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

Subspace estimation for subspace-based magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) is a powerful technique that offers us the ability to non-invasively image chemical distributions within the human body. However, due to its inherently poor trade-off between imaging speed, resolution, and signal-to-noise ratio (SNR), MRSI has remained impractical for many research and clinical applications. A large body of work has been done to improve this trade-off. Recently new subspace-based imaging methods have also been proposed as a means of dramatically accelerating MRSI. By taking advantage of the properties of a partially separable (PS) signal model, subspace-based methods offer increased flexibility in acquisition as well as image reconstruction, and thereby allow high-resolution, high-SNR MRSI images to be obtained in a fraction of the time required by standard techniques. An important ingredient common to all subspace-based imaging methods is the estimation of the subspace structure of the high-dimensional image function. However, accurate subspace estimation in the presence of noise and inhomogeneity in the main magnetic field is challenging. To this end we propose a novel method for subspace estimation which utilizes a regularized-reconstruction approach to correct for the effects of field inhomogeneity and noise. Carefully designed numerical simulations and experimental studies have been performed to evaluate the performance of the proposed method in a variety of experimental conditions. Results from these data show that the proposed method is able to obtain an accurate subspace estimation, either in terms of a projection error metric or by inspecting the residual after projecting the fully sampled data onto the estimated subspaces. Additionally, in vivo MRSI data was acquired to illustrate that the subspace estimated by the proposed method leads to high-quality spatiospectral reconstructions

23Na multi-quantum coherences: from cellular spectroscopy to clinical imaging development

Non-invasive biological tissue information is vital for medical diagnostics and treat- ment monitoring. While standard 1H magnetic resonance imaging (MRI) methods show detailed morphological information, 23Na MRI provides additional biochemical information about the tissue. Sodium nuclei have spin 3/2 and, therefore, can exhibit higher quantum coherence signals. Multi-quantum (MQ) imaging offers additional information compared to standard SQ sodium, which focuses on tissue sodium concentration (TSC), e.g., it is hard to discern edema and tumor regions, both exhibiting higher TSC. 23Na triple-quantum (TQ) signals are of high interest to probe the molecular environment in tissues and alleviate this problem. The focus of this thesis was on the 23Na TQ signal from preclinical investigations on cells via a simulation study and, finally, a transfer of preclinical and simulation find- ings into the development of an optimal clinical MQ imaging sequence, CRISTINA. First, the physiological importance was studied at ultra-high field, 9.4 T, to gain in- sight into TQ signal changes under different cellular conditions. An MR-compatible bioreactor setup allowed for finely tunable TQ signal monitoring of cell lines human liver cells (HEP G2) and neonatal cardiomyocytes of mice. Both cell lines showed a TQ signal in vital state, under standard perfusion [0.26%,15σ], normalized to the SQ signal. Hypertrophy was simulated with oxygen and nutrient stop and resulted in a TQ signal to 56% of the initial value [0.15%,24σ]. Re-perfusion resulted in a come back of the TQ signal to 92% of the initial value. Reference measurements without cells as well as dead cells showed a TQ signal of [0.06%,1σ and 0.016%,1σ]. Further, the long-standing debate of TQ signal connection to intracellular space was investigated based on liposomal cell-phantoms and it was shown that the TQ signal in liposomes was related to the interaction of the sodium ions with the double lipid membrane, which is constituted of negatively charged fatty acids. A single-voxel localization technique was developed on the preclinical system as the first step in the direction of a clinical sequence and tested on phantoms and in-vivo rat. Second, simulation of different phase-cycle schemes of the standard three-pulses coherence transfer technique was performed. A unified framework was developed to compare and find an optimum. Destructive effects of B0 inhomogeneity were investigated and verified for Ω(Hz) = (kπ + ξ)/(τ1), k ∈ Z. Stimulated echo signal stood as further potential biases but resulted in a continuous offset after Fourier Transformation. Third, knowledge from part I and II was transferred to develop an efficient clinical vii MQ imaging method: CRISTINA, a 2D Cartesian MQ, multi-echo imaging sequence for clinical use. A Multi-parameter fit routine provided T2 relaxations maps and ratio of TQ to SQ signals which could be of interest to monitor pathologies in future. CRISTINA was tested and optimized on phantoms and in vivo on 5 healthy brain volunteers. A linear relationship was found for the ratio TQ over SQ signal against agar concentrations (R2 =0.87, p-value = 0.0007) as well as for the SQ signal against TSC (R2 =0.75, p-value = 0.006)

