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CT cerebral blood flow maps optimally correlate with admission diffusion-weighted imaging in acute stroke but thresholds vary by postprocessing platform.

ABSTRACT: Admission infarct core lesion size is an important determinant of management and outcome in acute (<9 hours) stroke. Our purposes were to: (1) determine the optimal CT perfusion parameter to define infarct core using various postprocessing platforms; and (2) establish the degree of variability in threshold values between these different platforms.We evaluated 48 consecutive cases with vessel occlusion and admission CT perfusion and diffusion-weighted imaging within 3 hours of each other. CT perfusion was acquired with a "second-generation" 66-second biphasic cine protocol and postprocessed using "standard" (from 2 vendors, "A-std" and "B-std") and "delay-corrected" (from 1 vendor, "A-dc") commercial software. Receiver operating characteristic curve analysis was performed comparing each CT perfusion parameter-both absolute and normalized to the contralateral uninvolved hemisphere-between infarcted and noninfarcted regions as defined by coregistered diffusion-weighted imaging.Cerebral blood flow had the highest accuracy (receiver operating characteristic area under the curve) for all 3 platforms (P<0.01). The maximal areas under the curve for each parameter were: absolute cerebral blood flow 0.88, cerebral blood volume 0.81, and mean transit time 0.82 and relative Cerebral blood flow 0.88, cerebral blood volume 0.83, and mean transit time 0.82. Optimal receiver operating characteristic operating point thresholds varied significantly between different platforms (Friedman test, P<0.01).Admission absolute and normalized "second-generation" cine acquired CT cerebral blood flow lesion volumes correlate more closely with diffusion-weighted imaging-defined infarct core than do those of CT cerebral blood volume or mean transit time. Although limited availability of diffusion-weighted imaging for some patients creates impetus to develop alternative methods of estimating core, the marked variability in quantification among different postprocessing software limits generalizability of parameter map thresholds between platforms.

SUBMITTER: Kamalian S

PROVIDER: S-EPMC3125441 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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CT cerebral blood flow maps optimally correlate with admission diffusion-weighted imaging in acute stroke but thresholds vary by postprocessing platform.

Kamalian Shahmir S Kamalian Shervin S Maas Matthew B MB Goldmacher Greg V GV Payabvash Seyedmehdi S Akbar Adnan A Schaefer Pamela W PW Furie Karen L KL Gonzalez R Gilberto RG Lev Michael H MH

Stroke 20110505 7

<h4>Background and purpose</h4>Admission infarct core lesion size is an important determinant of management and outcome in acute (<9 hours) stroke. Our purposes were to: (1) determine the optimal CT perfusion parameter to define infarct core using various postprocessing platforms; and (2) establish the degree of variability in threshold values between these different platforms.<h4>Methods</h4>We evaluated 48 consecutive cases with vessel occlusion and admission CT perfusion and diffusion-weighte ...[more]

PMID: 21546490

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Project description:IntroductionAcquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar maps from FLAIR MRI for the first time.MethodsWe evaluate a pix2pix model, two modified CycleGANs using paired and unpaired data, and a convolutional autoencoder in synthesizing DWI fractional anisotropy (FA) and mean diffusivity (MD) from whole FLAIR volumes. In total, 420 FLAIR and DWI volumes (11,957 images) from multi-center dementia and vascular disease cohorts were used for training/testing. Generated images were evaluated using two groups of metrics: (1) human perception metrics including peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), (2) structural metrics including a newly proposed histogram similarity (Hist-KL) metric and mean squared error (MSE).ResultsPix2pix demonstrated the best performance both quantitatively and qualitatively with mean PSNR, SSIM, and MSE metrics of 23.41 dB, 0.8, 0.004, respectively for MD generation, and 24.05 dB, 0.78, 0.004, respectively for FA generation. The new histogram similarity metric demonstrated sensitivity to differences in fine details between generated and real images with mean pix2pix MD and FA Hist-KL metrics of 11.73 and 3.74, respectively. Detailed analysis of clinically relevant regions of white matter (WM) and gray matter (GM) in the pix2pix images also showed strong significant (p < 0.001) correlations between real and synthetic FA values in both tissue types (R = 0.714 for GM, R = 0.877 for WM).Discussion/conclusionOur results show that pix2pix's FA and MD models had significantly better structural similarity of tissue structures and fine details than other models, including WM tracts and CSF spaces, between real and generated images. Regional analysis of synthetic volumes showed that synthetic DWI images can not only be used to supplement clinical datasets, but demonstrates potential utility in bypassing or correcting registration in data pre-processing.

Project description:Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

Project description:Background and aims:Assessment of ischemic lesions on computed tomography or MRI diffusion-weighted imaging (DWI) using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used to guide acute stroke treatment. However, it has never been defined how many voxels need to be affected to label a DWI-ASPECTS region ischemic. We aimed to assess the effect of various lesion load thresholds on DWI-ASPECTS and compare this automated analysis with visual rating. Materials and methods:We analyzed overlap of individual DWI lesions of 315 patients from the previously published predictive value of fluid-attenuated inversion recovery study with a probabilistic ASPECTS template derived from 221 CT images. We applied multiple lesion load thresholds per DWI-ASPECTS region (>0, >1, >10, and >20% in each DWI-ASPECTS region) to compute DWI-ASPECTS for each patient and compared the results to visual reading by an experienced stroke neurologist. Results:By visual rating, median ASPECTS was 9, 84 patients had a DWI-ASPECTS score ?7. Mean DWI lesion volume was 22.1 (±35) ml. In contrast, by use of >0, >1-, >10-, and >20%-thresholds, median DWI-ASPECTS was 1, 5, 8, and 10; 97.1% (306), 72.7% (229), 41% (129), and 25.7% (81) had DWI-ASPECTS ?7, respectively. Overall agreement between automated assessment and visual rating was low for every threshold used (>0%: ?w?=?0.020 1%: ?w?=?0.151; 10%: ?w?=?0.386; 20% ?w?=?0.381). Agreement for dichotomized DWI-ASPECTS ranged from fair to substantial (?7: >10% ??=?0.48; >20% ??=?0.45; ?5: >10% ??=?0.528; and >20% ??=?0.695). Conclusion:Overall agreement between automated and the standard used visual scoring is low regardless of the lesion load threshold used. However, dichotomized scoring achieved more comparable results. Varying lesion load thresholds had a critical impact on patient selection by ASPECTS. Of note, the relatively low lesion volume and lack of patients with large artery occlusion in our cohort may limit generalizability of these findings.

Dataset Information

CT cerebral blood flow maps optimally correlate with admission diffusion-weighted imaging in acute stroke but thresholds vary by postprocessing platform.

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CT cerebral blood flow maps optimally correlate with admission diffusion-weighted imaging in acute stroke but thresholds vary by postprocessing platform.

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