Statistical characterization of residual noise in the low-rank approximation filter framework, general theory and application to hyperpolarized tracer spectroscopy

The use of low-rank approximation filters in the field of NMR is increasing due to their flexibility and effectiveness. Despite their ability to reduce the Mean Square Error between the processed signal and the true signal is well known, the statistical distribution of the residual noise is still undescribed. In this article, we show that low-rank approximation filters are equivalent to linear filters, and we calculate the mean and the covariance matrix of the processed data. We also show how to use this knowledge to build a maximum likelihood estimator, and we test the estimator's performance with a Montecarlo simulation of a 13C pyruvate metabolic tracer. While the article focuses on NMR spectroscopy experiment with hyperpolarized tracer, we also show that the results can be applied to tensorial data (e.g. using HOSVD) or 1D data (e.g. Cadzow filter).Comment: 26 pages, 7 figure

Corrections in clinical Magnetic Resonance Spectroscopy and SPECT:Motion correction in MR spectroscopy Downscatter correction in SPECT

The quality of medical scanner data is often compromised by several mechanisms. This can be caused by both the subject to be measured and the scanning principles themselves. In this PhD project the problem of subject motion was addressed for Single Voxel MR Spectroscopy in a cohort study of preterm infants. In Iodine-123 SPECT the problem of downscatter was addressed. This thesis is based on two papers. Paper I deals with the problem of motion in Single Voxel Spectroscopy. Two novel methods for the identification of outliers in the set of repeated measurements were implemented and compared to the known mean and median filtering. The data comes from non-anesthetized preterm infants, where motion during scanning is a common problem. Both the novel outlier identification and the independent component analysis (ICA) perform satisfactory and better than the common mean and median filtering. ICA performed best in the sense that it recovered most of the lost peak height in the spectra. The ICA motion correction algorithm described in paper I and in this thesis was applied to a quantitative analysis of the Single Voxel Spectroscopy data from the cohort study of preterm infants. This analysis revealed that differences between term and preterm infants are not to be found in the concentrations of Lactate (caused by inflammation or hypoxia-ischemia) and/or NAA (caused by hypoxia-ischemia) as hypothesized before the cohort study. Instead choline levels were decreased in the preterm infants, which might indicate a detrimental effect of the extra-uterine environment on brain development. Paper II describes a method to correct for downscatter in low count Iodine-123 SPECT with a broad energy window above the normal imaging window. Both spatial dependency and weight factors were measured. As expected, the implicitly assumed weight factor of one for energy windows with equal width is slightly too low, due the presence of a backscatter peak in the energy spectrum coming from high-energy photons. The effect on the contrast was tested in 10 subjects and revealed a 20% increase in the specific binding ratio of the striatum due to downscatter correction. This makes the difference between healthy subjects and patients more profound. Downscatter in Iodine-123 SPECT is not the only deteriorating mechanism. Normal scatter compromises the images quality as well. Since scatter correction of SPECT-images also can be performed by the subtraction of an energy window, a method was developed to perform scatter and downscatter correction simultaneously. A phantom study has been performed, where the in paper II described downscatter correction was extended with scatter correction. This new combined correction was compared to the known Triple Energy Window (TEW) correction method. Results were satisfying and indicate that TEW is more correct from the physics point of view, while the in paper II described method extended with scatter correction gives reasonable results, but is far less noise sensitive than TEW

Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling

Producción CientíficaPurpose: To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms for diffusion MRI. Methods: ODGD motion-compensated waveforms were designed for various moment-nullings Mn (n=0,1,2), for a range of b-values, and spatial resolutions, both without (ODGD-Mn) and with CG-nulling (ODGD-Mn-CG). Phantom and in-vivo (brain and liver) experiments were conducted with various ODGD waveforms to compare motion robustness, signal-to-noise ratio (SNR), and apparent diffusion coefficient (ADC) maps with state-of-the-art waveforms. Results:ODGD-Mn and ODGD-Mn-CG waveforms reduced the TE of state-of-the-art waveforms. This TE reduction resulted in significantly higher SNR (P < 0.05) in both phantom and in-vivo experiments. ODGD-M1 improved the SNR of BIPOLAR (42.8+-5.3 versus 32.9+-3.3) in the brain, and ODGD-M2 the SNR of motion-compensated (MOCO) and Convex Optimized Diffusion Encoding-M2 (CODE-M2) (12.3+-3.6 versus 9.7+-2.9 and 10.2+-3.4, respectively) in the liver. Further, ODGD-M2 also showed excellent motion robustness in the liver. ODGD-M2-CG waveforms reduced the CG-related dephasing effects of non CG-nulling waveforms in phantom and in-vivo experiments, resulting in accurate ADC maps. Conclusions: ODGD waveforms enable motion-robust diffusion MRI with reduced TEs, increased SNR, and reduced ADC bias compared to state-of-the-art waveforms in theoretical results, simulations, phantoms and in-vivo experiments.TEC2013-44194-PVA069U1

Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy.