Noise-reduction techniques for 1H-FID-MRSI at 14.1T: Monte-Carlo validation & in vivo application

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high-resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, the Marchenko-Pastur principal component analysis (MP-PCA) based denoising and the low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential and impact on preclinical 14.1T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte-Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio SNR while preserving noise properties in each spectrum for both in vivo and Monte-Carlo datasets. Relative metabolite concentrations were not significantly altered by either methods and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted on lower concentrated metabolites. Our study provided a framework on how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care especially for low-concentrated metabolites.Comment: Brayan Alves and Dunja Simicic are joint first authors. Currently in revision for NMR in Biomedicin

A subspace approach to spectral quantification for MR spectroscopic imaging

The problem of spectral quantification for magnetic resonance spectroscopic imaging (MRSI) is addressed in this thesis. We present a novel approach to solving this problem, incorporating both spatial and spectral prior information. More specifically, a new signal model is proposed which represents the spectral variations of each molecule as a subspace and the entire spectrum as a union-of-subspaces. The proposed model enables an efficient computational framework to quantify the unknown spectral parameters using both spectral and spatial prior information. Particularly, based on this model, the spectral quantification can be solved in two steps: (1) subspace estimation based on the empirical distributions of the spectral parameters obtained by initial spectral quantification imposing the spectral constraints, and (2) parameter estimation for the union-of-subspaces model imposing the spatial constraints. The proposed method has been evaluated using both simulated and experimental data, producing very impressive results. The resulting algorithm is expected to be useful for any metabolic imaging studies using MRSI. In this thesis, background materials including a brief review of the existing spectral quantification methods are firstly presented. Then the proposed subspace spectral model is introduced followed by a detailed description of the resulting quantification algorithm. Finally, spectral quantification results from both simulated and in vivo MRSI data are presented to demonstrate the performance of the proposed method

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

Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Magnetic resonance spectroscopic imaging (MRSI) is a rapidly developing medical imaging modality, capable of conferring both spatial and spectral information content, and has become a powerful clinical tool. The ability to non-invasively observe spatial maps of metabolite concentrations, for instance, in the human brain, can offer functional, as well as pathological insights, perhaps even before structural aberrations or behavioral symptoms are evinced. Despite its lofty clinical prospects, MRSI has traditionally remained encumbered by a number of practical limitations. Of primary concern are the vastly reduced concentrations of tissue metabolites when compared to that of water, which forms the basis for conventional MR imaging. Moreover, the protracted exam durations required by MRSI routinely approach the limits for patient compliance. Taken in conjunction, the above considerations effectively circumscribe the data collection process, ultimately translating to coarse image resolutions that are of diminished clinical utility. Such shortcomings are compounded by spectral contamination artifacts due to the system pointspread function, which arise as a natural consequence when reconstructing non-band-limited data by the inverse Fourier transform. These artifacts are especially pronounced near regions characterized by substantial discrepancies in signal intensity, for example, the interface between normal brain and adipose tissue, whereby the metabolite signals are inundated by the dominant lipid resonances. In recent years, concerted efforts have been made to develop alternative, non-Fourier MRSI reconstruction strategies that aim to surmount the aforementioned limitations. In this dissertation, we build upon the burgeoning medley of innovative and promising techniques, proffering a novel superresolution reconstruction framework predicated on the recent interest in low-rank signal modeling, along with state-of-the-art regularization methods. The proposed framework is founded upon a number of key tenets. Firstly, we proclaim that the underlying spatio-spectral distribution of the investigated object admits a bilinear representation, whereby spatial and spectral signal components can be effectively segregated. We further maintain that the dimensionality of the subspace spanned by the components is, in principle, bounded by a modest number of observable metabolites. Secondly, we assume that local susceptibility effects represent the primary sources of signal corruption that tend to disallow such representations. Finally, we assert that the spatial components belong to a class of real-valued, non-negative, and piecewise linear functions, compelled in part through the use of a total variation regularization penalty. After demonstrating superior spatial and spectral localization properties in both numerical and physical phantom data when compared against standard Fourier methods, we proceed to evaluate reconstruction performance in typical in vivo settings, whereby the method is extended in order to promote the recovery of signal variations throughout the MRSI slice thickness. Aside from the various technical obstacles, one of the cardinal prospective challenges for high-resolution MRSI reconstruction is the shortfall of reliable ground truth data prudent for validation, thereby prompting reservations surrounding the resulting experimental outcomes. [...

