurate brain imaging, dynamic cardiac imaging, functional imaging, and so forth
Advances in diffusion MRI acquisition and processing in the Human Connectome Project
The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013
Registration Methods for Quantitative Imaging
At the core of most image registration problems is determining a spatial transformation that relates the physical coordinates of two or more images. Registration methods have become ubiquitous in many quantitative imaging applications. They represent an essential step for many biomedical and bioengineering applications. For example, image registration is a necessary step for removing motion and distortion related artifacts in serial images, for studying the variation of biological tissue properties, such as shape and composition, across different populations, and many other applications. Here fully automatic intensity based methods for image registration are reviewed within a global energy minimization framework. A linear, shift-invariant, stochastic model for the image formation process is used to describe several important aspects of typical implementations of image registration methods. In particular, we show that due to the stochastic nature of the image formation process, most methods for automatic image registration produce answers biased towards `blurred' images. In addition we show how image approximation and interpolation procedures necessary to compute the registered images can have undesirable effects on subsequent quantitative image analysis methods. We describe the exact sources of such artifacts and propose methods through which these can be mitigated. The newly proposed methodology is tested using both simulated and real image data. Case studies using three-dimensional diffusion weighted magnetic resonance images, diffusion tensor images, and two-dimensional optical images are presented. Though the specific examples shown relate exclusively to the fields of biomedical imaging and biomedical engineering, the methods described are general and should be applicable to a wide variety of imaging problems
- …