Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.

Diffusion-Weighted Imaging Image Combined with Transcranial Doppler Ultrasound in the Diagnosis of Patients with Cerebral Infarction and Vertigo

This study aimed at exploring the application value of diffusion-weighted imaging (DWI) combined with transcranial Doppler (TCD) in the diagnosis of patients with cerebral infarction and vertigo (CI + V). In this article, using a retrospective case-control study, 100 CI + V patients (CI + V group) were examined by DWI combined with TCD. Seventy cases of noncerebral infarction with vertigo (control group) who were hospitalized at the same time were collected for clinical data analysis and comprehensive evaluation of each index. The results showed that in patients with CI + V, the abnormal rate of blood vessels was proportional to the size of the lesion, and the abnormal rate of blood vessels in the large-area infarction group (97%) was much higher than that of the small-area infarct group (62%) and the lacunar infarction group (51%). The overall abnormal rate of blood vessels in the CI + V group (71%) was greatly higher than that in the control group (15%), showing a statistically and extremely great difference (P < 0.01). In short, DWI can effectively extract lesion-related data, and combined with TCD examination, the clinical diagnosis of CI + V can be more accurately performed, which had a positive impact on the clinical work of CI + V. This work provided some reference for the clinical effective diagnosis method of CI + V.