What is the impact of head movement on automated CT perfusion mismatch evaluation in acute ischemic stroke?

Perfusion parameter map generation from TOF-MRA in stroke using generative adversarial networks

PURPOSE

To generate perfusion parameter maps from Time-of-flight magnetic resonance angiography (TOF-MRA) images using artificial intelligence to provide an alternative to traditional perfusion imaging techniques.

MATERIALS AND METHODS

This retrospective study included a total of 272 patients with cerebrovascular diseases; 200 with acute stroke (from 2010 to 2018), and 72 with steno-occlusive disease (from 2011 to 2014). For each patient the TOF MRA image and the corresponding Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) were retrieved from the datasets. The authors propose an adapted generative adversarial network (GAN) architecture, 3D pix2pix GAN, that generates common perfusion maps (CBF, CBV, MTT, TTP, Tmax) from TOF-MRA images. The performance was evaluated by the structural similarity index measure (SSIM). For a subset of 20 patients from the acute stroke dataset, the Dice coefficient was calculated to measure the overlap between the generated and real hypoperfused lesions with a time-to-maximum (Tmax) > 6 s.

RESULTS

The GAN model exhibited high visual overlap and performance for all perfusion maps in both datasets: acute stroke (mean SSIM 0.88-0.92, mean PSNR 28.48-30.89, mean MAE 0.02-0.04 and mean NRMSE 0.14-0.37) and steno-occlusive disease patients (mean SSIM 0.83-0.98, mean PSNR 23.62-38.21, mean MAE 0.01-0.05 and mean NRMSE 0.03-0.15). For the overlap analysis for lesions with Tmax>6 s, the median Dice coefficient was 0.49.

CONCLUSION

Our AI model can successfully generate perfusion parameter maps from TOF-MRA images, paving the way for a non-invasive alternative for assessing cerebral hemodynamics in cerebrovascular disease patients. This method could impact the stratification of patients with cerebrovascular diseases. Our results warrant more extensive refinement and validation of the method.

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease

Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

RAPID CT Perfusion-Based Relative CBF Identifies Good Collateral Status Better Than Hypoperfusion Intensity Ratio, CBV-Index, and Time-to-Maximum in Anterior Circulation Stroke

BACKGROUND AND PURPOSE

Information of collateral flow may help to determine eligibility for thrombectomy. Our aim was to identify CT perfusion-based surrogate parameters of good collateral status in acute anterior circulation ischemic stroke.

MATERIALS AND METHODS

In this retrospective study, we assessed the collateral status of 214 patients who presented with acute ischemic stroke due to occlusion of the MCA M1 segment or the carotid terminus. Collaterals were assessed on dynamic CTA images analogous to the multiphase CTA score by Menon et al. CT perfusion parameters (time-to-maximum, relative CBF, hypoperfusion intensity ratio, and CBV-index) were assessed with RAPID software. The Spearman rank correlation and receiver operating characteristic analyses were performed to identify the parameters that correlate with collateral scores and good collateral supply (defined as a collateral score of ≥4).

RESULTS

The Spearman rank correlation was highest for a relative CBF < 38% volume (ρ = -0.66, P < .001), followed by the hypoperfusion intensity ratio (ρ = -0.49, P < .001), CBV-index (ρ = 0.51, P < .001), and time-to-maximum > 8 seconds (ρ = -0.54, P < .001). Good collateral status was better identified by a relative CBF < 38% at a lesion size <27 mL (sensitivity of 75%, specificity of 80%) compared with a hypoperfusion intensity ratio of <0.4 (sensitivity of 75%, specificity of 62%), CBV-index of >0.8 (sensitivity of 60%, specificity of 78%), and time-to-maximum > 8 seconds (sensitivity of 68%, specificity of 76%).

CONCLUSIONS

Automated CT perfusion analysis allows accurate identification of collateral status in acute ischemic stroke. A relative CBF < 38% may be a better perfusion-based indicator of good collateral supply compared with time-to-maximum, the hypoperfusion intensity ratio, and the CBV-index.