Automatically computed rating scales from MRI for patients with cognitive disorders.

OBJECTIVES: The aims of this study were to examine whether visual MRI rating scales used in diagnostics of cognitive disorders can be estimated computationally and to compare the visual rating scales with their computed counterparts in differential diagnostics. METHODS: A set of volumetry and voxel-based morphometry imaging biomarkers was extracted from T1-weighted and FLAIR images. A regression model was developed for estimating visual rating scale values from a combination of imaging biomarkers. We studied three visual rating scales: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and white matter hyperintensities (WMHs) measured by the Fazekas scale. Images and visual ratings from the Amsterdam Dementia Cohort (ADC) (N = 513) were used to develop the models and cross-validate them. The PredictND (N = 672) and ADNI (N = 752) cohorts were used for independent validation to test generalizability. RESULTS: The correlation coefficients between visual and computed rating scale values were 0.83/0.78 (MTA-left), 0.83/0.79 (MTA-right), 0.64/0.64 (GCA), and 0.76/0.75 (Fazekas) in ADC/PredictND cohorts. When performance in differential diagnostics was studied for the main types of dementia, the highest balanced accuracy, 0.75-0.86, was observed for separating different dementias from cognitively normal subjects using computed GCA. The lowest accuracy of about 0.5 for all the visual and computed scales was observed for the differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Computed scales produced higher balanced accuracies than visual scales for MTA and GCA (statistically significant). CONCLUSIONS: MTA, GCA, and WMHs can be reliably estimated automatically helping to provide consistent imaging biomarkers for diagnosing cognitive disorders, even among less experienced readers. KEY POINTS: • Visual rating scales used in diagnostics of cognitive disorders can be estimated computationally from MRI images with intraclass correlations ranging from 0.64 (GCA) to 0.84 (MTA). • Computed scales provided high diagnostic accuracy with single-subject data (area under the receiver operating curve range, 0.84-0.94).

Fast and robust multi-atlas segmentation of brain magnetic resonance images.

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N=18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N=60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3-4 min. Our results compare favourably with other recently published results.

Early familial dilated cardiomyopathy: identification with determination of disease state parameter from cine MR image data.

PURPOSE: To characterize early changes in cardiac anatomy and function for lamin A/C gene (LMNA) mutation carriers by using magnetic resonance (MR) imaging and to develop tools to analyze and visualize the findings. MATERIALS AND METHODS: The ethical review board of the institution approved the study, and informed written consent was obtained. The patient group consisted of 12 subjects, seven women (mean age, 36 years; age range, 18-54 years) and five men (mean age, 28 years; age range, 18-39 years) of Finnish origin, who were each heterozygotes with one LMNA mutation that may cause familial dilated cardiomyopathy (DCM). All the subjects were judged to be healthy with transthoracic echocardiography. The control group consisted of 14 healthy subjects, 11 women (mean age, 41 years; range, 23-54 years) and three men (mean age, 45 years; range, 34-57 years), of Finnish origin. Cine steady state free precession MR imaging was performed with a 1.5-T system. The volumes, wall thickness, and wall motion of both left ventricle (LV) and right ventricle were assessed. A method combining multiple MR image parameters was used to generate a global cardiac function index, the disease state parameter (DSP). A visual fingerprint was generated to assess the severity of familial DCM. RESULTS: The mean DSP of the patient group (0.69 +/- 0.15 [standard deviation]) was significantly higher than that of the control group (0.32 +/- 0.13) (P = .00002). One subject had an enlarged LV. CONCLUSION: Subclinical familial DCM was identified by determination of the DSP with MR imaging, and this method might be used to recognize familial DCM at an early stage.

Evaluation of cardiac biventricular segmentation from multiaxis MRI data: a multicenter study.

PURPOSE: To validate a volumetric biventricular segmentation solution for multiaxis cardiac magnetic resonance (CMR) images. MATERIALS AND METHODS: The study population comprised 40 subjects. Biventricular end-diastolic and -systolic phases were segmented from both short-axis and horizontal long-axis or transaxial cine CMR images. Segmentation was based on fitting nonrigidly a 3D surface model to multiaxis CMR images. Five segmentations were performed: two manual segmentations by experts, automatic segmentation, and two segmentations where a user was allowed to correct errors in the automatic segmentation for 2 minutes and without time limits. Volumetry, distance measures, and visual grading were used to evaluate the quality of the segmentation. RESULTS: No difference was observed between automatic and manual segmentations in volumetric measures of the ventricles. The manual segmentation performed better for left-ventricular myocardial volume. The distance between surfaces as well as visual analysis did not show differences between automatic and manual segmentation for the endocardial border of the left ventricle but some corrections are needed for the right ventricle. CONCLUSION: Fully automatic segmentation produces good results in the assessment of left ventricular volume andendocardial border. Two minutes of user interaction are needed to obtain accurate results for the right ventricle.