The increasing demands for improving magnetic resonance imaging (MRI) quality, especially reducing the imaging time have been driving the channel number of parallel magnetic resonance imaging (Parallel MRI) to increase. When the channel number increases to 64 or even 128, the traditional method of stacking the same number of radio-frequency (RF) receivers with very low level of integration becomes expensive and cumbersome. However, the cost, size, power consumption of the Parallel MRI receivers can be dramatically reduced by designing a whole receiver front-end even multiple receiver front-ends on a single chip using CMOS technology, and multiplexing the output signal of each receiver front-end into one channel so that as much hardware resource can be shared by as many channels as possible, especially the digitizer. The main object of this research is focused on the analysis and design of fully integrated multi-channel RF receiver and multiplexing technology. First, different architectures of RF receiver and different multiplexing method are analyzed. After comparing the advantages and the disadvantages of these architectures, an architecture of receiver front-end which is most suitable for fully on-chip multi-channel design is proposed and a multiplexing method is selected. According to this proposed architecture, a four-channel receiver front-end was designed and fabricated using TSMC 0.18μm technology on a single chip and methods of testing in the MRI system using parallel planar coil array and phase coil array respectively as target coils were presented. Each channel of the receiver front-end includes an ultra low noise amplifier (LNA), a quadrature image rejection down-converter, a buffer, and a low-pass filter (LPF) which also acts as a variable gain amplifier (VGA). The quadrature image rejection downconverter consists of a quadrature generator, a passive mixer with a transimpedance amplifier which converts the output current signal of the passive mixer into voltage signal while acts as a LPF, and a polyphase filter after the TIA. The receiver has an over NF of 0.935dB, variable gain from about 80dB to 90dB, power consumption of 30.8mW, and chip area of 6mm2. Next, a prototype of 4-channel RF receiver with Time Domain Multiplexing (TDM) on a single printed circuit board (PCB) was designed and bench-tested. Then Parallel MRI experiment was carried out and images were acquired using this prototype. The testing results verify the proposed concepts.

Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such as the computed tomography (CT), MRI features a relatively long scan time that gives rise to many potential issues. The advent of parallel MRI, which employs multiple receiver coils, has started a new era in speeding up the scan of MRI by reducing the number of data acquisitions. However, the finally recovered images from under-sampled data sets often suffer degraded image quality. This thesis explores methods that incorporate prior knowledge of the image to be reconstructed to achieve improved image recovery in parallel MRI, following the philosophy that ‘if some prior knowledge of the image to be recovered is known, the image could be recovered better than without’. Specifically, the prior knowledge of image sparsity is utilized. Image sparsity exists in different domains. Image sparsity in the image domain refers to the fact that the imaged object only occupies a portion of the imaging field of view; image sparsity may also exist in a transform domain for which there is a high level of energy concentration in the image transform. The use of both types of sparsity is considered in this thesis. There are three major contributions in this thesis. The first contribution is the development of ‘GUISE’. GUISE employs an adaptive sampling design method that achieves better exploitation of image domain sparsity in parallel MRI. Secondly, the development of ‘PBCS’ and ‘SENSECS’. PBCS achieves better exploitation of transform domain sparsity by incorporating a prior estimate of the image to be recovered. SENSECS is an application of PBCS that achieves better exploitation of transform domain sparsity in parallel MRI. The third contribution is the implementation of GUISE and PBCS in contrast enhanced MR angiography (CE MRA). In their applications in CE MRA, GUISE and PBCS share the common ground of exploiting the high sparsity of the contrast enhanced angiogram. The above developments are assessed in various ways using both simulated and experimental data. The potential extensions of these methods are also suggested.

Parallel magnetic resonance imaging (MRI) with non-Cartesian sampling pattern is a promising technique that increases the scan speed using multiple receiver coils with reduced samples. However, reconstruction is challenging due to the increased complexity. Three reconstruction methods were evaluated: gridding, blocked uniform resampling (BURS) and non-uniform FFT (NUFFT). Computer simulations of parallel reconstruction were performed. Root mean square error (RMSE) of the reconstructed images to the simulated phantom were used as image quality criterion. Gridding method showed best RMSE performance. Two type of a priori constraints to reduce noise and artifacts were evaluated: edge preserving penalty, which suppresses noise and aliasing artifact in image while preventing over-smoothness, and object support penalty, which reduces background noise amplification. A trust region based step-ratio method that iteratively calculates the penalty coefficient was proposed for the penalty functions. Two methods to alleviate computation burden were evaluated: smaller over sampling ratio, and interpolation coefficient matrix compression. The performance were individually tested using computer simulations. Edge preserving penalty and object support penalty were shown to have consistent improvement on RMSE. The performance of calculated penalty coefficients on the two penalties were close to the best RMSE. Oversampling ratio as low as 1.125 was shown to have impact of less than one percent on RMSE for the radial sampling pattern reconstruction. The value reduced the three dimensional data requirement to less than 1/5 of what the conventional 2x grid needed. Interpolation matrix compression with compression ratio up to 50 percent showed small impact on RMSE. The proposed method was validated on 25 MR data set from a GE MR scanner. Six image quality metrics were used to evaluate the performance. RMSE, normalized mutual information (NMI) and joint entropy (JE) relative to a reference image from a separate body coil scan were used to verify the fidelity of reconstruction to the reference. Region of interest (ROI) signal to noise ratio (SNR), two-data SNR and background noise were used to validate the quality of the reconstruction. The proposed method showed higher ROI SNR, two-data SNR, and lower background noise over conventional method with comparable RMSE, NMI and JE to the reference image at reduced computer resource requirement.

