Assessment of Image Quality Requirements in Magnetic Resonance Imaging for Quantitative Brain Morphometry

Structural T1-weighted magnetic resonance imaging (MRI) provides sufficient anatomical details to measure and track changes in volumes of brain structures. The volumes of brain structures and changes in them can be used to study the effects of disease, treatment monitoring, aging, learning and brain development. The present thesis investigates the requirements for performing reproducible quantitative brain volume measurements with automated brain tissue segmentation tools and gives an error bound on the measurements under various experimental conditions. A short introduction into the challenges of performing reproducible brain volume measurements and the main issues that impede the adoption of quantitative volumetric measurements in clinical practice is given, followed by an overview of the acquisition, reconstruction and automated image segmentation methods used to perform quantitative brain volume measurements. The first part of this study was carried out to investigate the reproducibility of volumetric measurements preformed on different systems with a standardized ADNI protocol. Systematic biases in volume measurements were observed when there were changes in systems between the first scan and rescan. An important finding in the context of patient management was that neither repositioning nor a two-week gap between the measurements did significantly contribute to the uncertainty in volumetric measurements when compared to the uncertainty in a back-to-back scan-rescan scenario. In the second part of this study, the impact of new highly-accelerated acquisition protocols on automated brain tissue volume measurements was investigated. A single system was used to collect the data and acquisition time was varied at the expense of the SNR. An important outcome of this study was that for qualitative assessment accelerated protocols provided similar information. However, the automated volume measurements with the highly-accelerated protocols were found biased compared to the measurements with standardized ADNI protocol. In the final part of this study, scaling procedures were investigated as means for compensating for the observed differences in sequential automated brain volume measurements. A new image-property-based compensation strategy was proposed and compared to the current state-of-the-art protocol-based approaches. The main outcomes of this study were that there are limitations to the current state-of-the-art protocol-based approaches, namely that volume correction coefficients used in the protocol-based approaches can vary as a function of age, and there is an indication that the proposed image-property-based approach can be more robust to the age and contrast-dependent effects compared to protocol-based approaches

Prospective head motion correction using FID-guided on-demand image navigators

PURPOSE: We suggest a motion correction concept that employs free-induction-decay (FID) navigator signals to continuously monitor motion and to guide the acquisition of image navigators for prospective motion correction following motion detection. METHODS: Motion causes out-of-range signal changes in FID time series that, and in this approach, initiate the acquisition of an image navigator. Co-registration of the image navigator to a reference provides rigid-body-motion parameters to facilitate prospective motion correction. Both FID and image navigator are integrated into a prototype magnetization-prepared rapid gradient-echo (MPRAGE) sequence. The performance of the method is investigated using image quality metrics and the consistency of brain volume measurements. RESULTS: Ten healthy subjects were scanned (a) while performing head movements (nodding, shaking, and moving in z-direction) and (b) to assess the co-registration performance. Mean absolute errors of 0.27 +/- 0.38 mm and 0.19 +/- 0.24 degrees for translation and rotation parameters were measured. Image quality was qualitatively improved after correction. Significant improvements were observed in automated image quality measures and for most quantitative brain volume computations after correction. CONCLUSION: The presented method provides high sensitivity to detect head motion while minimizing the time invested in acquiring navigator images. Limits of this implementation arise from temporal resolution to detect motion, false-positive alarms, and registration accuracy

Prospective motion correction with FID-triggered image navigators

In this work, we propose a method for prospective motion correction in MRI using a novel image navigator module, which is triggered by a free induction decay (FID) navigator. Only when motion occurs, the image navigator is run and new positional information is obtained through image registration. The image navigator was specifically designed to match the impact on the magnetization and the acoustic noise of the host sequence. This detection-correction scheme was implemented for an MP-RAGE sequence and 5 healthy volunteers were scanned at 3T while performing various head movements. The correction performance was demonstrated through automated brain segmentation and an image quality index whose results are sensitive to motion artifacts