Imma Boada

Wallerian Degeneration in the Corticospinal Tract Evaluated by Diffusion Tensor Imaging Correlates with Motor Deficit 30 Days after Middle Cerebral Artery Ischemic Stroke

BACKGROUND AND PURPOSE: The quantification and clinical significance of WD in CSTs following supratentorial stroke are not well understood. We evaluated the anisotropy by using DTI and signal-intensity changes on conventional MR imaging in the CST to determine whether these findings are correlated with limb motor deficit in patients with MCA ischemic stroke. MATERIALS AND METHODS: We studied 60 patients within 12 hours of stroke onset. At admission, day 3, and day 30 of evolution, patients underwent multimodal MR imaging, including DTI sequences. We assessed the severity of limb weakness by using the motor subindex scores (5a, 5b, 6a, 6b) of the m-NIHSS and established 3 groups: I (m-NIHSS scores of 0), II (m-NIHSS, 1–4), and III (m-NIHSS, 5–8). FA values and rFAs were measured on the affected and the unaffected CSTs in the pons. RESULTS: FA values for the CST were significantly lower on the affected side compared with the unaffected side only at day 30 (P < .001), and the rFA was significantly correlated with the motor deficit at day 30 (P < .001; r = −0.793). The sensitivity, specificity, and positive and negative predictive values for motor deficit by rFA < 0.925 were 95.2%, 94.9%, 90.9%, and 97.4%, respectively. CONCLUSIONS: WD in the CST revealed by DTI correlates with motor deficit 30 days after MCA ischemic stroke. This study highlights the utility of imaging follow-up at 30 days and the potential of DTI as a surrogate marker in clinical trials.

Acute damage to the posterior limb of the internal capsule on diffusion tensor tractography as an early imaging predictor of motor outcome after stroke.

Practical applications of diffusion tensor imaging are few, but this seems to be an interesting and a potentially important one: can it be used to predict motor outcome after stroke? Sixty patients within 12 hours of stroke were assessed with tractography at 5 different locations in the corticospinal tracts at admission, and at days 3 and 30. Patients with acute damage to the posterior limb of the internal capsule had the worst outcome and clinical severity at presentation. Conclusions: In the acute setting, tractography is promising for stroke mapping to predict motor outcome. Acute corticospinal tract damage at the level of the posterior limb of the internal capsule is a significant predictor of unfavorable motor outcome. BACKGROUND AND PURPOSE: Early prediction of motor outcome is of interest in stroke management. We aimed to determine whether lesion location at DTT is predictive of motor outcome after acute stroke and whether this information improves the predictive accuracy of the clinical scores. MATERIALS AND METHODS: We evaluated 60 consecutive patients within 12 hours of middle cerebral artery stroke onset. We used DTT to evaluate CST involvement in the motor cortex and premotor cortex, centrum semiovale, corona radiata, and PLIC and in combinations of these regions at admission, at day 3, and at day 30. Severity of limb weakness was assessed by using the motor subindex scores of the National Institutes of Health Stroke Scale (5a, 5b, 6a, 6b). We calculated volumes of infarct and fractional anisotropy values in the CST of the pons. RESULTS: Acute damage to the PLIC was the best predictor associated with poor motor outcome, axonal damage, and clinical severity at admission (P < .001). There was no significant correlation between acute infarct volume and motor outcome at day 90 (P = .176, r = 0.485). The sensitivity, specificity, and positive and negative predictive values of acute CST involvement at the level of the PLIC for motor outcome at day 90 were 73.7%, 100%, 100%, and 89.1%, respectively. In the acute stage, DTT predicted motor outcome at day 90 better than the clinical scores (R2 = 75.50, F = 80.09, P < .001). CONCLUSIONS: In the acute setting, DTT is promising for stroke mapping to predict motor outcome. Acute CST damage at the level of the PLIC is a significant predictor of unfavorable motor outcome.

