Making the Most of Brain Imaging Through Quantitative Volumetric Analysis
Volumetric analysis of brain MRI allows for accurate and prompt rendering of the cortical surface. Here’s how we’re using it in three common neurological diseases.
By Kunio Nakamura, PhD; James B. Leverenz, MD; and Z. Irene Wang, PhD
Cleveland Clinic is a non-profit academic medical center. Advertising on our site helps support our mission. We do not endorse non-Cleveland Clinic products or services Policy
Important MRI characteristics of neurological diseases can be obtained from volumetric analysis of brain MRI. Current technologies allow for volumetric analysis to achieve realistic rendering of the cortical surface in an accurate and prompt manner. Cleveland Clinic’s Neurological Institute has been applying this technology across multiple subspecialties. A few examples are profiled below.
One of Cleveland Clinic’s long-standing research interests in multiple sclerosis (MS) is volumetric brain analysis. Since our first report in 1999 on brain atrophy in MS using brain parenchymal fraction,1 Cleveland Clinic’s Lerner Research Institute and Mellen Center for Multiple Sclerosis have continued to perform quantitative measurement of whole-brain atrophy in both clinical trials and research studies. The method has been applied in more than 20 separate clinical and research studies to date on over 40,000 MRIs collected here and from other centers.
We have also developed methods for measuring cortical thinning, i.e., the cortical longitudinal atrophy detection algorithm (CLADA),2 and for measuring gray matter atrophy using pairwise Jacobian integration.3 These methods are particularly suited for longitudinal studies in that they are designed to detect subtle differences in brain MRIs and quantify the structural change.
More recently, we have studied thalamic atrophy, which occurs early in the course of MS. To this end, we have created a large normative trajectory of thalamic volumes from over 1,500 neurologically normal subjects using publicly available databases to more accurately detect abnormal thalamic volumes4 (Figure 1).
Cleveland Clinic Lou Ruvo Center for Brain Health is a three-site program that accommodates more than 14,000 patient visits annually at locations in Cleveland, Ohio; Las Vegas, Nevada; and Weston, Florida. In addition to standard MRI imaging, all scans performed by our center can have automated post-imaging volumetric measurements to accurately assess for atrophy and other characteristics, such as severity of white matter changes. We also employ amyloid imaging, FDG-PET and both qualitative and quantitative dopamine transporter imaging (DaTscan™ and DaTQUANT™) (Figure 2).
We frequently supplement findings from these advanced imaging modalities with cerebrospinal fluid analysis to confirm atypical Alzheimer’s disease (e.g., early onset, atypical clinical syndrome) and to diagnose rare neurological disorders such as paraneoplastic and prion disease. Imaging findings are frequently integrated in many of the 25-plus active clinical trials in the Lou Ruvo Center for Brain Health research program. Distinctive offerings include clinical trials in pre-symptomatic at-risk patients, repurposing of medications, and treatments for non-Alzheimer’s dementias such as Lewy body disease.
At Cleveland Clinic’s Epilepsy Center, one recent success with MRI volumetric analysis has been achieved in the diagnosis prediction of patients with epilepsy and Rasmussen’s encephalitis (RE).5 Over the past decade, MRI has become an increasingly important tool in the diagnosis of RE, as well as in assessing disease progression and therapeutic effectiveness. We set out to examine how volumetric measures can help predict RE — i.e., whether the extent of atrophy on MRI can be quantified to predict the probability that a patient with suspected RE truly has RE.
In our study, we included 42 MRI scans from a group of pediatric patients with RE, and performed automated MRI volumetric measurements (one example is shown in Figure 3). Ratios of volumes from the affected hemisphere divided by those from the unaffected hemisphere (such as interhemispheric ratio) were used as input to a logistic regression classifier that was trained to separate patients from controls.
Our study showed that automated quantitative volumetric analysis provides accurate separation of RE patients from normal controls and non-RE epilepsy patients (Figure 4) and thus may assist in the diagnosis of RE.
Used in relevant clinical settings, such as initial and follow-up investigations in epilepsy patients with suspected RE, the probability curve that was estimated (see our study5) can potentially provide an objective measure to solidify the confidence of an RE diagnosis. Additionally, with the methodology established in this paper, such patients can be studied prospectively by comparing volumetric findings with surgical pathology/biopsy.
Dr. Nakamura (firstname.lastname@example.org) is a project scientist in the Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute.
Dr. Leverenz (email@example.com) is Director of Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland.
Dr. Wang (firstname.lastname@example.org) is a staff scientist in Cleveland Clinic’s Epilepsy Center and joint staff in the Department of Biomedical Engineering.