Reconstructing undersampled MR Images by utilizingprincipal-component-analysis-based pattern recognition

Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal component analysis based on pattern recognition. The experiments demonstrate that this method can reduce aliasing artefacts and achieve a high peak signal-to-noise ratio compared to a compressed sensing reconstruction

Novel Magnetic Resonance Acquisition and Processing Strategies for Biological Tissue Characterisation

Proton magnetic resonance techniques have become indispensable for characterising tissues non-invasively. These methods provide abundant information regarding metabolism, morphology and histology of the sample under study. While these techniques were more expensive in the past compared to radioactive methods, modern advances in hardware and methodology provide the potential to use magnetic resonance systems more efficiently and widely. In this context, this thesis explored innovative magnetic resonance technologies from three independent perspectives which are suitable for tissue characterisation, utilising techniques from a wide range of disciplines including physics, engineering, biology and medical sciences. One strategy relates to compressed sensing magnetic resonance imaging, seeking to recover detailed features at high undersampling rates. A data-adaptive sparse transform facilitated by principal component analysis was introduced as an alternative to the conventional pre-defined sparse transform. Moreover, the principal component analysis was used in a recognition algorithm for the reconstruction of undersampled data. The performances of these approaches were studied in cases of localised changes in the acquired images. The results demonstrated that the recognition reconstruction algorithm performed better than wavelet compressed sensing. This progress can be utilised to accelerate current state of the art imaging protocols at high magnetic field strengths. Furthermore, the prior knowledge contained in high resolution databases may enhance imaging capabilities of technologies at low magnetic field strengths. A second approach exploits nuclear magnetic resonance diffusion contrast instead of contrast agents for tissue characterisation. Microstructural information and global fractional anisotropy can be obtained from diffusion-diffusion correlation spectroscopy via a novel multi-dimensional gradient scheme. The concept was validated by random walk simulations and experiments of biological samples. Both correlation maps and global fractional anisotropy of in vitro healthy and tumour-bearing mouse brains were found to be different, thus providing a potential application of the proposed scheme in diffusion oncology. In addition, a threshold algorithm on the selection of a region of interest was implemented to minimise inter-observer variations. This technique was applied to a pilot study of diffusion weighted imaging data which were acquired from patients after x-ray mammography indicated lesions. The statistical analysis revealed an optimal threshold similar to values commonly used in positron emission tomography. Apart from selecting regions automatically, various data processing methods were implemented and compared with each other regarding their diagnostic accuracies. This field study provides opportunities for standardising procedures in diffusion weighted mammography, which may be integrated into clinical analysis in the future

Reconstructing undersampled MR Images by utilizingprincipal-component-analysis-based pattern recognition

Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal component analysis based on pattern recognition. The experiments demonstrate that this method can reduce aliasing artefacts and achieve a high peak signal-to-noise ratio compared to a compressed sensing reconstruction

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s, multi-dimensional magnetic resonance (MR) has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions. MR spectroscopy beyond one dimension allows the study of the correlation, exchange processes, and separation of overlapping spectral information. The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media. Apart from Fourier transform, methods have been developed for processing the multi-dimensional time-domain data, identifying the fluid components, and estimating pore surface permeability via joint relaxation and diffusion spectra. Through the resolution of spectroscopic signals with spatial encoding gradients, multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases. Signals in each voxel are usually expressed as multi-exponential decay, representing microstructures or environments along multiple pore scales. The separation of contributions from different environments is a common ill-posed problem, which can be resolved numerically. Moreover, the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra. This paper reviews the algorithms that have been proposed to process multi-dimensional MR datasets in different scenarios. Detailed information at the microscopic level, such as tissue components, fluid types and food structures in multi-disciplinary sciences, could be revealed through multi-dimensional MR

Reconstructing undersampled MR Images by utilizingprincipal-component-analysis-based pattern recognition

Compressed sensing technique is a recent framework for signal sampling and recovery. It allows signal acquisition with less sampling than required by Nyquist-Shannon theorem and reduces data acquisition time in MRI. When the sampling rate is low, prior knowledge is essential to reconstruct the missing features. In this paper, a different reconstruction method is proposed by using the principal component analysis based on pattern recognition. The experiments demonstrate that this method can reduce aliasing artefacts and achieve a high peak signal-to-noise ratio compared to a compressed sensing reconstruction

Determining mean fractional anisotropy using DDCOSY: preliminary results in biological tissues

Complex materials are ubiquitous in science, engineering and nature. One important parameter for characterising their morphology is the degree of anisotropy. Magnetic resonance imaging offers non-invasive methods for quantitative measurements of the materials anisotropy, most commonly via diffusion tensor imaging and the subsequent extraction of the spatially resolved fractional anisotropy (FA) value. Here, we propose an alternative way of determining the FA as a sample average for cases where spatially resolved methods are not needed or not applicable. It is based on a particular diffusion–diffusion correlation spectroscopy protocol, allowing for the extraction of the mean (i.e. sample averaged) FA value. We demonstrate that mean FA values obtained from three anisotropic biological tissues are consistent with those extracted using diffusion tensor imaging. Moreover, we show that differences of mean FA values in healthy and tumour-bearing mouse brains allow to distinguish these tissue types. We anticipate that the proposed method will be beneficial in the wider context of medical and material science

A new approach of two-dimensional the NMR relaxation measurement in flowing fluid

Driven-equilibrium fast saturation recovery (DEFSR), as a new method for two-dimensional (2-D) nuclear magnetic resonance (NMR) relaxation measurement based on pulse sequence in flowing fluid, is proposed. The two-dimensional functional relationship between the ratio of transverse relaxation time to longitudinal relaxation time of fluid (7yr2) and T1 distribution is obtained by means of DEFSR with only two one-dimensional measurements. The rapid measurement of relaxation characteristics for flowing fluid is achieved. A set of the down-hole NMR fluid analysis system is independently designed and developed for the fluid measurement. The accuracy and practicability of DEFSR are demonstrated

Symmetry of the gradient profile as second experimental dimension in the short-time expansion of the apparent diffusion coefficient as measured with NMR diffusometry

The time-dependent apparent diffusion coefficient as measured by pulsed gradient NMR can be used to estimate parameters of porous structures including the surface-to-volume ratio and the mean curvature of pores. In this work, the short-time diffusion limit and in particular the influence of the temporal profile of diffusion gradients on the expansion as proposed by Mitra et al. (1993) is investigated. It is shown that flow-compensated waveforms, i.e. those whose first moment is zero, are blind to the term linear in observation time, which is the term that is proportional to mean curvature and surface permeability. A gradient waveform that smoothly interpolates between flow-compensated and bipolar waveform is proposed and the degree of flow-compensation is used as a second experimental dimension. This two-dimensional ansatz is shown to yield an improved precision when characterizing the confining domain. This technique is demonstrated with simulations and in experiments performed with cylindrical capillaries of 100 μm radius