Table 3.

Approaches for the improvement of MRI methodology

Challenge	Approach and results	Reference
Image acquisition
Evaluate measurement properties of the Brain Atrophy and Lesion Index (BALI) [118] at 1.5-T and 3-TMRI.	Compared T1- and T2-weighted imaging at different field strengths for their ability to correctly diagnose patient groups. Found that 1.5-T BALI scores were similar to those obtained using 3-T images.	[81]
Compare 1.5-T and 3-T MRI for automated hippocampal segmentation	Compared the ability of baseline MRI data of patients scanned at both 1.5 T and 3 T to make a clinical diagnosis based on hippocampal radial distance. Found that both field strengths yielded comparable hippocampal atrophy patterns but that 3 T had a superior signal-to-noise ratio and ability to detect atrophy.	[82]
Effect of 1.5-T versus 3-T field strengths and image registration strategy on VBM	Tested different diffeomorphic spatial registration strategies over two field strengths for their ability to detect AD-related atrophy. Registration strategy affected the estimation of AD-related atrophy, whereas field strengths affected assessment of brain anatomy in the cerebellum, the precentral gyrus, and the thalamus bilaterally.	[83]
Change in 3.0-T MRI image acquisition scheme between ADNI-1 and subsequent grants	Used voxel-based morphometry to compare 3.0-T Tl-weighted volumes obtained in ADNI-1 and ADNI-2. The protocol used in ADNI-2 resulted in increased gray matter and localized decreases in white matter compared to ADNI-1 images and the total volumes of gray matter, white matter, and cerebrospinal fluid also differed. These results should be considered when comparing images obtained during these two protocols using VBM.	[84]
Analyzing the effect of geometric distortions on different scanner/protocol combinations.	Used the ADNI phantom to measure MRI image distortion. Found that the size of distortion field varied between scanners and protocols but that corrections applied reduced distortion to 1 mm or less.	[85]
Preprocessing
Different approaches for the normalization of regional volumes by intracranial volume (ICV) may influence the relationship between hippocampus and cognition.	Tested the effect of the three methods: raw volumes, volume to ICV fractions, or regional volumes, on the relationship between hippocampal volume and cognition. Found that the three approaches did not alter this association but had small effects on the prediction of diagnostic status.	[86]
Robust and accurate automatic brain extraction across diverse subject groups.	Proposed a method combining an atlas-based approach and a deformable surface-based approach guided by prior information on local intensities and specific populations. Found that the method was accurate across all disease states and across human lifespan and performed favorably compared to existing protocols.	[87]
Registration and segmentation
Selection of the most discriminative features for deformable image registration.	Proposed an image registration framework that uses deep learning to discover morphological patterns in image patches. Achieved more accurate registration results compared to state-of-the-art methods.	[88]
Development of a brain parcellation tool based on multi-atlas algorithms that is robust for many different imaging protocols	Used a multiple atlas, likelihood fusion algorithm to test parcellation of the entire brain using six protocols across different manufacturers and field strengths. Found that there was little effect of different protocols on the variability of brain volumes.	[89]
Optimal selection of the regularization parameter	Presents a nonregression approach for the selection of the regularization parameter based on the Variational-Bayesian cycle. Found this is more computationally efficient than other methods of noise reduction.	[90]
	Presents a novel method based on full Bayesian inference on a probabilistic registration model, for inferring spatially varying regularization in nonlinear registration. The proposed model is data driven and its spatially adaptive prior provides better localization of regional volume change.	[91]
Test-retest reliability of automated segmentation methods	Used FreeSurfer to process intrasession and day-to-day scans of subjects. Found that intersession variability exceeded intrasession variability for some regions	[92]
Faster image registration	Used three approaches to accelerate the image registration package elastix: (1) parallelization on the CPU; (2) parallelization on the GPU; and (3) improvements of the B-spline transformation model. Reported an acceleration factor of 4 to 5 fold and that the accelerated version had similar classification accuracies to the original version.	[93]
Accurate partial volume estimation in tissue labeling	Proposed a fast algorithm based on a Bayesian maximum a posteriori formulation. Algorithm enhanced diagnostic accuracy in ADNI standardized data set.	[94]
Automated segmentation of other regions	Proposed a novel automated method for the segmentation of the human brainstem into midbrain and pons called Landmark-based Automatic Brainstem Segmentation (LABS) which uses a revised landmark-based approach integrated with a thresholding method. LABS correlated highly with manual segmentation.	[95]
	Present a novel segmentation algorithm for measuring change in MTL volume. Baseline MTL volume is defined as an atlas image and mapped onto the corresponding follow-up image to measure volume change. The automated approach measured significant differences between clinical groups, unlike existing FreeSurfer software.	[96]
Longitudinal scans
Develop scoring and training methods for BALI for the accurate quantitative and of whole-brain structural changes	Trained raters using a step-by-step BALI process. New raters achieved >90% accuracy following 2 weeks of training and achieved both high interrater and intrarater correlation coefficients. Suggests that BALI is a robust method for assessing the whole-brain health in MCI and AD patients.	[97]
Use of accelerated versus unaccelerated scans in serial MRI to detect longitudinal change	Used symmetric diffeomorphic image normalization (SyN) to normalize serial scans obtained using TBM. Found that groupwise discrimination and sample size estimates were comparable using accelerated and unaccelerated scans but that the two protocols resulted in differences in TBM-Syn.	[98]
