Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease.
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ABSTRACT:
SUBMITTER: Brewer JB
PROVIDER: S-EPMC5947999 | biostudies-literature | 2009 Mar
REPOSITORIES: biostudies-literature
Publications
Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease.
Brewer J B JB Magda S S Airriess C C Smith M E ME
AJNR. American journal of neuroradiology 20081226 3
Volumetric analysis of structural MR images of the brain may provide quantitative evidence of neurodegeneration and help identify patients at risk for rapid clinical deterioration. This note describes tests of a fully automated MR imaging postprocessing system for volumetric analysis of structures (such as the hippocampus) known to be affected in early Alzheimer disease (AD). The system yielded results that correlated highly with independent computer-aided manual segmentation and were sensitive ...[more]
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Project description:To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland-Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.
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Project description:ObjectiveTo evaluate the spatial pattern and regional rates of neocortical atrophy from normal aging to early Alzheimer disease (AD).MethodsLongitudinal MRI data were analyzed using high-throughput image analysis procedures for 472 individuals diagnosed as normal, mild cognitive impairment (MCI), or AD. Participants were divided into 4 groups based on Clinical Dementia Rating Sum of Boxes score (CDR-SB). Annual atrophy rates were derived by calculating percent cortical volume loss between baseline and 12-month scans. Repeated-measures analyses of covariance were used to evaluate group differences in atrophy rates across regions as a function of impairment. Planned comparisons were used to evaluate the change in atrophy rates across levels of disease severity.ResultsIn patients with MCI-CDR-SB 0.5-1, annual atrophy rates were greatest in medial temporal, middle and inferior lateral temporal, inferior parietal, and posterior cingulate. With increased impairment (MCI-CDR-SB 1.5-2.5), atrophy spread to parietal, frontal, and lateral occipital cortex, followed by anterior cingulate cortex. Analysis of regional trajectories revealed increasing rates of atrophy across all neocortical regions with clinical impairment. However, increases in atrophy rates were greater in early disease within medial temporal cortex, whereas increases in atrophy rates were greater at later stages in prefrontal, parietal, posterior temporal, parietal, and cingulate cortex.ConclusionsAtrophy is not uniform across regions, nor does it follow a linear trajectory. Knowledge of the spatial pattern and rate of decline across the spectrum from normal aging to Alzheimer disease can provide valuable information for detecting early disease and monitoring treatment effects at different stages of disease progression.
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Project description:IntroductionVolumetric image analysis to detect progressive brain tissue loss in patients with multiple sclerosis (MS) has recently been suggested as a promising marker for "no evidence of disease activity." Software packages for longitudinal whole-brain volume analysis in individual patients are already in clinical use; however, most of these methods have omitted region-based analysis. Here, we suggest a fully automatic analysis pipeline based on the free software packages FSL and FreeSurfer.Materials and methodsFifty-five T1-weighted magnetic resonance imaging (MRI) datasets of five patients with confirmed relapsing-remitting MS and mild to moderate disability were longitudinally analyzed compared to a morphometric reference database of 323 healthy controls (HCs). After lesion filling, the volumes of brain segmentations and morphometric parameters of cortical parcellations were automatically screened for global and regional abnormalities. Error margins and artifact probabilities of regional morphometric parameters were estimated. Linear models were fitted to the series of follow-up MRIs and checked for consistency with cross-sectional aging in HCs.ResultsAs compared to leave-one-out cross-validation in a subset of the control dataset, anomaly detection rates were highly elevated in MRIs of two patients. We detected progressive volume changes that were stronger than expected compared to normal aging in 4/5 patients. In individual patients, we also identified stronger than expected regional decreases of subcortical gray matter, of cortical thickness, and areas of reducing gray-white contrast over time.ConclusionStatistical comparison with a large normative database may provide complementary and rater independent quantitative information about regional morphological changes related to disease progression or drug-related disease modification in individual patients. Regional volume loss may also be detected in clinically stable patients.
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