使用图像强度不均匀性校正的非对比头部CT急性梗死体积的自动估计

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2019-08-21 DOI:10.1155/2019/1720270
K. Cauley, G. Mongelluzzo, S. Fielden
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引用次数: 9

摘要

在中风发作的最初几个小时内进行的非光栅头部CT扫描中,识别早期缺血性变化(EIC)可能对后续治疗具有重要意义,尽管这些研究对早期中风的界定很差。早期梗死缺乏清晰的病变边界,无法进行手动体积测量,也无法使用边缘检测或区域填充算法进行测量。我们希望检验这样一种假设,即图像强度不均匀性校正可以为识别早期缺血性梗死特有的细微区域低密度提供一种灵敏的方法。利用图像强度不均匀性校正(IIC)和强度阈值技术,开发了一种数字图像分析算法。比较了两种不同的IIC算法(FSL和ITK)。该方法通过模拟梗死和临床病例进行评估。对于合成梗死,测量的梗死体积与真实病变体积具有很强的相关性(对于密度降低20%的“梗死”,两种算法的Pearson r=0.998);两种算法都显示出随着病变大小的增加和病变密度的降低而提高的准确性。在临床病例中(30例患者中有41例急性梗死),使用FSL IIC计算的梗死体积与ASPECTS评分(Pearson r=0.680)和入院NIHSS(Pearsonr=0.544)相关。计算的梗死容量与静脉注射tPA治疗的临床决定高度相关。当应用于非光栅头CT时,图像强度不均匀性校正提供了一种用于图像分析的工具,以帮助检测EIC,并评估和指导扫描质量的改进,以实现EIC的最佳检测。
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Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction
Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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