Quantification of thrombus Hounsfield units on noncontrast CT predicts stroke subtype and early recanalization after intravenous recombinant tissue plasminogen activator

Anecdotally we know that high-density clots are probably more organized and difficult to lyse. These investigators calculated HU values for MCA thrombi on noncontrast CT within 4.5 hours of symptom onset and correlated it with successful recanalization after intravenous tPA treatment given 169 +/− 102 minutes thereafter. Best outcomes were achieved for M1, low-density, and thrombi not originating from the heart. Worse outcomes were related to high-density thrombi and those originating from the heart. BACKGROUND AND PURPOSE: Little is known about the factors that determine recanalization after intravenous thrombolysis. We assessed the value of thrombus Hounsfield unit quantification as a predictive marker of stroke subtype and MCA recanalization after intravenous rtPA treatment. MATERIALS AND METHODS: NCCT scans and CTA were performed on patients with MCA acute stroke within 4.5 hours of symptom onset. Demographics, stroke severity, vessel hyperattenuation, occlusion site, thrombus length, and time to thrombolysis were recorded. Stroke origin was categorized as LAA, cardioembolic, or indeterminate according to TOAST criteria. Two blinded neuroradiologists calculated the Hounsfield unit values for the thrombus and contralateral MCA segment. We used ROC curves to determine the rHU cutoff point to discriminate patients with successful recanalization from those without. We assessed the accuracy (sensitivity, specificity, and positive and negative predictive values) of rHU in the prediction of recanalization. RESULTS: Of 87 consecutive patients, 45 received intravenous rtPA and only 15 (33.3%) patients had acute recanalization. rHU values and stroke mechanism were the highest predictive factors of recanalization. The Matthews correlation coefficient was highest for rHU (0.901). The sensitivity, specificity, and positive and negative predictive values for lack of recanalization after intravenous rtPA for rHU ≤ 1.382 were 100%, 86.67%, 93.75%, and 100%, respectively. LAA thrombi had lower rHU than cardioembolic and indeterminate stroke thrombi (P = .004). CONCLUSIONS: The Hounsfield unit thrombus measurement ratio can predict recanalization with intravenous rtPA and may have clinical utility for endovascular treatment decision making.

Automatic Transfer Functions Based on Informational Divergence

In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.

Medical Image Segmentation Based on Mutual Information Maximization

In this paper we propose a two-step mutual information-based algorithm for medical image segmentation. In the first step, the image is structured into homogeneous regions, by maximizing the mutual information gain of the channel going from the histogram bins to the regions of the partitioned image. In the second step, the intensity bins of the histogram are clustered by minimizing the mutual information loss of the reversed channel. Thus, the compression of the channel variables is guided by the preservation of the information on the other. An important application of this algorithm is to preprocess the images for multimodal image registration. In particular, for a low number of histogram bins, an outstanding robustness in the registration process is obtained by using as input the previously segmented images.

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

Semi-automated method for brain hematoma and edema quantification using computed tomography

In this paper, a semi-automated method for brain hematoma and edema segmentation, and volume measurement using computed tomography imaging is presented. This method combines a region growing approach to segment the hematoma and a level set segmentation technique to segment the edema. The main novelty of this method is the strategy applied to define the propagation function required by the level set approach. To evaluate the method, 18 patients with brain hematoma and edema of different size, shape and location were selected. The obtained results demonstrate that the proposed approach provides objective and reproducible segmentations that are similar to the manually obtained results. Moreover, the processing time of the proposed method is about 4 min compared to the 10 min required for manual segmentation.

Normalized similarity measures for medical image registration

Two new similarity measures for rigid image registration, based on the normalization of Jensens difference applied to Renyi and Tsallis-Havrda-Charvat entropies, are introduced. One measure is normalized by the first term of Jensens difference, which in our proposal coincides with the marginal entropy, and the other by the joint entropy. These measures can be seen as an extension of two measures successfully applied in medical image registration: the mutual information and the normalized mutual information. Experiments with various registration modalities show that the new similarity measures are more robust than the normalized mutual information for some modalities and a determined range of the entropy parameter. Also, a certain improvement on accuracy can be obtained for a different range of this parameter.

High-Dimensional normalized mutual information for image registration using random lines

Mutual information has been successfully used as an effective similarity measure for multimodal image registration. However, a drawback of the standard mutual information-based computation is that the joint histogram is only calculated from the correspondence between individual voxels in the two images. In this paper, the normalized mutual information measure is extended to consider the correspondence between voxel blocks in multimodal rigid registration. The ambiguity and high-dimensionality that appears when dealing with the voxel neighborhood is solved using uniformly distributed random lines and reducing the number of bins of the images. Experimental results show a significant improvement with respect to the standard normalized mutual information.

Reliability of the ABC/2 Method in Determining Acute Infarct Volume

Infarct volume is used as a surrogate outcome measure in clinical trials of therapies for acute ischemic stroke. ABC/2 is a fast volumetric method, but its accuracy remains to be determined. We aimed to study the accuracy and reproducibility of ABC/2 in determining acute infarct volume with diffusion‐weighted imaging.