A prospective study was undertaken in women undergoing neoadjuvant chemotherapy for locally advanced breast cancer in order to determine the ability of quantitative magnetic resonance imaging (MRI) and proton spectroscopy (MRS) to predict ultimate tumour response (percentage decrease in volume) or to detect early response. Magnetic resonance imaging and MRS were carried out before treatment and after the second of six treatment cycles. Pharmacokinetic parameters were derived from T1-weighted dynamic contrast-enhanced MRI, water apparent diffusion coefficient (ADC) was measured, and tissue water:fat peak area ratios and water T2 were measured using unsuppressed one-dimensional proton spectroscopic imaging (30 and 135 ms echo times). Pharmacokinetic parameters and ADC did not detect early response; however, early changes in water:fat ratios and water T2 (after cycle two) demonstrated substantial prognostic efficacy. Larger decreases in water T2 accurately predicted final volume response in 69% of cases (11/16) while maintaining 100% specificity and positive predictive value. Small/absent decreases in water:fat ratios accurately predicted final volume non-response in 50% of cases (3/6) while maintaining 100% sensitivity and negative predictive value. This level of accuracy might permit clinical application where early, accurate prediction of non-response would permit an early change to second-line treatment, thus sparing patients unnecessary toxicity, psychological morbidity and delay of initiation of effective treatment

Noise Estimation, Noise Reduction and Intensity Inhomogeneity Correction in MRI Images of the Brain

Rician noise and intensity inhomogeneity are two common types of image degradation that manifest in the acquisition of magnetic resonance imaging (MRI) system images of the brain. Many noise reduction and intensity inhomogeneity correction algorithms are based on strong parametric assumptions. These parametric assumptions are generic and do not account for salient features that are unique to specific classes and different levels of degradation in natural images. This thesis proposes the 4-neighborhood clique system in a layer-structured Markov random field (MRF) model for noise estimation and noise reduction. When the test image is the only physical system under consideration, it is regarded as a single layer Markov random field (SLMRF) model, and as a double layer MRF model when the test images and classical priors are considered. A scientific principle states that segmentation trivializes the task of bias field correction. Another principle states that the bias field distorts the intensity but not the spatial attribute of an image. This thesis exploits these two widely acknowledged scientific principles in order to propose a new model for correction of intensity inhomogeneity. The noise estimation algorithm is invariant to the presence or absence of background features in an image and more accurate in the estimation of noise levels because it is potentially immune to the modeling errors inherent in some current state-of-the-art algorithms. The noise reduction algorithm derived from the SLMRF model does not incorporate a regularization parameter. Furthermore, it preserves edges, and its output is devoid of the blurring and ringing artifacts associated with Gaussian and wavelet based algorithms. The procedure for correction of intensity inhomogeneity does not require the computationally intensive task of estimation of the bias field map. Furthermore, there is no requirement for a digital brain atlas which will incorporate additional image processing tasks such as image registration

Laboratory evaluation of laser-induced breakdown spectroscopy (LIBS) as a new in situ chemical sensing technique for the deep ocean

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2007Present-day expeditionary oceanography is beginning to shift from a focus on short- term ship and submersible deployments to an ocean observatory mode where long- term temporally-focused studies are feasible. As a result, a critical need for in situ chemical sensors is evolving. New sensors take a significant amount of time to develop; thus, the evaluation of techniques in the laboratory for use in the ocean environment is becoming increasingly important. Laser-induced breakdown spectroscopy (LIBS) possesses many of the characteristics required for such in situ chemical sensing, and is a promising technique for field measurements in extreme environments. Although many LIBS researchers have focused their work on liquid jets or surfaces, little at- tention has been paid to bulk liquid analysis, and especially to the effect of oceanic pressures on LIBS signals. In this work, laboratory experiments validate the LIBS technique in a simulated deep ocean environment to pressures up to 2.76 × 107 Pa. A key focus of this work is the validation that select elements important for understand- ing hydrothermal vent fluid chemistry (Na, Ca, Mn, Mg, K, and Li) are detectable using LIBS. A data processing scheme that accurately deals with the extreme nature of laser-induced plasma formation was developed that allows for statistically accu- rate comparisons of spectra. The use of both single and double pulse LIBS for high pressure bulk aqueous solutions is explored and the system parameters needed for the detection of the key analytes are optimized. Using both single and double pulse LIBS, the limits of detection were found to be higher than expected as a result of the spectrometer used in this experimentation. However, the results of this validation show that LIBS possesses the characteristics to be a viable chemical sensing method for in situ analyte detection in high pressure environments like the deep ocean.National Science Foundation for support of this research under grants OCE0352278 and OCE0352242. Additional support was received from WHOI’s Deep Ocean Exploration Institute who awarded this research with two grants. The WHOI Ocean Ventures Fund and the Department of Defens

A study on the application of independent component analysis to in vivo ¹H magnetic resonance spectra of childhood brain tumours for data processing

Independent component analysis (ICA) has the potential of automatically determining metabolite, macromolecular and lipid (MMLip) components that make up magnetic resonance (MR) spectra. However, the realiability with which this is accomplished and the optimal ICA approach for investigating in vivo MR spectra, have not yet been determined. A wavelet shrinkage de-noising based enhancement algorithm, utilising a newly derived relationship between the real and imaginary parts of the MR spectrum, is proposed. This algorithm is more robust compared with conventional de-noising methods. The two approaches for applying ICA, blind source separation (BSS) and feature extraction (FE), are thoroughly examined. A feature dimension selection method, which has not been adequately addressed, is proposed to set a theoretical guideline for ICA dimension reduction. Since the advantages and limitations of BSS-ICA and FE-ICA are different, combining them may compensate their disadvantages and lead to better results. A novel ICA approach involving a hybrid of the two techniques for automated decomposition of MRS dataset is proposed. It has been demonstrated that hybrid ICA provides more realistic individual metabolite and MMLip components than BSS-ICA or FE-ICA. It can aid metabolite identification and assignment, and has the potential for extracting biologically useful features and discovering biomarkers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning

L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN).The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)