The influence of the intracranial pressure field must be discussed with the development of a single-element transducer for low-intensity transcranial focused ultrasound because the skull plays a significant role in blocking and dispersing ultrasound wave propagation. Ultrasound propagation is mainly affected by the structure and acoustic properties of the skull; thus, we aimed to investigate the impact of simplifying the acoustic properties of the skull on the simulation of the transcranial pressure field to present guidance for efficient skull modeling in full-wave simulations. We constructed a three-dimensional computational model for ultrasound transmission with the same structure but varying acoustic properties of the skull. The structural information and heterogeneous acoustic properties of the skull were acquired from computed tomography images, and we segmented the skull into three layers (3 L), including spongy and compact bones. We then assigned homogeneous acoustic properties to a single layer (1 L) or 3 L of the skull. In addition, we investigated the influence of different types of transducers and different ultrasound frequencies (1.1 MHz, 0.5 MHz, and 0.25 MHz) on the intracranial pressure field to provide a comparison of the heterogenous and homogeneous models. We indicated the importance of numerical simulations in estimating the intracranial pressure field of the skull owing to beam distortions. When we simplified the skull model, both the 1 L and 3 L models showed contours of the acoustic focus comparable to those of the heterogeneous model. When we evaluated the peak pressure and volume of the acoustic focus, the 1 L model produced a better estimation of peak pressure with a difference <10%, and the 3 L model is suitable to obtain smaller errors in the volume of the acoustic focus. In conclusion, we examined the possibility of simplification of skull models using 1 L and 3 L homogeneous properties in the numerical simulation for focused ultrasound. The results show that the layered homogeneous model can provide characteristics comparable to those of the acoustic focus in heterogeneous models.

Focused ultrasound is a promising therapeutic technique, as it involves the focusing of an ultrasonic beam with sufficient acoustic energy into a target brain region with high precision. Low-intensity ultrasound transmission by a single-element transducer is mostly established for neuromodulation applications and blood–brain barrier disruption for drug delivery. However, transducer positioning errors can occur without fine control over the sonication, which can affect repeatability and lead to reliability problems. The objective of this study was to determine whether the target brain region would be stable under small displacement (0.5 mm) of the transducer based on numerical simulations. Computed-tomography-derived three-dimensional models of a rat head were constructed to investigate the effects of transducer displacement in the caudate putamen (CP) and thalamus (TH). Using three different frequencies (1.1, 0.69, and 0.25 MHz), the transducer was displaced by 0.5 mm in each of the following six directions: superior, interior, anterior, posterior, left, and right. The maximum value of the intracranial pressure field was calculated, and the targeting errors were determined by the full-width-at-half-maximum (FWHM) overlap between the free water space (FWHM_water) and transcranial transmission (FWHM_base). When the transducer was positioned directly above the target region, a clear distinction between the target regions was observed, resulting in 88.3%, 81.5%, and 84.5% FWHM_water for the CP and 65.6%, 76.3%, and 64.4% FWHM_water for the TH at 1.1, 0.69, and 0.25 MHz, respectively. Small transducer displacements induced both enhancement and reduction of the peak pressure and targeting errors, compared with when the transducer was displaced in water. Small transducer displacement to the left resulted in the lowest stability, with 34.8% and 55.0% targeting accuracy (FWHM_water) at 1.1 and 0.69 MHz in the TH, respectively. In addition, the maximum pressure was reduced by up to 11% by the transducer displacement. This work provides the targeting errors induced by transducer displacements through a preclinical study and recommends that attention be paid to determining the initial sonication foci in the transverse plane in the cases of small animals.

Methods to improve drug delivery efficiency through blood-brain barrier disruption (BBBD) based on microbubbles and focused ultrasound (FUS) are continuously being studied. However, most studies are being conducted in preclinical trial environments using small animals. The use of the human skull shows differences between the clinical and preclinical trials. BBBD results from preclinical trials are difficult to represent in clinical trials because various distortions of ultrasound by the human skull are excluded in the former. Therefore, in our study, a clinical validation platform based on a preclinical trial environment, using a human skull fragment and a rat model, was developed to induce BBBD under conditions similar to clinical trials. For this, a human skull fragment was inserted between the rat head and a 250 kHz FUS transducer, and optimal ultrasound parameters for the free field (without human skull fragment) and human skull (with human skull fragment) were derived by 300 mV_pp and 700 mV_pp, respectively. BBBD was analyzed according to each case using magnetic resonance images, Evans blue dye, cavitation, and histology. Although it was confirmed using magnetic resonance images and Evans blue dye that a BBB opening was induced in each case, multiple BBB openings were observed in the brain tissues. This phenomenon was analyzed by numerical simulation, and it was confirmed to be due to standing waves owing to the small skull size of the rat model. The stable cavitation doses (SCD_h and SCD_u) in the human skull decreased by 13.6- and 5.3-fold, respectively, compared to those in the free field. Additionally, the inertial cavitation dose in the human skull decreased by 1.05-fold compared to that of the free field. For the histological analysis, although some extravasated red blood cells were observed in each case, it was evaluated as recoverable based on our previous study results. Therefore, our proposed platform can help deduct optimal ultrasound parameters and BBBD results for clinical trials in the preclinical trials with small animals because it considers variables relevant to the human skull.

Deep learning-based analysis of brain magnetic resonance imaging (MRI) plays a crucial role in the early diagnosis of Alzheimer’s disease (AD). However, data scarcity and racial bias present significant challenges to the generalization of diagnostic models. Large-scale public datasets, which are predominantly composed of Caucasian individuals, often lead to performance degradation when applied to other ethnic groups owing to domain shifts. To address these issues, this study proposes a two-stage transfer learning framework. Initially, a 3D ResNet model was pretrained on a large-scale Alzheimer’s disease neuroimaging initiative (ADNI) dataset to learn structural brain features. Subsequently, the pretrained weights were transferred and fine-tuned on a small-scale Korean dataset utilizing only 30% of the data for training. The proposed model achieved superior performance in classifying mild cognitive impairment (MCI), which is crucial for early diagnosis, compared with a model trained from scratch using 70% of the Korean data. Furthermore, it effectively mitigated the significant performance degradation observed when directly applying the pretrained model, demonstrating its ability to resolve the domain-shift issue. This study explored the feasibility of transfer learning to address data scarcity and domain shift issues in AD classification, underscoring its potential for developing AI-based diagnostic systems tailored to specific ethnic populations.