This research builds on our hypothesis that white matter damage and associated neurocognitive symptoms, in children treated for cancer with cranial spinal irradiation, spans a continuum of severity that can be reliabl...
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ISBN:
(纸本)0819444286
This research builds on our hypothesis that white matter damage and associated neurocognitive symptoms, in children treated for cancer with cranial spinal irradiation, spans a continuum of severity that can be reliably probed using noninvasive MR technology. Quantitative volumetric assessments of MR imaging and psychological assessments were obtained in 40 long-term survivors of malignant brain tumors treated with cranial irradiation. Neurocognitive assessments included a test of intellect (Wechsler Intelligence Test for Children, Wechsler Adult Intelligence Scale), attention (Conner's Continuous Performance Test), and memory (California Verbal Learning Test). One-sample t-tests were conducted to evaluate test performance of survivors against age-adjusted scores from the test norms;these analyses revealed significant impairments in all apriori selected measures of intelligence, attention, and memory. Partial correlation analyses were performed to assess the relationships between brain tissues volumes (normal appearing white matter (NAWM), gray matter, and CSF) and neurocognitive function. Global intelligence (r = 0.32, p = 0.05) and global attentional (r = 0.49, p < 0.01) were significantly positively correlated with NAWM volumes, whereas global memory was significantly positively correlated with overall brain parenchyma (r = 0.38, p = 0.04). We conclude that quantitative assessment of MR examinations in survivors of childhood cancer treated with cranial irradiation reveal that loss of NAWM is associated with decreased intellectual and attentional deficits, whereas overall parenchyma loss, as reflected by increased CSF and decreased white matter, is associated with memory-related deficits.
To reduce partial volume contamination, we present a linear interpolation combining quantitative T1 information with segmented base images. In addition, manual segmentation was completed for comparison to both of the ...
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ISBN:
(纸本)0819440086
To reduce partial volume contamination, we present a linear interpolation combining quantitative T1 information with segmented base images. In addition, manual segmentation was completed for comparison to both of the techniques. To quantitatively assess T1, a precise and accurate inversion recovery (PAIR) sequence was acquired. The Kohonen SOM segmentation algorithm used the four base images as inputs and had nine output neurons. The segmented regions were , manually classified by an expert for training a multi-layered backpropagation neural network to automate this process. A linear interpolation based on mean T1 relaxivity for each segmented class (regional method) and a pixel by pixel basis (pixel method) was performed. Manual segmentation was performed directly on base images by three observers. Differences between the techniques are reported as percent errors of the mean difference divided by the mean estimates of the manual segmentation. Within observer variances for the manual segmentation were less than 5.6% while between observer variances were 11.7% and 7.2% for white and gray matter respectively. The regional method had variances of 4.1 % and 1.0% while the pixel method produced variances of 5.8% and 1.5% for white and gray matter, respectively, compared to the manual segmentation.
This paper presents a novel semi-automated segmentation and classification method based on raw signal intensities from a quantitative T1 relaxation technique with two novel approaches for the removal of partial volume...
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This paper presents a novel semi-automated segmentation and classification method based on raw signal intensities from a quantitative T1 relaxation technique with two novel approaches for the removal of partial volume effects. The segmentation used a Kohonen Self Organizing Map that eliminated inter- and intra-operator variability. A Multi-layered Backpropagation Neural Network was able to classify the test data with a predicted accuracy of 87.2% when compared to manual classification. A Linear interpolation of the quantitative T1 information by region and on a pixel-by-pixel basis was used to redistribute voxels containing a partial volume of gray matter (GM) and white matter (WM) or a partial volume of GM and cerebrospinal fluid (CSF) into the principal components of GM, WM, and CSF. The method presented was validated against manual segmentation of the base images by three experienced observers. Comparing segmented outputs directly to the manual segmentation revealed a difference of less than 2% in GM and less than 6% in WM for pure tissue estimations for both the regional and pixel-by-pixel redistribution techniques. This technique produced accurate estimates of the amounts of GM and WM while providing a reliable means of redistributing partial volume effects. (C) 2001 Elsevier Science Inc. All rights reserved.
The evaluation of pediatric osteosarcoma has suffered from the lack of an accurate imaging measure of response. One major problem is that osteosarcoma do not shrink in response to chemotherapy;instead, viable tumor is...
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The evaluation of pediatric osteosarcoma has suffered from the lack of an accurate imaging measure of response. One major problem is that osteosarcoma do not shrink in response to chemotherapy;instead, viable tumor is replaced by necrotic tissue. Currently available techniques that use dynamic contrast-enhanced magnetic resonance imaging to quantitatively evaluate tumor response fail to assess the percentage of necrosis, At present, histopathologic evaluation of resected tissue is the only means of measuring the percentage of necrosis in treated osteosarcoma. The current study presents a non-invasive method to visualize necrotic and viable tumor and quantitatively assess the response of osteosarcoma, Our technique uses a hybrid neural network consisting of a Kohonen self-organizing map to segment dynamic contrast-enhanced magnetic resonance images and a multilayer backpropagation neural network to classify the segmented images. Because the hybrid neural network is completely automated, our technique removes both inter- and intra-operator error. An analysis comparing the percentage of necrosis from our technique to the histopathologic analysis revealed a highly significant Spearman correlation coefficient of 0.617 with p < 0.001. (C) 1998 Elsevier Science Inc.
In the treatment of children with brain tumors, balancing the efficacy of treatment against commonly observed side effects is difficult because of a lack of quantitative measures of brain damage that can be correlated...
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In the treatment of children with brain tumors, balancing the efficacy of treatment against commonly observed side effects is difficult because of a lack of quantitative measures of brain damage that can be correlated with the intensity of treatment. We quantitatively assessed volumes of brain parenchyma on magnetic resonance (MR) images using a hybrid combination of the Kohonen self-organizing map for segmentation and a multilayer backpropagation neural network for tissue classification. Initially, we analyzed the relationship between volumetric differences and radiologists' grading of atrophy in 80 subjects. This investigation revealed that brain parenchyma and white matter volumes significantly decreased as atrophy increased, whereas gray matter volumes had no relationship with atrophy. Next, we compared 37 medulloblastoma patients treated with surgery, irradiation, and chemotherapy to 19 patients treated with surgery and irradiation alone. This study demonstrated that, in these patients, chemotherapy had no significant effect on brain parenchyma, white matter, or gray matter volumes. We then investigated volumetric differences due to cranial irradiation in 15 medulloblastoma patients treated with surgery and radiation therapy, and compared these with a group of 15 age-matched patients with low-grade astrocytoma treated with surgery alone. With a minimum follow-up of one year after irradiation, all radiation-treated patients demonstrated significantly reduced white matter volumes, whereas gray matter volumes were relatively unchanged compared with those of age-matched patients treated with surgery alone. These results indicate that reductions in cerebral white matter: 1) are correlated significantly with atrophy;2) are not related to chemotherapy;and 3) are correlated significantly with irradiation. This hybrid neural network analysis of subtle brain volume differences with magnetic resonance may constitute a direct measure of treatment-induced brain damage. (C)
We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images, This hybrid neural network method uses a Kohonen self-organizing neural network for segmentatio...
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We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images, This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification, To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations, Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years;seven male, seven female), This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle, An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (r(i)) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively, An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p <0.05 for white matter, gray matter, and white/gray partial volumes, The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability, This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability.
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