Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change

Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre-surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label-fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored.

Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

BACKGROUND

To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated.

AIM

To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD.

METHODS

From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations.

RESULTS

Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7.5% of the hippocampi for FreeSurfer and in 6.9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±7.2% for FreeSurfer, ±9.7% for expert manual delineation, and ±10.0% for FIRST. Methods ranked the same for reproducibility of 1-year μL volume change, with LoA of ±218 μL for FreeSurfer, ±319 μL for expert manual delineation, and ±333 μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change.

CONCLUSION

Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.

Improved volumetric measurement of brain structure with a distortion correction procedure using an ADNI phantom

PURPOSE

Serial magnetic resonance imaging (MRI) images acquired from multisite and multivendor MRI scanners are widely used in measuring longitudinal structural changes in the brain. Precise and accurate measurements are important in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease. However, geometric distortions in MRI images decrease the accuracy and precision of volumetric or morphometric measurements. To solve this problem, the authors suggest a commercially available phantom-based distortion correction method that accommodates the variation in geometric distortion within MRI images obtained with multivendor MRI scanners.

METHODS

The authors' method is based on image warping using a polynomial function. The method detects fiducial points within a phantom image using phantom analysis software developed by the Mayo Clinic and calculates warping functions for distortion correction. To quantify the effectiveness of the authors' method, the authors corrected phantom images obtained from multivendor MRI scanners and calculated the root-mean-square (RMS) of fiducial errors and the circularity ratio as evaluation values. The authors also compared the performance of the authors' method with that of a distortion correction method based on a spherical harmonics description of the generic gradient design parameters. Moreover, the authors evaluated whether this correction improves the test-retest reproducibility of voxel-based morphometry in human studies.

RESULTS

A Wilcoxon signed-rank test with uncorrected and corrected images was performed. The root-mean-square errors and circularity ratios for all slices significantly improved (p < 0.0001) after the authors' distortion correction. Additionally, the authors' method was significantly better than a distortion correction method based on a description of spherical harmonics in improving the distortion of root-mean-square errors (p < 0.001 and 0.0337, respectively). Moreover, the authors' method reduced the RMS error arising from gradient nonlinearity more than gradwarp methods. In human studies, the coefficient of variation of voxel-based morphometry analysis of the whole brain improved significantly from 3.46% to 2.70% after distortion correction of the whole gray matter using the authors' method (Wilcoxon signed-rank test, p < 0.05).

CONCLUSIONS

The authors proposed a phantom-based distortion correction method to improve reproducibility in longitudinal structural brain analysis using multivendor MRI. The authors evaluated the authors' method for phantom images in terms of two geometrical values and for human images in terms of test-retest reproducibility. The results showed that distortion was corrected significantly using the authors' method. In human studies, the reproducibility of voxel-based morphometry analysis for the whole gray matter significantly improved after distortion correction using the authors' method.

Jacobian integration method increases the statistical power to measure gray matter atrophy in multiple sclerosis

Gray matter atrophy provides important insights into neurodegeneration in multiple sclerosis (MS) and can be used as a marker of neuroprotection in clinical trials. Jacobian integration is a method for measuring volume change that uses integration of the local Jacobian determinants of the nonlinear deformation field registering two images, and is a promising tool for measuring gray matter atrophy. Our main objective was to compare the statistical power of the Jacobian integration method to commonly used methods in terms of the sample size required to detect a treatment effect on gray matter atrophy. We used multi-center longitudinal data from relapsing-remitting MS patients and evaluated combinations of cross-sectional and longitudinal pre-processing with SIENAX/FSL, SPM, and FreeSurfer, as well as the Jacobian integration method. The Jacobian integration method outperformed these other commonly used methods, reducing the required sample size by a factor of 4-5. The results demonstrate the advantage of using the Jacobian integration method to assess neuroprotection in MS clinical trials.

