Imagine you have a shiny new car and you want to test its speed and maneuverability. What do you do? You take it out for a spin, of course! In a similar way, scientists wanted to evaluate the effectiveness of low-intensity pulsed ultrasound (LIPUS) in treating traumatic brain injuries. They used a technique called apparent diffusion coefficient (ADC) analysis to measure the changes in the brain’s water diffusion and derive important features from it. This was done using magnetic resonance imaging (MRI) on rats with brain injuries. The results showed that the ADC values and derived features could accurately determine the therapeutic effect of LIPUS. In fact, a combined model of these features showed the highest diagnostic performance in predicting the effectiveness of LIPUS treatment. This research opens up exciting possibilities for using ADC analysis as a tool for evaluating the potential of LIPUS in treating brain injuries in the future.

PurposeIn order to evaluate the neuroprotective effect of low-intensity pulsed ultrasound (LIPUS) for acute traumatic brain injury (TBI), we studied the potential of apparent diffusion coefficient (ADC) values and ADC-derived first-order features regarding this problem.MethodsForty-five male Sprague Dawley rats (sham group: 15, TBI group: 15, LIPUS treated: 15) were enrolled and underwent magnetic resonance imaging. Scanning layers were acquired using a multi-shot readout segmentation of long variable echo trains (RESOLVE) to decrease distortion. The ultrasound transducer was applied to the designated region in the injured cortical areas using a conical collimator and was filled with an ultrasound coupling gel. Regions of interest were manually delineated in the center of the damaged cortex on the diffusion weighted images (b = 800 s/mm2) layer by layer for the TBI and LIPUS treated groups using the open-source software ITK-SNAP. Before analysis and modeling, the features were normalized using a z-score method, and a logistic regression model with a backward filtering method was employed to perform the modeling. The entire process was completed using the R language.ResultsDuring the observation time, the ADC values ipsilateral to the trauma in the TBI and LIPUS groups increased rapidly up to 24 h. After statistical analysis, the 10th percentile, 90th percentile, mean, skewness, and uniformity demonstrated a significant difference among three groups. The receiver operating characteristic curve (ROC) analysis shows that the combined LR model exhibited the highest area under the curve value (AUC: 0.96).ConclusionThe combined LR model of first-order features based on the ADC map can acquire a higher diagnostic performance than each feature only in evaluating the neuroprotective effect of LIPUS for TBI. Models based on first-order features may have potential value in predicting the therapeutic effect of LIPUS in clinical practice in the future.

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