Overview of MR Imaging Volumetric Quantification in Neurocognitive Disorders

Abstract

This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias. Finally, an overview of general principles for protocol acquisition of magnetic resonance imaging for volumetric quantification will be discussed along with the current choices of FDA cleared algorithms for use in clinical practice.

OVERVIEW OF METHODS OF MAGNETIC RESONANCE VOLUMETRIC QUANTIFICATION

Historical Perspective

A variety of MR imaging (MRI) methods exist for evaluating neurocognitive disorders including neurodegenerative diseases such as Alzheimer disease (AD). These include perfusion MRI with arterial spin labeling,¹ diffusion tensor imaging,² and functional MRI.³ However, despite these innovations, structural imaging remains the only clinically recommended MR modality in the evaluation of dementia as recommended by the American Academy of Neurology.⁴ As the number of MR images done in the United States remains high at 118 per 1000,⁵ the ability to maximally use information from these images has the potential to influence a large number of patients. The cost of MRI, although nontrivial, remains cheaper than brain PET with a typical noncontrast MRI scan costing on average $437.20 compared to $1266.40 for an FDG-PET and $2721.83 for an amyloid PET.⁶ Thus, MRI remains the most widely available modality available to patients for evaluation of cognitive disorders such as AD and is approximately 3 to 6 times cheaper in cost than PET neuronuclear imaging.

There has also been an expansion over the past 2 decades in the use of volumetric MRI in research settings. A PubMed search of the terms “volumetric MRI” on September 27, 2019 yielded 8652 results for peer-reviewed publications. In 1999, there were 120 peer-reviewed publications on this topic with increasing numbers of such papers each successive year to 674 publications in 2018 and 662 thus far in 2019. Interestingly a publication overviewing the number of systematic reviews for different subspecialties of radiology from mammography to interventional techniques to neuroradiology found that the most evidence-based method with the highest number of systematic reviews was volumetric MRI with 241 systematic reviews as of 2014.⁷ Thus, volumetric MRI even 5 years ago retains a unique status amongst other radiological imaging methods with the most evidence-based application based upon the large number of systematic reviews.

However, the historical use of brain MRIs for the application of neurocognitive disorders and AD has been focused on visual evaluations to assess for “organic” lesions that could mimic AD. These include brain tumors, infarctions, dural AV fistulas, and infections. In this context, however, little was applied to extract information from these structural MR images to better identify the etiology of the underlying neurodegenerative disease or neurocognitive disorder. This review article overviews such methods, examples from the literature of such use in AD and other dementias, and finally we will suggest different options for applying volumetric quantification in practice.

Early Computer-based Methodology and Voxel-based Techniques

Computer-based algorithms for analyzing and extracting information from structural MRI images have, as of 2019, existed for more than 20 years and have been expressed in the literature with different innovations and iterations. One of the earlier examples of such methods was the boundary shift integral. This method uses the borders of the cerebral cortex on longitudinal registered images to detect changes in brain structure by movement of these borders. In one of the first articles on this topic, Freeborough and Fox⁸ used boundary shift integral to identify changes in 21 pairs of control images and 11 pairs of AD images to identify a mean loss of 1.8 cm³ brain volume in controls versus 34.7 cm³ of brain volume loss in AD over the course of 386 days or little more than 1 year. Although this method demonstrated the longitudinal volume loss expected with progressive atrophy in AD, it could only be applied to longitudinal pairs of images. Methods that could permit for the analysis of cross-sectional data were also needed. A key development in this area was the creation of voxel-based morphometry (VBM) which was first introduced within the statistical parametric mapping framework into the literature as early as 1995.^9,10 This approach allows for the analysis of structural MR brain images by warping them into a common stereotactic space, typically Montreal Neurologic Institute space, followed by segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid images that is then smoothed by a Gaussian filter for use in statistical analyses. The main advantage of this method is its application in large cohort analyses^11,12 to identify patterns of structural abnormality such as aging and AD. A related method, called tensor-based morphometry can also identify changes in brain structure of GM or WM of a given cohort of patients or research subjects based upon quantitative comparison to a normal template.^13,14 Newer methods such as surface-based morphometry allow for voxel-wise determination of cortical thickness and statistical comparisons of this metric between groups of controls and patients.¹⁵ However, several drawbacks of this method exist. From a methodological standpoint, the presence of thousands of voxels in structural brain MR images presents problems with multiple comparisons. The solutions for this problem have been addressed in multiple articles^16,17 and is out of the scope of discussion of this review. Perhaps more practically from a clinical perspective, the results of VBM studies while informative for understanding disease processes are harder to extrapolate to single subject patient cases due to the variability of results when comparing groups of patients with controls.

