Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning

Abstract

Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with a target mismatch profile from diffusion-weighted imaging (DWI) and clinical information alone, using perfusion MRI as the reference standard. Materials and Methods Imaging data sets of patients with acute ischemic stroke with baseline perfusion MRI and DWI were retrospectively reviewed from multicenter data available from 2008 to 2019 (Imaging Collaterals in Acute Stroke, Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2, and University of California, Los Angeles stroke registry). For perfusion MRI, rapid processing of perfusion and diffusion software automatically segmented the hypoperfusion lesion (time to maximum, ≥6 seconds) and ischemic core (apparent diffusion coefficient [ADC], ≤620 × 10^-6 mm²/sec). A three-dimensional U-Net deep learning model was trained using baseline DWI, ADC, National Institutes of Health Stroke Scale score, and stroke symptom sidedness as inputs, with the union of hypoperfusion and ischemic core segmentation serving as the ground truth. Model performance was evaluated using the Dice score coefficient (DSC). Target mismatch classification based on the model was compared with that of the clinical-DWI mismatch approach defined by the DAWN trial by using the McNemar test. Results Overall, 413 patients (mean age, 67 years ± 15 [SD]; 207 men) were included for model development and primary analysis using fivefold cross-validation (247, 83, and 83 patients in the training, validation, and test sets, respectively, for each fold). The model predicted the hypoperfusion lesion with a median DSC of 0.61 (IQR, 0.45-0.71). The model identified patients with target mismatch with a sensitivity of 90% (254 of 283; 95% CI: 86, 93) and specificity of 77% (100 of 130; 95% CI: 69, 83) compared with the clinical-DWI mismatch sensitivity of 50% (140 of 281; 95% CI: 44, 56) and specificity of 89% (116 of 130; 95% CI: 83, 94) (P < .001 for all). Conclusion A three-dimensional U-Net deep learning model predicted the hypoperfusion lesion from diffusion-weighted imaging (DWI) and clinical information and identified patients with a target mismatch profile with higher sensitivity than the clinical-DWI mismatch approach. ClinicalTrials.gov registration nos. NCT02225730, NCT01349946, NCT02586415 © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Kallmes and Rabinstein in this issue.

Graphical abstract

Figure 1:

Block diagram shows the attention-gated three-dimensional U-Net model with clinical data fusion and a schematic of the attention gate. Input images include four three-dimensional image slabs sized at 128 × 128 × 16 pixels: diffusion-weighted imaging (b = 1000 sec/mm²), apparent diffusion coefficient (ADC), ADC mask thresholded at 620 × 10⁻⁶ mm²/sec, and a mask indicating the side of stroke. Normalized National Institutes of Health Stroke Scale (NIHSS) scores are broadcast to the shape of the bottleneck layer and linked with the image features. The number of channels is denoted above each box and each block represents a four-dimensional vector. In an attention gate, the output of the previous layer (g) and the symmetric encoding layer (x^l) undergo convolution (with a 1 × 1-pixel kernel), summation, and rectified linear unit (ReLU) activation. Then another convolution with sigmoid activation is applied to extract the attention coefficient (a), which is then multiplied with the skip connection.

Figure 2:

Flowcharts of patient inclusion and exclusion. For each of the five folds in the primary analysis cohort, there were 247 patients in the training set, 83 patients in the validation set, and 83 patients in the test set. There was no overlap in patients between training, validation, or test sets. DEFUSE = Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution, DWI = diffusion-weighted imaging, ICA = internal carotid artery, ICAS = Imaging Collaterals in Acute Stroke, MCA = middle cerebral artery, PWI = perfusion-weighted imaging, UCLA = University of California, Los Angeles.

Figure 3:

(A) Images in a 60-year-old woman with a National Institutes of Health Stroke Scale (NIHSS) score of 2 and right M1 segment occlusion exemplify a large vessel occlusion case. Rapid software identified a hypoperfusion lesion of 146 mL and a core of 32 mL. The model predicted 201 mL for the hypoperfusion lesion (as shown in the bottom row), with accurate spatial location and a Dice score coefficient (DSC) of 0.71. (B) Images in a 40-year-old man with an NIHSS score of 7 and left M2 segment occlusion exemplify a case without large vessel occlusion. Rapid software identified a hypoperfusion lesion of 62 mL and a core of 30 mL. The model predicted 103 mL for the hypoperfusion lesion, with accurate spatial location and a DSC of 0.64. Ax = axial, Cor = coronal, DWI = diffusion-weighted imaging, Sag = sagittal.