Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans.

PURPOSE: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation. METHODS: Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features. RESULTS: The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%. CONCLUSION: Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

Effect of baseline magnetic resonance imaging (MRI) apparent diffusion coefficient lesion volume on functional outcome in ischemic stroke.

OBJECTIVE: We explored the relationship between predicted infarct core, predicted ischemic penumbras and predicted final infarct volumes obtained though apparent diffusion coefficient (ADC)-based method, as well as other clinical variables, and functional outcome. METHODS: Patients with acute cerebral ischemic stroke were retrospectively recruited. The National Institutes of Health Stroke Scale score was evaluated at baseline and the modified Rankin Scale (mRS) at day 90. Favorable outcome was defined as an mRS score of 0 to 2, and unfavorable outcome as 3 to 6. Multimodal stroke magnetic resonance imaging was carried out at presentation. The volumes of diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) were measured using the regions of interest (ROI) method. The volumes of predicted infarct core, predicted ischemic penumbra and predicted final infarct were obtained by an automated image analysis system based on baseline ADC maps. The association between baseline magnetic resonance imaging volumes, baseline clinical variables, and functional outcome was statistically analyzed. RESULTS: The study included 30 males and 20 females (mean±SD age, 56±10 years). Baseline DWI, PWI and PWI-DWI mismatch volumes were not correlated with day-90 mRS (P>0.05). Predicted infarct core, predicted ischemic penumbra and predicted final infarct through ADC-based method were all correlated with day-90 mRS (P<0.05). A better outcome was associated with a smaller predicted volume. Low baseline National Institutes of Health Stroke Scale and recanalization also demonstrated a trend toward a favorable outcome. Receiver operating characteristic analysis showed that the area under the curve of predicted final infarct volume and recanalization were higher with statistical significance (P<0.001). DISCUSSION: Predicted volumes obtained from ADC-based methods, especially predicted final infarct volume, as well as baseline National Institutes of Health Stroke Scale and recanalization may have effect on functional outcome in acute ischemic stroke.

Prediction of infarct core and salvageable ischemic tissue volumes by analyzing apparent diffusion coefficient without intravenous contrast material.

RATIONALE AND OBJECTIVES: To investigate whether baseline apparent diffusion coefficient (ADC) maps can be employed to predict both infarct core and salvageable ischemic tissue volumes in acute ischemic stroke. MATERIALS AND METHODS: An automated image analysis system based on baseline ADC maps was tested against 30 patients with acute ischemic stroke of anterior circulation to predict both infarct core and salvageable ischemic tissue volumes. The predicted infarct core and predicted salvageable ischemic tissue were quantitatively and qualitatively compared with follow-up imaging data in recanalization and no recanalization groups, respectively. Direct comparisons with perfusion- and diffusion- weighted magnetic resonance imaging measures were also made. Wilcoxon signed-rank test, Spearman rank correlation, and Bland-Altman plots were performed. RESULTS: In the recanalization group, the predicted infarct core volume was significantly correlated with the final infarct volume (r = 0. 868, P < .001). In the no recanalization group, the predicted final infarct volume (sum of the predicted infarct core and salvageable ischemic tissue volumes), as well as the predicted salvageable ischemic tissue volume, was also significantly correlated with the true final infarct volume (r = 0.955, P < .001) and infarct growth (r = 0.918, P < .001), respectively. The volumes of perfusion-diffusion mismatch were significantly larger than those of infarct growth and predicted salvageable ischemic tissue. Good agreement between predicted and true final infarct lesions was visualized by Bland-Altman plots in two groups. Direct visual comparative analysis revealed good qualitative agreement between the true final infarct and predicted lesions in 21 patients. CONCLUSION: The proposed ADC based approach may be a feasible and practical tool to predict the volumes of infarct core and salvageable ischemic tissue without intravenous contrast media-enhanced perfusion-weighted imaging at baseline.