Advancing Magnetic Resonance Spectroscopy and Endoscopy with Prior Knowledge

Reconstruction is key to the generation of anatomic, functional and biochemical information in the field of Magnetic Resonance (MR) in medicine. Here, prior knowledge based on various conditions is utilized through reconstruction to accelerate current MR techniques and reduce artifacts. First, prior knowledge from Magnetic Resonance Imaging (MRI) is exploited to accelerate spatial localization in Magnetic Resonance Spectroscopy (MRS). The MRS information is contained in one extra chemical shift dimension, beyond the three spatial dimensions of MRI, and can provide valuable in vivo metabolic information for the study of numerous diseases. However, its research and clinical applications are often compromised by long scan times. Here, a new method of localized Spectroscopy with Linear Algebraic Modeling (SLAM) is proposed for accelerating MRS scans. The method assumes pre-conditions that the MRS scan is preceded by a scout MRI scan and that a compartment-averaged MRS measurement will suffice for the assessment of metabolic status. SLAM builds a priori MRI-based segmentation information into the standard Fourier-encoded MRS model of chemical shift imaging (CSI), to directly reconstruct compartmental spectra. Second, SLAM is extended to higher dimensions and to incorporate parallel imaging techniques that deploy pre-acquired sensitivity information based on the use of separate multiple receive-coil elements, to further accelerate scan speed. In addition, eddy current-induced phase effects are incorporated into the SLAM model, and a modified reconstruction algorithm provides improved suppression of signal leakage due to heterogeneity in the MRS signal, especially when employing sensitivity encoding. Third, prior information from MRI is also used to reduce the problem of lipid artifacts in 1H brain CSI. CSI is routinely used for human brain MRS studies, and low spatial resolution in CSI causes partial volume error and signal ‘bleed’ that is especially deleterious to voxels near the scalp. A standard solution is to apply spatial apodization, which adversely affects spatial resolution. Here, a novel automated strategy for partial volume correction that employs grid shifting (‘PANGS’) is presented, which minimizes lipid signal bleed without compromising spatial resolution. PANGS shifts the reconstruction coordinate in a designated region of image space—the scalp, identified by MRI—to match the tissue center of mass instead of the geometric center of each voxel. Last, prior knowledge of the spatially sparse nature of endoscopic MRI images acquired with tiny internal MRI antennae, and that of the null signal location of the endoscopic probe, are used to accelerate MR endoscopy and reduce motion artifacts. High-resolution endoscopic MRI is susceptible to degradation from physiological motion, which can necessitate time-consuming cardiac gating techniques. Here, we develop acceleration techniques based on the compressed sensing theory, and un-gated motion compensation strategies using projection shifting, to effectively produce faster motion-suppressed MRI endoscopy

Technological innovations in magnetic resonance for early detection of cardiovascular diseases

Most recent technical innovations in cardiovascular MR imaging (CMRI) are presented in this review. They include hardware and software developments, and novelties in parametric mapping. All these recent improvements lead to high spatial and temporal resolution and quantitative information on the heart structure and function. They make it achievable ambitious goals in the field of mapletic resonance, such as the early detection of cardiovascular pathologies. In this review article, we present recent innovations in CMRI, emphasizing the progresses performed and the solutions proposed to some yet opened technical problems