	Compared the impact of nonaccelerated versus accelerated scans on brain atrophy using the means normalized boundary shift interval (KN-BSI) and deformation-based morphometry. Found differences in measured atrophy rates using scanners from different vendors but little difference between nonaccelerated and accelerated baseline scans and follow-up scans.	[99]
	Used morphometry to compare numerical summaries of accelerated versus nonaccelerated scans across patient groups over 6- and 12-month scanned intervals. Scan acceleration had minimal effects on the TBM-derived atrophy measures.	[100]
Prediction of a brain image at a particular time point given minimal longitudinal data	Presented an algorithm for the simultaneous registration of N longitudinal image pairs. Information from each pier is used to constrain the registration of each other pair. The use of a groupwise consistency prior can predict an image act and third time point not included in the registration step.	[101]
	Presented an algorithm to incorporate information from the entire patient group to predict longitudinal change, as they share similar spatial distributions of volume change. Use longitudinal registration was a groupwise coupling prior and found it able to estimate change robustly.	[102]
	Proposed a method for supplementing the lack of longitudinal information for an individual patient with cross-sectional data from the population. Used a probabilistic model based on James Stein estimators to improve geodesic estimation. Method allowed prediction of brain changes of images over time.	[103]
Accounting for spatially inhomogeneous longitudinal data	Proposed a method based on the Sandwich Estimator to account for within-subject correlation in longitudinal data. Found that the method was flexible and fit within-and between-subject effects on the single model in an unbalanced longitudinal data set.	[104]
Measuring longitudinal gray-matter volume change in the default mode network	Proposed use of a volume standardized with global gray-matter volume. Method detected significant differences in longitudinal gray matter in the default mode network across patient groups.	[105]
Daily changes in brain volume resulting from physiological fluctuations may impact ability of imaging to detect longitudinal changes in brain volume.	Used statistical modeling of MRI images, measuring the brain parenchymal fraction to account for variations in head size. Found a statistically significant time of day effect on brain parenchymal fraction. Suggests that an acquisition time bias should be accounted for in brain volumetric studies.	[106]
Improvement of the boundary shift interval for measuring longitudinal change in brain volume	Proposed an extension to the boundary shift interval which uses probabilistic segmentations and then estimates a nonbinary exclusive or a region of interest to better capture patterns of brain atrophy.	[107]
Cortical thickness estimation
Cortical thickness estimation	Presented an algorithm driven by the graph spectrum and heat kernel theory to estimate cortical thickness. Successfully detected statistical differences between patient classes.	[108]
	Tested the ability of voxel-based morphometry (VBM) to measure cortical thickness. Found that the VBM was less sensitive to cortical atrophy as it was biased to medial temporal lobe atrophy and that FreeSurfer was more sensitive to cortical thinning.	[109]
Morphometry
Cortical pattern analysis	Proposed a multi-resolution approach which prescribes shape descriptors that characterize the signal at each mesh vertex. Method showed increased sensitivity and statistical power to detect group-level differences.	[110]
Ventricular morphology analysis	Presented a novel system for ventricular morphometry based on the hyperbolic Ricci flow method and tensor morphometry statistics. The TBM statistics enhanced surface shape analysis and the method revealed shape differences close to the temporal lobe and posterior cingulate. Correlations were detected between ventricular morphometry, neuropsychological measures, and metabolism.	[111]
Representation of overall brain morphology	Proposed a novel approach to deformation-based morphometry, regional flux analysis, based on the scheme halts decomposition of deformations parameterized by stationary velocity fields. The framework unifies voxel-based and regional approaches and had good power to discover shapes deformations both cross-sectionally and longitudinally.	[112]
	Introduced BrainPrint, a fully automated framework which generates a compact representation of brain morphology by capturing shape information from both cortical and subcortical structures. Method was efficient and discriminative.	[113]
	Presented a mass univariate framework that uses longitudinal VBM data and Bayesian inference to analyze whole-brain structural changes over time. The probabilistic model detects individual and group trajectories of disease progression.	[114]
Measuring patterns of brain morphological changes in populations.	Proposed a data-driven probabilistic unsupervised framework that automatically segments heterogeneous set of images using an atlas-based method and clusters images into homogeneous subgroups. It constructs separate probabilistic atlases for each cluster. Found that combining segmentation and atlas construction led to improved segmentation accuracy and clusters generated coincided with clinical subgroups.	[115]
Identification of shape deformation patterns	Developed a data-driven global analysis of brain anatomy using kernel partial least squares and a regression model to quantify shape changes that explain variations in clinical neuropsychological measures. Method identified similar patterns in AD to predefined ROIs as well as other new patterns of deformation.	[116]
	Presented a framework for intrinsic comparison of surface metric structures and curvatures based on a Riemannian framework. Framework was able to efficiently detect boundaries between functionally and structurally distinct regions.	[117]

Abbreviations: MRI, magnetic resonance imaging; AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; MTL, medial temporal lobe; ROI, regions of interest.