Magnetic Resonance Imaging (MRI) is now increasingly being used for fast imaging applications such as real-time cardiac imaging, functional brain imaging, contrast enhanced MRI, etc. Imaging speed in MRI is mainly limited by different imaging parameters selected by the pulse sequences, the subject being imaged and the RF hardware system in operation. New pulse sequences have been developed in order to decrease the imaging time by a faster k-space scan. However, they may not be fast enough to facilitate imaging in real time. Parallel MRI (pMRI), a technique initially used for improving image SNR, has emerged as an effective complementary approach to reduce image scan-time. Five methods, viz., SENSE [Pruesmann, 1999], PILS [Griswold, 2000], SMASH [Sodickson, 1997], GRAPPA [Griswold, 2002] and SPACE RIP [Kyriakos, 2000]; developed in the past decade have been studied, simulated and compared in this research. Because of the dependence of the parallel imaging methods on numerous factors such as receiver coil configuration, k-space subsampling factor, k-space coverage in the imaging environment, there is a critical need to find the method giving the best results under certain imaging conditions. The tools developed in this research help the selection of the optimal method for parallel imaging depending on a particular imaging environment and scanning parameters. Simulations on real MR phased-array data show that SENSE and GRAPPA provide better image reconstructions when compared to the remaining techniques.

La plupart des constructeurs de systèmes d'imagerie par résonance magnétique (IRM) proposent un large choix d'applications de post-traitement sur les données IRM reconstruites a posteriori, mais très peu de ces applications peuvent être exécutées en temps réel pendant l'examen. Mises à part certaines solutions dédiées à l'IRM fonctionnelle permettant des expériences relativement simples ainsi que d'autres solutions pour l'IRM interventionnelle produisant des scans anatomiques pendant un acte de chirurgie, aucun outil n'a été développé pour l'IRM pondérée en diffusion (IRMd). Cependant, comme les examens d'IRMd sont extrêmement sensibles à des perturbations du système hardware ou à des perturbations provoquées par le sujet et qui induisent des données corrompues, il peut être intéressant d'investiguer la possibilité de reconstruire les données d'IRMd directement lors de l'examen. Cette thèse est dédiée à ce projet innovant. La contribution majeure de cette thèse a consisté en des solutions de débruitage des données d'IRMd en temps réel. En effet, le signal pondéré en diffusion peut être corrompu par un niveau élevé de bruit qui n'est plus gaussien, mais ricien ou chi non centré. Après avoir réalisé un état de l'art détaillé de la littérature sur le bruit en IRM, nous avons étendu l'estimateur linéaire qui minimise l'erreur quadratique moyenne (LMMSE) et nous l'avons adapté à notre cadre de temps réel réalisé avec un filtre de Kalman. Nous avons comparé les performances de cette solution à celles d'un filtrage gaussien standard, difficile à implémenter car il nécessite une modification de la chaîne de reconstruction pour y être inséré immédiatement après la démodulation du signal acquis dans l'espace de Fourier. Nous avons aussi développé un filtre de Kalman parallèle qui permet d'appréhender toute distribution de bruit et nous avons montré que ses performances étaient comparables à celles de notre méthode précédente utilisant un filtre de Kalman non parallèle. Enfin, nous avons investigué la faisabilité de réaliser une tractographie en temps-réel pour déterminer la connectivité structurelle en direct, pendant l'examen. Nous espérons que ce panel de développements méthodologiques permettra d'améliorer et d'accélérer le diagnostic en cas d'urgence pour vérifier l'état des faisceaux de fibres de la substance blanche. / Most magnetic resonance imaging (MRI) system manufacturers propose a huge set of software applications to post-process the reconstructed MRI data a posteriori, but few of them can run in real-time during the ongoing scan. To our knowledge, apart from solutions dedicated to functional MRI allowing relatively simple experiments or for interventional MRI to perform anatomical scans during surgery, no tool has been developed in the field of diffusion-weighted MRI (dMRI). However, because dMRI scans are extremely sensitive to lots of hardware or subject-based perturbations inducing corrupted data, it can be interesting to investigate the possibility of processing dMRI data directly during the ongoing scan and this thesis is dedicated to this challenging topic. The major contribution of this thesis aimed at providing solutions to denoise dMRI data in real-time. Indeed, the diffusion-weighted signal may be corrupted by a significant level of noise which is not Gaussian anymore, but Rician or noncentral chi. After making a detailed review of the literature, we extended the linear minimum mean square error (LMMSE) estimator and adapted it to our real-time framework with a Kalman filter. We compared its efficiency to the standard Gaussian filtering, difficult to implement, as it requires a modification of the reconstruction pipeline to insert the filter immediately after the demodulation of the acquired signal in the Fourier space. We also developed a parallel Kalman filter to deal with any noise distribution and we showed that its efficiency was quite comparable to the non parallel Kalman filter approach. Last, we addressed the feasibility of performing tractography in real-time in order to infer the structural connectivity online. We hope that this set of methodological developments will help improving and accelerating a diagnosis in case of emergency to check the integrity of white matter fiber bundles.