Region of Interest Quantification With Manual Segmentation

Region of interest (ROI) volumetric quantification particularly in AD has focused predominantly on the hippocampus. Such work typically involves a trained tracer with anatomical knowledge of the hippocampus physically hand drawing the borders of the hippocampus on successive coronal sections of the hippocampus. Standardized criteria for manual hippocampal segmentation has been proposed and hand-drawn hippocampal regions are considered a “gold standard” when comparing the results of automated algorithms for volumetric quantification.^18–20 Although these criteria addressed the standardization of manually segmented total, right, and left hippocampal volumes, it should be noted that similar criteria are being developed for the quantification of hippocampal subfields including CA1, CA2, CA3, the dentate gyrus, and the parahippocampal cortex.²¹ As of the time of this article, however, this criteria has not yet been finalized. With respect to comparisons between VBM and hand-drawn quantified hippocampal volume, one study showed high correlation between VBM using DARTEL and hand-drawn hippocampal volumes.²² DARTEL stands for Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra.²³ The method uses a series of deformable registrations between individual subject images of a given study and a mean template of all study subjects to produce better accuracy in GM, WM, and cerebrospinal fluid segmentation in brain MR images. Another study comparing the ability of VBM versus hippocampal manual tracings for detecting AD atrophy showed VBM detected AD atrophy in the hippocampus with 96% area under the curve versus 89% for manual hippocampal tracings.²⁴ Although manual hippocampal tracings were essential for establishing a method for determining hippocampal volumes, the main drawback in this method particularly for clinical applications is time. One article determined that the time required for manual segmentation of both the right and left hippocampal volumes was 40 minutes per patient.²⁵ Such time requirements make the clinical application of hand-drawn hippocampal volumes less practical, and thus necessitating the development of faster automated ROI quantification methods.

Automated Methods for Region of Interest Quantification of Brain Structure

The most commonly used software program for automated ROI analysis is Freesurfer (https://surfer.nmr.mgh.harvard.edu/), with its initial publication on cortical segmentation and quantification published in 1999.²⁶ This same program was then used to generate cortical thickness maps.²⁷ Eventually, a separate atlas called the Desikan-Killiany atlas for gyral-based ROI quantification was generated with 68 parcellations bilaterally including for hippocampal, entorhinal, and parahippocampal gyrus quantifications.²⁸ Eventually, a version of this software program that includes hippocampal subfield quantification was also released.²⁹ This method also highlights the importance of atlas-based segmentation in producing valid reproducible results. FreeSurfer produces results with good reproducibility with cortical thickness, surface area, and brain volume intraclass correlation coefficients of 81%, 87%, and 88%, respectively. With respect to the hippocampus, FreeSurfer showed good agreement with manual hippocampal segmentations with intraclass correlation coefficients of 84.6% for the right hippocampus and 85.9% for the left hippocampus.²⁵ In addition, another study showed that the test-rest error for hippocampal measurements was approximately 2% with FreeSurfer (version 6.0) in the whole hippocampus, CA2–3, CA4-dentate gyrus and subiculum and about 5% for the CA1 and presubiculum. It should be noted that the choice of FreeSurfer version can influence results. One investigation found that when comparing FreeSurfer results across multiple versions (v4.3.1, v4.5.0, and v5.0.0) the differences brain volume averaged 8.8% and cortical thickness differences between versions averaged 2.8%.³⁰ Thus, when using FreeSurfer in cross-sectional and in particular longitudinal investigations the same version should be used to attain greater accuracy in results. Although Freesurfer does generate hippocampal subfield measurements, it is not the only program to do so. A program called volBrain (https://www.volbrain.upv.es/) also generates hippocampal subfield metrics either from a volumetric T1 image, such as an Magnetization-Prepared Rapid Gradient-Echo or Spoiled Gradient Recalled Acquisition in Steady State, or a combination of a volumetric T1 and T2 image.³¹ Another method that also results in automated ROIs within the statistical parametric mapping platform is called Individual Brain Atlases using Statistical Parametric Mapping (IBASPM).³² This program has been applied in MRI scans of persons with AD showing good area under the curve as high as 88% for the left hippocampus and 87% for the right hippocampus.³³ However, IBASPM in a separate study of hippocampal volume in major depressive disorder did not show good agreement with FreeSurfer. In that study, FreeSurfer volumes were on average 35% higher than IBASPM.³⁴ The reproducibility for deep gray structures was investigated in a multicenter study showing a 1-year volume change ranging from 1.26% for the left thalamus to 8.63% for the right amygdala.³⁵ Thus, basic knowledge of reproducibility measures of volumetric MRI should be kept in mind when interpreting these data and should be integrated with the clinical picture evaluated in a given patient.

