Abstract
BackgroundIn conjunction with an epidemiologically determined treatment window, current radiological acute ischemic stroke practice discerns two lesion (stage) types: core (dead tissue, identified by diffusion-weighted imaging (DWI)) and penumbra (tissue region receiving just enough blood flow to be potentially salvageable, identified by the perfusion diffusion mismatch). However, advancements in preclinical and clinical studies have indicated that this approach may be too rigid, warranting a more fine-grained patient-tailored approach. This study aimed to demonstrate the ability to noninvasively provide insights into the current in vivo stroke lesion cascade. MethodsTo elucidate a finer-grained depiction of the acute focal ischemic stroke cascade in vivo, we retrospectively applied our multimodal apparent diffusion (MAD) method to multi-b-value DWI, up to a b-value of 10,000 s/mm2 in 34 patients with acute focal ischemic stroke. Fuzzy C Means was used to cluster the MAD parameters. ResultsWe discerned 18 clusters consistent with normal appearing tissue (NAT) types and 14 potential ischemic lesion (stage) types, providing insights into the variability and aggressiveness of lesion progression and current anomalous stroke-related imaging features. Of the 529 ischemic stroke lesion instances previously identified by two radiologists, 493 (92 %) were autonomously identified; 460 (87 %) were identified as efficaciously or better than the radiologists. ConclusionsThe data analyzed included a small number of clinical patients without follow-up or contemporaneous histology; therefor, the findings and theorizing should be treated as conjecture. Nevertheless, each identified NAT and lesion type is consistent with the known underpinnings of physiological tissues and pathological ischemic stroke lesion (stage) types. Several findings should be considered in current clinical imaging: WM fluid accumulation, BBB compromise conundrum, b1000 identified core may not be dead tissue, and a practical reason for DWI (pseudo) normalization.
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