Fast Absolute Quantification of In Vivo Water and Fat Content with Magnetic Resonance Imaging

Quantitative water fat imaging offers a non-invasive method for monitoring and staging diseases associated with changes in either water or fat content in tissue. In this work absolute water and fat mass density measurement with in vivo Magnetic Resonance Imaging (MRI) is demonstrated. T1 independent, T2* corrected chemical shift based water-fat separated images are acquired. By placing a phantom with known mass density in the field of view for signal intensity calibration, absolute water or fat mass density can be computed, assuming the B1+ (transmit) and B1- (receive) fields can be measured. Phantom experiments with known water fat concentration were conducted to validate the feasibility of proposed method and in vivo data was collected from healthy volunteers. Results show good agreement with known values of in vivo water density. Each measurement was within one breath hold. Fast absolute quantification of water and fat with MRI is feasible in the abdomen

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

Accelerated Imaging Techniques for Chemical Shift Magnetic Resonance Imaging

Chemical shift imaging is a method for the separation two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are acquired at different echo times. Image acceleration techniques, typically parallel imaging, are often used to improve the spatial coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstructions and proposes new image reconstructions for accelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water-fat separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations such as they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A SNR estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method is shown to offer comparable image quality relative to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this technique is shown to offer improved image quality when compared to the current standard of parallel imaging. Accelerated chemical shift imaging was demonstrated for metabolic of hyperpolarized [1-13C] pyruvate. Pyruvate, lactate, alanine, and bicarbonate images were reconstructed from undersampled datasets. Phantoms were used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets were used to demonstrate. Alternatively, acceleration was also achieved through the use of a high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can be play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate

Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

Coil combination using linear deconvolution in k-space for phase imaging

Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper. Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method. Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase. Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future

Field Inhomogeneity Compensation in High Field Magnetic Resonance Imaging (MRI)

This thesis concentrates on the reduction of field (both main field B0 and RF field B1) inhomogeneity in MRI, especially at high B0 field. B0 and B1 field inhomogeneity are major hindrances in high B0 field MRI applications. B1 inhomogeneity will lead to spatially varying signal intensity in the MR images. B0 inhomogeneity produces blurring, distortion and signal loss at tissue interfaces. B0 artifacts are usually termed off-resonance or susceptibility artifacts. None of the existing methods can perfectly correct these inhomogeneity artifacts.This thesis aims at developing three-dimensional (3D) tailored RF (TRF) pulses to mitigate these artifacts. A current limitation in the use of 3D TRF techniques, however, is that pulses are often too long for practical clinical applications. Multiple transmission techniques are proposed to decrease pulse lengths and provide an inherent correction for B1 inhomogeneity. Shorter pulses are also more robust to profile distortions from susceptibility effects.Specifically, slice-selective 3D TRF pulses for multiple (or ¡°parallel¡±) transmitters were designed and validated in uniform phantom and human brain experiments at 3 Tesla. A pseudo-transmit sensitivity encoding (¡°transmit SENSE¡±) method was introduced using a body coil transmitter and multiple receivers to mimic the real parallel transmitter experiment. The kz-direction was controlled by fast switching of gradients in a fashion similar to Echo planar imaging (EPI). The transverse plane (kx-ky) was sampled sparsely with hexagonal trajectories, and accelerated with the transmit SENSE method. The transmit SENSE 3D TRF pulses reduced the B1 inhomogeneity compared to standard SINC pulses in human brain scans. The undersampled transmit SENSE pulses were only 4.3ms long and could excite a 5mm thick slice, which is very promising for clinical applications. Furthermore, these pulses are shown by numerical simulation to have promise in correcting through-plane susceptibility artifacts

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

© 2019 International Society for Magnetic Resonance in Medicine Proton MRS (1H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use

Spatiotemporal brain imaging and modeling

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2004.Includes bibliographical references.This thesis integrates hardware development, data analysis, and mathematical modeling to facilitate our understanding of brain cognition. Exploration of these brain mechanisms requires both structural and functional knowledge to (i) reconstruct the spatial distribution of the activity, (ii) to estimate when these areas are activated and what is the temporal sequence of activations, and (iii)to determine how the information flows in the large-scale neural network during the execution of cognitive and/or behavioral tasks. Advanced noninvasive medical imaging modalities are able to locate brain activities at high spatial and temporal resolutions. Quantitative modeling of these data is needed to understand how large-scale distributed neuronal interactions underlying perceptual, cognitive, and behavioral functions emerge and change over time. This thesis explores hardware enhancement and novel analytical approaches to improve the spatiotemporal resolution of single (MRI) or combined (MRI/fMRI and MEG/EEG) imaging modalities. In addition, mathematical approaches for identifying large-scale neural networks and their correlation to behavioral measurements are investigated. Part I of the thesis investigates parallel MRI. New hardware and image reconstruction techniques are introduced to improve spatiotemporal resolution and to reduce image distortion in structural and functional MRI. Part II discusses the localization of MEG/EEG signals on the cortical surface using anatomical information from AMTRI, and takes advantage of the high temporal resolution of MEG/EEG measurements to study cortical oscillations in the human auditory system. Part III introduces a multivariate modeling technique to identify "nodes" and "connectivity" in a(cont.) large-scale neural network and its correlation to behavior measurements in the human motor system.by Fa-Hsuan Lin.Ph.D