MAGNETIC RESONANCE VOLUMETRIC QUANTIFICATION IN NEUROCOGNITIVE DISORDERS

Neurodegenerative Disease: Emphasis On Alzheimer Disease

An exhaustive review of volumetric findings in neurodegenerative disease is beyond the scope of this article. However, several trends can be noted from literature in major disorders such as AD and its proposed prodromal state, mild cognitive impairment (MCI). One study comparing hippocampal volumes in AD and MCI compared to dementia with Lewy bodies suggested lower hippocampal volumes – particularly CA1 and the subiculum compared to dementia with Lewy body.³⁶ There is also evidence that loss of MRI volumes may precede the clinical diagnosis of AD. Hippocampal volumes are lower by approximately 10% 3 years before AD diagnosis is rendered. In addition, there is a graded acceleration of hippocampal volume loss rates up to 5 years before AD is clinically identified. Specifically, such work has demonstrated 6% decrease in volume with atrophy accelerating by 0.3% per year in the 2 to 4 (at ∼0.3%/yr 2) in the years leading up to the diagnosis during the MCI stage.^37–39 This work highlights the importance of longitudinal imaging in the evaluation of dementia, particularly AD. If one wants to use imaging to raise the possibility of an underlying neurodegenerative disorder a helpful imaging sign is the presence of regionally specific progressive volume loss, particularly when quantified. Thus, in this framework, simply showing low hippocampal volume loss at one time point may be suggestive but ultimately insufficient for raising the possibility of AD. However, progressive quantified hippocampal volume loss on MRI would be a better indicator particularly when the cognitive decline is both domain specific for AD and along a chronically progressive time course. A corollary to this concept as to the importance of longitudinal imaging can also be observed in progressive supranuclear palsy where longitudinal brain stem atrophy is correlated to clinical progression.⁴⁰ Notably, longitudinal volume loss can be difficult to evaluate even for experienced neuroradiologists. In traumatic brain injury (TBI), where brain atrophy is commonly observed, a comparison of radiologist interpretation and NeuroQuant found that NeuroQuant identified atrophy (50%), asymmetry (83.3%), and progressive volume loss (70%) compared to radiologist visual interpretation performance on the same cases (12.5, 0%, and 0%, respectively for the same metrics).⁴¹ Incorporating volumetric results into the interpretation of clinical scans also requires an understanding of the regional specificity of brain volume loss. For example, TBI-related atrophy has been identified in frontal⁴² and ventral diencephalic regions.⁴³ In behavior variant frontal temporal dementia, prominent quantified frontal-striatal atrophy has been identified while the striatum remains relatively preserved in AD.⁴⁴ Cortical and subcortical atrophy has been quantified on MRI and is related to the underlying burden of WM hyperintensities.⁴⁵ The locational specificity of volume loss differs in AD compared to normal aging. One VBM study of 169 cognitively normal controls who remained cognitively normal 5 years after their MRI scan compared to 33 persons with probable AD showed anterior hippocampal and entorhinal volume loss related to AD compared to posterior hippocampal volume loss related to aging.¹² This work further supported the concept that from a quantitative MRI standpoint, AD is not a disorder of accelerated aging.⁴⁶ Another study of hippocampal subfields with FreeSurfer 6.0 demonstrated volume loss in the CA1, subiculum, presubiculum, molecular layer, and fimbria with correlation of the left subiculum most strongly correlated with cognitive measures.⁴⁷ Such hippocampal subfield specificity in AD will be an important focus of future evaluations as hippocampal volume loss, while typically noted in AD, can also be seen in other disorders such as depression²⁵ and mesial temporal sclerosis.⁴⁸ It should also be noted that the delineation of such subfields to identify volume loss is harder to perform on visual inspection of MR images compared to quantitative metrics.

