Intensity correction with a pair of spoiled gradient recalled echo images

Test-retest reliability of myelin imaging in the human spinal cord: Measurement errors versus region- and aging-induced variations

PURPOSE

To implement a statistical framework for assessing the precision of several quantitative MRI metrics sensitive to myelin in the human spinal cord: T1, Magnetization Transfer Ratio (MTR), saturation imposed by an off-resonance pulse (MTsat) and Macromolecular Tissue Volume (MTV).

METHODS

Thirty-three healthy subjects within two age groups (young, elderly) were scanned at 3T. Among them, 16 underwent the protocol twice to assess repeatability. Statistical reliability indexes such as the Minimal Detectable Change (MDC) were compared across metrics quantified within different cervical levels and white matter (WM) sub-regions. The differences between pathways and age groups were quantified and interpreted in context of the test-retest repeatability of the measurements.

RESULTS

The MDC was respectively 105.7ms, 2.77%, 0.37% and 4.08% for T1, MTR, MTsat and MTV when quantified over all WM, while the standard-deviation across subjects was 70.5ms, 1.34%, 0.20% and 2.44%. Even though particular WM regions did exhibit significant differences, these differences were on the same order as test-retest errors. No significant difference was found between age groups for all metrics.

CONCLUSION

While T1-based metrics (T1 and MTV) exhibited better reliability than MT-based measurements (MTR and MTsat), the observed differences between subjects or WM regions were comparable to (and often smaller than) the MDC. This makes it difficult to determine if observed changes are due to variations in myelin content, or simply due to measurement error. Measurement error remains a challenge in spinal cord myelin imaging, but this study provides statistical guidelines to standardize the field and make it possible to conduct large-scale multi-center studies.

Surface Coil Intensity Correction in Magnetic Resonance Imaging in Spinal Metastases

Objective

To evaluate the clinical application of phased-array surface coil intensity correction in magnetic resonance imaging (MRI) in spinal metastases.

Methods

3 phantoms and 50 patients with a corresponding total number of 80 spinal metastases were included in this study. Fast spin echo T1- and T2- weighted MRI with and without surface coil intensity correction was routinely performed for all phantoms and patients. Phantoms were evaluated by means of variance to mean ratio of signal intensity on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction. Spinal metastases were evaluated by image quality scores; reading time per case on both T1- and T2- weighted MRI obtained with and without surface coil intensity correction.

Results

Spinal metastases were diagnosed more successfully on MRI with surface coil intensity correction than on MRI with conventional surface coil technique. The variance to mean ratio of signal intensity was 53.36% for original T1-weighted MRI and 53.58% for original T2-weighted MRI. The variance to mean ratio of signal intensity was reduced to 18.99% for T1-weighted MRI with surface coil intensity correction and 22.77% for T2-weighted MRI with surface coil intensity correction. The overall image quality scores (interface conspicuity of lesion and details of lesion) were significantly higher than those of the original MRI. The reading time per case was shorter for MRI with surface coil intensity correction than for MRI without surface coil intensity correction.

Conclusions

Phased-array surface coil intensity correction in MRIs of spinal metastases provides improvements in image quality that leads to more successfully detection and assessment of spinal metastases than original MRI.

Evaluating quantitative proton-density-mapping methods

Quantitative magnetic resonance imaging (qMRI) aims to quantify tissue parameters by eliminating instrumental bias. We describe qMRI theory, simulations, and software designed to estimate proton density (PD), the apparent local concentration of water protons in the living human brain. First, we show that, in the absence of noise, multichannel coil data contain enough information to separate PD and coil sensitivity, a limiting instrumental bias. Second, we show that, in the presence of noise, regularization by a constraint on the relationship between T1 and PD produces accurate coil sensitivity and PD maps. The ability to measure PD quantitatively has applications in the analysis of in-vivo human brain tissue and enables multisite comparisons between individuals and across instruments. Hum Brain Mapp 37:3623-3635, 2016. © 2016 Wiley Periodicals, Inc.

Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging

We describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent, quantitative estimate of macromolecular volume can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.

Fast T1 mapping determined using incomplete inversion recovery look-locker 3D balanced SSFP acquisition and a simple two-parameter model fit

PURPOSE

To evaluate a fast T1 mapping technique using incomplete inversion recovery 3D balanced steady-state free precession acquisition along with a two-parameter model fit.

MATERIALS AND METHODS

Using Bloch simulations, we explored the two-parameter model fit for data acquired using such an acquisition scheme. The parameter space over which the fit holds good was determined through simulations. A linear correction was derived for the R1* (1/T1*) values so determined. Two phantoms and six volunteers were scanned using the described technique. Comparison scans using full recovery as well as gold standard inversion recovery spin echo were also performed.

RESULTS

The two-parameter fit works exceedingly well over a large parameter space. T1 values in the phantoms showed an error of 4.9% and 39% before correction and 0.9% and 1.6% after correction. For the six volunteers, error in T1 value was 5.3% for white matter (WM) and 2.4% for gray matter (GM) after correction, while it was 11.2% and 18.2% before correction.

CONCLUSION

The work presented here allows for T1 map determination with higher resolution and shorter acquisition time than previously possible. The technique is especially well suited for GM/WM T1 mapping.

An iterative two-threshold analysis for single-subject functional MRI of the human brain

OBJECTIVES

Current thresholding strategies for the analysis of functional MRI (fMRI) datasets may suffer from specific limitations (e.g. with respect to the required smoothness) or lead to reduced performance for a low signal-to-noise ratio (SNR). Although a previously proposed two-threshold (TT) method offers a promising solution to these problems, the use of preset settings limits its performance. This work presents an optimised TT approach that estimates the required parameters in an iterative manner.

METHODS

The iterative TT (iTT) method is compared with the original TT method, as well as other established voxel-based and cluster-based thresholding approaches and spatial mixture modelling (SMM) for both simulated data and fMRI of a hometown walking task at different experimental settings (spatial resolution, filtering and SNR).

RESULTS

In general, the iTT method presents with remarkable sensitivity and good specificity that outperforms all conventional approaches tested except for SMM in a few cases. This also holds true for challenging conditions such as high spatial resolution, the absence of filtering, high noise level, or a low number of task repetitions.

CONCLUSION

Thus, iTT emerges as a good candidate for both scientific fMRI studies at high spatial resolution and more routine applications for clinical purposes.