Noncontrast CT in acute stroke

Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs

Although cerebral edema is a major cause of death and deterioration following hemispheric stroke, there remains no validated biomarker that captures the full spectrum of this critical complication. We recently demonstrated that reduction in intracranial cerebrospinal fluid (CSF) volume (∆ CSF) on serial computed tomography (CT) scans provides an accurate measure of cerebral edema severity, which may aid in early triaging of stroke patients for craniectomy. However, application of such a volumetric approach would be too cumbersome to perform manually on serial scans in a real-world setting. We developed and validated an automated technique for CSF segmentation via integration of random forest (RF) based machine learning with geodesic active contour (GAC) segmentation. The proposed RF + GAC approach was compared to conventional Hounsfield Unit (HU) thresholding and RF segmentation methods using Dice similarity coefficient (DSC) and the correlation of volumetric measurements, with manual delineation serving as the ground truth. CSF spaces were outlined on scans performed at baseline (< 6 h after stroke onset) and early follow-up (FU) (closest to 24 h) in 38 acute ischemic stroke patients. RF performed significantly better than optimized HU thresholding (p < 10^− 4 in baseline and p < 10^− 5 in FU) and RF + GAC performed significantly better than RF (p < 10^− 3 in baseline and p < 10^− 5 in FU). Pearson correlation coefficients between the automatically detected ∆ CSF and the ground truth were r = 0.178 (p = 0.285), r = 0.876 (p < 10^− 6) and r = 0.879 (p < 10^− 6) for thresholding, RF and RF + GAC, respectively, with a slope closer to the line of identity in RF + GAC. When we applied the algorithm trained from images of one stroke center to segment CTs from another center, similar findings held. In conclusion, we have developed and validated an accurate automated approach to segment CSF and calculate its shifts on serial CT scans. This algorithm will allow us to efficiently and accurately measure the evolution of cerebral edema in future studies including large multi-site patient populations.

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Random forest with use of Haar-like features was used to segment CSF on CT scans.

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This machine-learning algorithm performed better than Hounsfield Unit thresholding.

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This automated method quantified change in CSF volume (∆ CSF) between CT scans.

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∆ CSF may quantify severity of cerebral edema after stroke.

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Automated approaches to quantify cerebral edema may aid with large-scale studies.

Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS ([Formula: see text] and [Formula: see text], respectively) and with follow-up mRS ([Formula: see text] and [Formula: see text]), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences [Formula: see text] point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.

3D movement correction of CT brain perfusion image data of patients with acute ischemic stroke

INTRODUCTION

Head movement during CT brain perfusion (CTP) acquisition can deteriorate the accuracy of CTP analysis. Most CTP software packages can only correct in-plane movement and are limited to small ranges. The purpose of this study is to validate a novel 3D correction method for head movement during CTP acquisition.

METHODS

Thirty-five CTP datasets that were classified as defective due to head movement were included in this study. All CTP time frames were registered with non-contrast CT data using a 3D rigid registration method. Location and appearance of ischemic area in summary maps derived from original and registered CTP datasets were qualitative compared with follow-up non-contrast CT. A quality score (QS) of 0 to 3 was used to express the degree of agreement. Furthermore, experts compared the quality of both summary maps and assigned the improvement score (IS) of the CTP analysis, ranging from -2 (much worse) to 2 (much better).

RESULTS

Summary maps generated from corrected CTP significantly agreed better with appearance of infarct on follow-up CT with mean QS 2.3 versus mean QS 1.8 for summary maps from original CTP (P = 0.024). In comparison to original CTP data, correction resulted in a quality improvement with average IS 0.8: 17 % worsened (IS = -2, -1), 20 % remained unchanged (IS = 0), and 63 % improved (IS = +1, +2).

CONCLUSION

The proposed 3D movement correction improves the summary map quality for CTP datasets with severe head movement.

Multiparametric multidetector computed tomography scanning on suspicion of hyperacute ischemic stroke: validating a standardized protocol

Multidetector computed tomography (MDCT) scanning has enabled the early diagnosis of hyperacute brain ischemia. We aimed at validating a standardized protocol to read and report MDCT techniques in a series of adult patients. The inter-observer agreement among the trained examiners was tested, and their results were compared with a standard reading. No false positives were observed, and an almost perfect agreement (Kappa>0.81) was documented when the CT angiography (CTA) and cerebral perfusion CT (CPCT) map data were added to the noncontrast CT (NCCT) analysis. The inter-observer agreement was higher for highly trained readers, corroborating the need for specific training to interpret these modern techniques. The authors recommend adding CTA and CPCT to the NCCT analysis in order to clarify the global analysis of structural and hemodynamic brain abnormalities. Our structured report is suitable as a script for the reproducible analysis of the MDCT of patients on suspicion of ischemic stroke.