Structural brain plasticity in adult learning and development

Recent research using magnetic resonance imaging has documented changes in the adult human brain's grey matter structure induced by alterations in experiential demands. We review this research and relate it to models of brain plasticity from related strands of research, such as work on animal models. This allows us to generate recommendations and predictions for future research that may advance the understanding of the function, sequential progression, and microstructural nature of experience-dependent changes in regional brain volumes. Informed by recent evidence on adult age differences in structural brain plasticity, we show how understanding learning-related changes in human brain structure can expand our knowledge about adult development and aging. We hope that this review will promote research on the mechanisms regulating experience-dependent structural plasticity of the adult human brain.

Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS.

Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques

BACKGROUND AND PURPOSE

Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems.

MATERIALS AND METHODS

Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm.

RESULTS

Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider.

CONCLUSIONS

If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.

Sensory Neurons of the Human Brachial Plexus

BACKGROUND:

Extensive neuron death following peripheral nerve trauma is implicated in poor sensory recovery. Translational research for experimentally proven neuroprotective drugs requires knowledge of the numbers and distribution of sensory neurons in the human upper limb and a novel noninvasive clinical measure of neuron loss.

OBJECTIVE:

To compare optical fractionation and volumetric magnetic resonance imaging (MRI) of dorsal root ganglia (DRG) in histological quantification and objective clinical assessment of human brachial plexus sensory neurons.

METHODS:

Bilateral C5-T1 DRG were harvested from 5 human cadavers for stereological volume measurement and sensory neuron counts (optical fractionator). MRI scans were obtained from 14 healthy volunteers for volumetric analysis of C5-T1 DRG.

RESULTS:

The brachial plexus is innervated by 425 409 (standard deviation 15 596) sensory neurons with a significant difference in neuron counts and DRG volume between segmental levels (P &lt; .001), with C7 ganglion containing the most. DRG volume correlated with neuron counts (r = 0.75, P &lt; .001). Vertebral artery pulsation hindered C5 and 6 imaging, yet high-resolution MRI of C7, C8, and T1 DRG permitted unbiased volume measurement. In accord with histological analysis, MRI confirmed a significant difference between C7, C8, and T1 DRG volume (P &lt; .001), interindividual variability (CV = 15.3%), and sex differences (P = .04). Slight right-left sided disparity in neuron counts (2.5%, P = .04) was possibly related to hand dominance, but no significant volume disparity existed.

CONCLUSION:

Neuron counts for the human brachial plexus are presented. These correlate with histological DRG volumes and concur with volumetric MRI results in human volunteers. Volumetric MRI of C7-T1 DRG is a legitimate noninvasive proxy measure of sensory neurons for clinical study.

Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans

SienaX and Siena are widely used and fully automated algorithms for measuring whole brain volume and volume change in cross-sectional and longitudinal MRI studies and are particularly useful in studies of brain atrophy. The reproducibility of the algorithms was assessed using the 3D T1 weighted MP-RAGE scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The back-to-back (BTB) MP-RAGE scans in the ADNI data set makes it a valuable benchmark against which to assess the performance of algorithms of measuring atrophy in the human brain with MRI scans. A total of 671 subjects were included for SienaX and 385 subjects for Siena. The annual percentage brain volume change (PBVC) rates were -0.65±0.82%/year for the healthy controls, -1.15±1.21%/year for mild cognitively impairment (MCI) and -1.84±1.33%/year for AD, in line with previous findings. The median of the absolute value of the reproducibility of SienaX's normalized brain volume (NBV) was 0.96% while the 90th percentile was 5.11%. The reproducibility of Siena's PBVC had a median of 0.35% and a 90th percentile of 1.37%. While the median reproducibility for SienaX's NBV was in line with the values previously reported in the literature, the median reproducibility of Siena's PBVC was about twice that reported. Also, the 90th percentiles for both SienaX and Siena were about twice the size that would be expected for a Gaussian distribution. Because of the natural variation of the disease among patients over a year, a perfectly reproducible whole brain atrophy algorithm would reduce the estimated group size needed to detect a specified treatment effect by only 30% to 40% as compared to Siena's.