APPLICATION OF MAGNETIC RESONANCE VOLUMETRIC QUANTIFICATION IN CLINICAL PRACTICE

Protocol Considerations

For regional quantification to be performed, it is essential to acquire volumetric T1-weighted images as part of the imaging protocol. All scans should ideally be acquired on a 3 T scanner. Should 1.5 T scanners only be available then this can be acceptable for 3D T1 volumetric imaging volumetric imaging, but follow-up scans should be done on the same field strength to allow for better comparison. On GE scanners, 3D T1 images are denoted a as Spoiled Gradient Recalled Acquisition in Steady State and on Siemens scanners these are referred to as 3D magnetization-prepared rapid gradient-echo (MPRAGE). A study comparing 2D to 3D sequences found substantially significant reductions in the accuracy of FreeSurfer quantifications on 2D spin echo versus 3D MPRAGE T1 images. Such T1 3D volumetric images are best acquired in sagittal acquisition as this shortens scan time and then can be later reconstructed into axial or coronal reformats. When acquiring additional sequences with these 3D T1-weighted images as part of a dementia imaging protocol, additional sequences should also be obtained to evaluate other secondary causes of dementia. These include diffusion-weighted images for evaluation of acute infarction, Jakob–Creutzfeldt disease, infection, or other metabolic of inflammatory processes. Similar processes can also be interrogated on T2-weighted and FLAIR images that are best utilized for evaluation of vascular dementia. The decision of whether or not to use 2D or 3D T2 and FLAIR imaging depends on whether or not subsequent postprocessing will be used to quantify FLAIR lesion hyperintensities or if 3D T2-weighted images may be used as part of a hippocampal subfield postprocessing pipeline. Within this context, separate dedicated hippocampal imaging need not be applied in clinical practice, although this may change with the application of new sequences in the future, such a 7 T hippocampal imaging that is not standard of care at present.⁴⁹ To evaluate for cerebral microbleeds, that may also be important for identifying hypertensive pathology, cerebral amyloid angiopathy, or evidence of TBI, susceptibility sensitive sequences are also important. Even with inclusion of 3D T1-weighted, T2, and FLAIR images this entire protocol can reasonably be obtained in approximately 30 minutes, ensuring adequate clinical throughput in either inpatient or outpatient settings. In addition, postcontrast imaging need not be applied in a protocol to evaluate for dementia or cognitive impairment unless a clinical suspicion exists for neoplasm as the etiology of dementia and such suspicion may be raised in those with a history of primary malignancy. If postcontrast imaging is applied it is important to note that such images cannot be used for volumetric quantification as only noncontrast 3D T1-weighted images can be used. The inclusion of other advanced imaging methods such as functional MRI or arterial spin labeled, whereas both important for the evaluation of dementia^3,50 are not included in protocol considerations in this article as it is not assumed that all practice settings have the technical resources to employ such advanced imaging. However, the above imaging protocol suggestions are easy to apply in most MRI centers and are thus potentially the most widely accessible to patients and physicians.

SOFTWARE CONSIDERATIONS

The choice of software for volumetric imaging analysis depends greatly upon several factors. First is the existing analysis resources in a given practice group or setting. For example, if an academic group has extensive in-house software or uses well validated software such as FreeSurfer with the ability to customize cohorts for analysis, then it may be advantageous to use such an approach. However, if for example a private practice group lacks such resources and needs the highest possible throughput then use of an FDA cleared software program is an option particularly with groups that may initially lack technical experience in applying such programs. The cost to use such programs per patient averages $82.68 and is covered by some insurance plans under the CPT code 76377. The first such FDA cleared program is called NeuroQuant (https://www.cortechslabs.com/products/neuroquant/). It has been applied in AD,⁵¹ TBI,⁵² multiple sclerosis,⁵³ and toxic mold exposure.⁵⁴ The second FDA cleared program for evaluation of brain structure is called Neuroreader (https://brainreader.net/) and has been validated in AD with correlations to manually segmented hippocampal volumes as high as 91%⁵⁵ and MCI⁵⁶ while also being applied in TBI⁴³ and mesial temporal sclerosis.⁴⁸ The other FDA cleared software by IcoMetrix (https://icometrix.com/) has published in multiple sclerosis. The newest FDA cleared program, Quantib (https://www.quantib.com), while developed based upon data from a well published epidemiological database called the Rotterdam Study⁵⁷ does not have any publications at the time of the writing of this review. All of these programs provide automated quantification of both hippocampal and extra hippocampal structures including lobar and subcortical regions. It should also be noted that in addition to all of the software programs discussed in this review additional publically available programs exist for hippocampal quantification such as JFL/CL (https://www.nitrc.org/projects/ashs/; https://sites.google.com/site/hipposubfields/) and HIPPOSEG (http://niftyweb.cs.ucl.ac.uk/program.php?p=BRAIN-STEPS). However, these programs are not FDA cleared and limited to hippocampal volumetric quantification.

FUTURE DIRECTIONS

MR volumetric quantification in neuroimaging offers considerable value for the variety of neurological disorders discussed in this review. Yet, there remains a disconnect between the wealth of peer-reviewed literature that exists to support the use of this method and actual clinical applications. This gap can be bridged by collaboration between neurologists, neuroradiologists, geriatricians, and geriatric psychiatrists to develop the best clinical programs for the dissemination of these data in clinical reports, communication of related information to patients, and feedback between patients to clinicians and between clinicians as well to improve the effectiveness of MR volumetric quantification. As artificial intelligence algorithms continue to evolve in dementia diagnosis,⁵⁸ the importance of MR quantified volumes as input data for such machine learning tools will be an important application of this emerging technology to the future added value of neuroimaging in neurocognitive disorders.